Liquid cooling control method and system for battery control device

Abstract

The present invention relates to the field of temperature control technology, and specifically to a liquid cooling control method and system for a battery control device. In the present invention, through real-time collection and regional screening of temperature and power data, accurate identification of high-temperature areas is achieved, clear cooling demand zoning is provided, and a dynamic adjustment strategy of liquid cooling pump pressure and flow parameters is optimized by genetic algorithm. While effectively balancing energy consumption and cooling efficiency, the cooling response speed and control accuracy are improved, and the pump speed and valve opening are adjusted in real time. Through automatic control, the temperature change is quickly responded to to ensure the gradual realization of cooling goals and reduce the lag in the thermal management process. The temperature control effect is continuously evaluated by fuzzy control algorithm, and the cooling intensity is dynamically adjusted according to the monitoring results to ensure efficient cooling while achieving balanced 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.

Description

Technical Field

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

[0002] The field of temperature control technology involves monitoring, managing, and regulating the temperature of equipment, systems, and components to ensure they operate in optimal conditions. The goal is to prevent overheating and overcooling from adversely affecting equipment performance, lifespan, and safety through precise thermal management.

[0003] The purpose of the liquid cooling control method of the battery control device is to quickly dissipate the heat generated by the battery during operation through effective thermal management means, so as to avoid performance degradation or safety risks caused by overheating, extend the battery life, and at the same time improve the operating efficiency and reliability of the overall system, achieve a balanced 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 identify specific areas with high precision and dynamically partition them in temperature control, and are unable to perform differentiated cooling for different temperature areas, which can easily lead to waste of resources or insufficient local cooling. They are also unable to adapt to rapid changes in heat distribution in real time, which may lead to overheating or overcooling, adversely affecting equipment safety and operating efficiency. Existing technologies lack the ability to make long-term dynamic adjustments and are unable to optimize based on historical data. The uneven temperature distribution can easily lead to local thermal runaway or performance degradation, resulting in higher energy consumption costs, increased 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 shortcomings of the prior art and to propose a liquid cooling control method and system for a battery control device.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a liquid cooling control method for a battery control device, comprising the following steps:

[0007] Step 1: The sensor network collects temperature and power output data from each battery cell. High-temperature areas and normal operating areas are screened based on temperature thresholds. By comparing real-time data with safety standards, cooling demand zones are determined and a temperature control zoning strategy is generated.

[0008] Step 2: Based on the temperature control zoning strategy, calculate the deviation between the high-temperature area and the overall average temperature, set a precise temperature reduction target, adjust the cooling parameters through real-time monitoring, and generate cooling target setting details;

[0009] 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, to generate flow and pressure optimization parameters;

[0010] Step 4: According to the flow and 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;

[0011] 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 achieved, the cooling intensity is increased again, the cooling strategy is optimized, and a cooling strategy optimization plan is generated;

[0012] Step 6: Based on the cooling strategy optimization plan, 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.

[0013] As a further solution of the present invention, the specific steps of generating the temperature control zoning strategy are:

[0014] The sensor network collects the temperature and power output data of each battery cell. During the classification process, a preset temperature threshold is used to screen high-temperature areas and normal working areas. The classification results are recorded item by item, and the regional temperature data is collected in sequence to generate the temperature zone division results.

[0015] Based on the temperature zone division results, the deviations between the high temperature zone and the safety standard are compared point by point, the cooling demand range is delineated by accumulating statistical deviations, the cooling demand level of each zone is calibrated item by item, and the cooling demand zoning results are generated;

[0016] 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 temperature control parameter list 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.

[0017] As a further solution of the present invention, the specific steps of generating the cooling target setting details are:

[0018] Based on the temperature control zoning strategy, the difference between the high temperature area and the overall average temperature is gradually measured, and the regional target temperature is calculated in combination with the preset target temperature adjustment range. The adjustment target data is recorded in sequence to generate the temperature adjustment target parameters;

[0019] 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;

[0020] Based on the cooling target setting details, the adjusted regional temperature change data is analyzed point by point and compared with the target value range, and the cooling parameters of the regions with large deviations are corrected. The correction of all regions is gradually completed to generate the cooling target setting details.

[0021] As a further solution of the present invention, the specific steps of generating the flow pressure optimization parameters are:

[0022] Based on the cooling target setting details, a genetic algorithm is used to measure the initial states 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;

[0023] Based on the initial parameter setting results, 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;

[0024] Based on the dynamic adjustment optimization data, the distribution of the liquid cooling pump flow and pressure 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.

[0025] As a further solution of the present invention, the genetic algorithm is according to the formula:

[0026] 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.

[0027] As a further solution of the present invention, the specific steps of generating the adjustment effect monitoring result are:

[0028] Based on the flow and 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;

[0029] Based on the pump speed and valve adjustment data, the real-time temperature change value of each zone is measured, the valve opening is gradually corrected to match the temperature change requirements, and the temperature change of each zone is recorded to generate regional temperature monitoring data;

[0030] Based on the regional temperature monitoring data, the measured values ​​are compared with the target values, and the adjustment parameters of the pump speed and valve are gradually corrected. The verification is completed through real-time data recording to generate the adjustment effect monitoring results.

[0031] As a further solution of the present invention, the specific steps of generating the cooling strategy optimization solution are:

[0032] 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 a temperature assessment result;

[0033] Based on the temperature evaluation results, a fuzzy control algorithm is used to gradually increase the cooling intensity, adjust the output pressure of the flow control device and the cooling flow, re-measure the cooling effect of each area, record the changes in cooling parameters item by item, and generate a cooling parameter adjustment record;

[0034] Based on the cooling parameter adjustment records, the cooling strategies are optimized one by one. 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.

[0035] As a further solution of the present invention, the fuzzy control algorithm is according to the formula:

[0036] in: For the 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.

[0037] As a further solution of the present invention, the specific steps of generating the continuous operation control framework are:

[0038] 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 parameter values ​​are recorded to generate parameter update results.

[0039] Based on the parameter update results, 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 between the areas is gradually analyzed to generate long-term monitoring data.

[0040] Based on the long-term monitoring data, the cooling parameters are gradually corrected. 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.

[0041] A liquid cooling control system for a battery control device, the liquid cooling control system for the battery control device being used to execute the liquid cooling control method for the battery control device described above, the system comprising:

[0042] Temperature control zoning module: This module collects temperature and power output data from each battery cell through a sensor network, uses temperature thresholds to screen high-temperature areas and normal operating areas, compares the temperature of the high-temperature areas with safety standards, and uses the comparison results to partition cooling requirements and generate a temperature control zoning strategy.

[0043] Cooling target setting module: Based on the temperature control zoning strategy, it calculates the deviation between the temperature of the high-temperature area and the overall average temperature, compares the temperature deviation of the high-temperature area with the preset temperature reduction target, sets the temperature reduction target for each area, and generates cooling target setting details by adjusting the dynamic distribution of cooling flow and pressure through real-time monitoring;

[0044] Flow and pressure optimization module: Based on the cooling target setting details, a genetic algorithm is used to generate an initial cooling strategy. 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, and the adjusted energy consumption and cooling effect are recorded. The optimized pressure and flow distribution relationship is mapped to the regional cooling strategy table to generate flow and pressure optimization parameters.

[0045] Cooling adjustment module: Based on the flow and pressure optimization parameters, the module adjusts the pump speed and valve opening through automated control, compares the adjusted pump speed value with the regional cooling demand, monitors the changes in the adjusted flow rate and regional temperature in real time, records the cooling effect data of each region, and generates adjustment effect monitoring results;

[0046] Cooling strategy evaluation module: Based on the adjustment effect monitoring results, a fuzzy control algorithm is used to evaluate the temperature control effect of each battery cell, the cooling demand value of the non-compliant area is classified with the adjusted cooling resource allocation value, 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;

[0047] Continuous control module: Based on the cooling strategy optimization plan, 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 long-term liquid cooling control model, and generate a continuous operation control framework.

[0048] Compared with the prior art, the advantages and positive effects of the present invention are:

[0049] 1. This invention achieves precise identification of high-temperature areas through real-time collection and regional screening of temperature and power data, providing clear cooling demand zoning. The deviation between the high-temperature area and the overall average temperature is calculated to clearly define the temperature reduction target. Dynamically adjusted cooling parameters are used to optimize cooling resource utilization and enhance control over heat distribution.

[0050] 2. This invention uses a genetic algorithm to optimize the dynamic regulation strategy for the liquid cooling pump's pressure and flow parameters. This effectively balances energy consumption and cooling efficiency while improving cooling response speed and control accuracy. Real-time adjustment of pump speed and valve opening allows for rapid response to temperature changes through automated control, ensuring the gradual achievement of cooling targets and reducing lag in the thermal management process.

[0051] 3. This invention uses a fuzzy control algorithm to continuously evaluate temperature control effectiveness and dynamically adjusts cooling intensity based on the monitoring results, ensuring efficient cooling while achieving a balanced temperature distribution. Through long-term data monitoring and continuous optimization of cooling operations, a systematic thermal management framework is established, achieving efficient and stable temperature management. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0053] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0054] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0055] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0056] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0057] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0058] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0059] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, 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 intended to limit the present invention.

[0061] See also Figure 1 The present invention provides a technical solution: a liquid cooling control method for a battery control device, comprising the following steps:

[0062] S1: The sensor network collects temperature and power output data from each battery cell, filters out high-temperature areas and normal operating areas based on temperature thresholds, and determines cooling demand zones by comparing real-time data with safety standards, generating a temperature control zoning strategy.

[0063] S2: Based on the temperature control zoning strategy, calculate the deviation between the high-temperature area and the overall average temperature, set a precise temperature reduction target, adjust the cooling parameters through real-time monitoring, and generate cooling target setting details;

[0064] S3: Based on the cooling target setting details, a genetic algorithm is used and a dynamic adjustment control mechanism is used to adjust the pressure and flow of the liquid cooling pump while balancing energy consumption and efficiency to generate flow and pressure optimization parameters;

[0065] S4: Based on the flow and pressure optimization parameters, the pump speed and valve opening are adjusted through automated control, the temperature changes after adjustment are monitored in real time, and the adjustment effect monitoring results are generated;

[0066] S5: 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 achieved, the cooling intensity is increased again, the cooling strategy is optimized, and a cooling strategy optimization plan is generated;

[0067] S6: Based on the cooling strategy optimization plan, update the liquid cooling control parameters, optimize the cooling operation through long-term data monitoring, and make continuous adjustments to generate a continuous operation control framework.

[0068] See also Figure 2 ,The specific steps to generate the temperature control partition strategy are:

[0069] S101: The temperature and power output data of each battery cell are collected through a sensor network. During the classification process, a preset temperature threshold is used to screen high-temperature areas and normal operating areas. The classification results are recorded item by item, and the regional temperature data is collected in sequence to generate a temperature zone division result.

[0070] S102: Based on the temperature zone division results, the deviations of the high temperature areas and the safety standards are compared point by point. By accumulating and statistically analyzing the deviations, the cooling demand range is defined. The cooling demand level of each area is calibrated item by item to generate the cooling demand zoning results.

[0071] S103: Based on the cooling demand zoning results, the cooling demand levels of each zone are matched, temperature control strategy parameters are allocated in sequence, a temperature control parameter list for each zone is established in sequence according to the demand, the parameter list after allocation is recorded, and a temperature control zoning strategy is generated;

[0072] A sensor network is used to collect temperature and power output data from each battery cell in real time. The collected data is gradually recorded using a node-distributed collection algorithm, and the data format is parsed using a temperature data parsing method. This includes setting the temperature sampling rate to 1 second and outputting the data in a CSV file format. The recorded collected data is then organized and classified one by one according to the preset temperature threshold. After the high-temperature area and normal working area are classified using conditional filtering commands, the classification results are generated item by item. The regional temperature data is then aggregated in sequence using a regional marking algorithm, and the organized data table is output to generate the temperature zone division results.

[0073] Based on the temperature zone division results, regional data is used to compare the deviations between high-temperature areas and safety standards. A linear deviation calculation method is used to accumulate and count the deviation values ​​point by point. The deviation amount is determined through step-by-step cumulative statistical operations. The cooling demand range is gradually delineated by region based on the statistical deviation amount, and the cooling demand level of each region is calibrated using a regional priority allocation algorithm. The level division ranges from level 1 emergency cooling demand to level 3 low cooling demand. After the cooling demand levels of all regions are calibrated in sequence, the cooling demand zoning results are generated.

[0074] Based on the cooling demand zoning results, a dynamic parameter allocation algorithm is used to match the strategy parameters of the cooling demand level of each area. The temperature control strategy parameters are allocated item by item through a table lookup method. The parameters include the coolant flow range setting value of 2 to 10 liters per minute, the cooling pressure range setting value of 0.5 to 2 MPa, and the cooling time cycle range setting value of 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 strategy is generated.

[0075] See also Figure 3 ,The specific steps to generate cooling target setting details are:

[0076] S201: Based on the temperature control zoning strategy, gradually measure the difference between the high-temperature area and the overall average temperature, calculate the regional target temperature based on the preset target temperature adjustment range, record the adjustment target data in sequence, and generate the temperature adjustment target parameter;

[0077] S202: 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;

[0078] S203: 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 of the regions with large deviations are corrected. The corrections are gradually completed for all regions to generate the cooling target setting details;

[0079] Based on the temperature control zoning strategy, a point-by-point data comparison algorithm is used to gradually measure the difference between the high-temperature area and the overall average temperature. The specific steps include extracting real-time temperature data from the high-temperature area, comparing each data point with the overall average temperature, and using the difference calculation formula to gradually record the difference between each high-temperature area and the average temperature. The dynamic target allocation algorithm is used to calculate the regional target temperature in combination with the preset target temperature adjustment range. The specific operation of the dynamic target allocation algorithm includes setting the adjustment range to 2 to 5 degrees Celsius, allocating the adjustment range to the target temperature of each area one by one, recording the adjustment target data of all areas in sequence, and generating the temperature adjustment target parameters;

[0080] Based on the temperature adjustment target parameters, a real-time monitoring algorithm is used to monitor the temperature change process item by item. Specifically, the monitoring interval is set to 5 seconds, and the monitoring range is all data points in each high-temperature area. The data collected every second is gradually analyzed and the temperature change trend is updated. The monitoring data is combined with the parameter optimization algorithm to modify the cooling parameters. The parameter optimization algorithm includes dynamically adjusting the coolant flow range to 2 to 10 liters per minute, adjusting the coolant temperature setting range to 10 to 20 degrees Celsius, and adjusting the pressure range to 0.5 to 2 MPa. The cooling parameters are adjusted one by one in real time, and the parameters after each adjustment are recorded to generate the cooling target setting details.

[0081] Based on the cooling target setting details, the regional data comparison algorithm is used to analyze the temperature change data of the adjusted regions point by point, specifically including extracting the temperature change data of each region and comparing it point by point with the target value range, and using the deviation correction algorithm to correct the cooling parameters. The operation of the deviation correction algorithm includes gradually locating the areas where the deviation exceeds 2 degrees Celsius, and gradually adjusting the ratio of the flow parameter to the pressure parameter in the order of cooling priority. The flow parameter adjustment range is to increase or decrease by no more than 1 liter per minute, and the pressure adjustment range is to increase or decrease by no more than 0.2 MPa. The correction operation of all regions is gradually completed, the corrected parameter data is recorded, and the cooling target setting details are generated.

[0082] See also Figure 4 , the specific steps to generate flow pressure optimization parameters are:

[0083] S301: 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. The parameter data after the setting is recorded to generate an initial parameter setting result;

[0084] S302: Based on the initial parameter setting results, gradually adjust the pressure and flow values ​​of the pump, calibrate the ratio between the two through synchronous measurement and data recording, and record the energy consumption during the adjustment process to generate dynamic adjustment optimization data;

[0085] S303: Based on the dynamic adjustment optimization data, the distribution of the liquid cooling pump flow and pressure in different areas is recalibrated. 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.

[0086] Based on the cooling target setting details, a genetic algorithm is used to measure the initial states of the liquid cooling pump pressure and flow rate. The random population generation method is used to initialize the pressure and flow values ​​of the liquid cooling pump. The size of the random population is set to 50, the pressure value range is set to 0.5 to 2 MPa, and the flow value range is set to 2 to 10 liters per minute. The ratio of pressure and flow rate is gradually adjusted, and the fitness function is used to evaluate the fitness of the current parameter combination. The parameters of the fitness function include the pressure stability coefficient, the flow uniformity coefficient, and the energy consumption recording coefficient. The 10% individuals with the lowest fitness are eliminated, and a new population is generated using single-point crossover and mutation operations. The pressure and flow values ​​generated after crossover and mutation are recorded to generate the initial parameter setting results.

[0087] Based on the initial parameter setting results, a synchronous adjustment algorithm is used to gradually adjust the pressure and flow values ​​of the pump. The pressure adjustment amplitude is set to 0.1 MPa each time, and the flow adjustment amplitude is set to 0.5 liters per minute. The real-time ratio of the pressure and flow values ​​is measured through synchronous sampling. The sampling frequency is set to 10 seconds each time. The data recording module is used to gradually record each set of pressure and flow data during the adjustment process. The real-time energy consumption monitoring module is combined to record the current energy consumption data item by item. The sampling data and energy consumption records are integrated to generate dynamic adjustment optimization data.

[0088] Based on the dynamic adjustment optimization data, the recalibration algorithm is used to recalibrate the distribution of the liquid cooling pump flow and pressure in different areas one by one. The flow and pressure data of each area are measured point by point through the multi-area test method. Each measurement is set to three repeated samplings, and each group of sampling data is gradually averaged to reduce accidental errors. According to the calibration results, the data with a deviation exceeding 5% is gradually corrected. The adjustment correction range includes the flow value variation range of 0.2 to 1 liters per minute, and the pressure value variation range of 0.1 to 0.3 MPa. After completing the calibration of all areas, the final configuration values ​​of the flow and pressure of each area are recorded to generate the flow and pressure optimization parameters.

[0089] Genetic algorithm, according to the formula:

[0090] 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;

[0091] Execution process: First measure the pressure value of the liquid cooling pump and flow value , monitor the operating status of the liquid cooling pump in real time through the sensor, input the collected pressure and flow data as the basic parameters of the formula, and then set the weight parameter of the pressure to flow ratio , the initial value is set to 0.5, and gradually adjusted The value of the pressure and flow is dynamically balanced, and the preliminary liquid cooling setting value is obtained. Then, combined with the thermal load state of the cooling target battery pack, the heat generated by the battery is measured and the thermal load coefficient is calculated. , using thermal sensors to collect temperature data in real time, and combining it with the heat exchange model to obtain The specific value of the heat load is set at the same time. , the initial value is 0.3, adjust the specific value according to the experimental optimization, and then calculate the energy efficiency ratio of the liquid cooling pump , record the energy consumption of the liquid cooling pump per unit time through the energy consumption monitoring module, evaluate its efficiency index based on the pressure and flow of the liquid cooling pump, and set the energy efficiency weight parameter The initial value can be 0.2, which is optimized and adjusted according to the operating performance. Finally, it is substituted into the formula for comprehensive calculation to generate the initial parameter setting results of the liquid cooling control system. The calculation results are stored in the recording device to provide a reference basis for the subsequent liquid cooling control process.

[0092] See also Figure 5 , the specific steps to generate the adjustment effect monitoring results are:

[0093] S401: Based on the flow and pressure optimization parameters, the initial value of the pump speed is gradually adjusted, and the valve opening is manually adjusted step by step to match it. Each state value after the pump speed adjustment is recorded to generate pump speed and valve adjustment data;

[0094] S402: Based on the pump speed and valve adjustment data, the real-time temperature change value of each zone is measured, the valve opening is gradually corrected to match the temperature change requirements, and the temperature change of each zone is recorded to generate regional temperature monitoring data;

[0095] S403: Based on the regional temperature monitoring data, the measured values ​​are compared with the target values, and the adjustment parameters of the pump speed and valve are gradually corrected. The adjustment parameters are verified through real-time data recording and the adjustment effect monitoring results are generated;

[0096] Based on the flow and pressure optimization parameters, a step-by-step adjustment algorithm is used to gradually adjust the initial pump speed value. The pump speed adjustment range is manually set to increase or decrease by 100 revolutions per minute each time. The initial pump speed setting range is 500 to 1500 revolutions per minute. The adjusted pump speed value is recorded through synchronous sampling. The valve opening matching algorithm is used to gradually adjust the valve opening within a range of 5% each time. All status data corresponding to the valve opening and pump speed are recorded to generate pump speed and valve adjustment data.

[0097] Based on the pump speed and valve adjustment data, a real-time temperature measurement method is used to gradually measure the real-time temperature changes in each area. A high-frequency sampling module is used to set the sampling frequency to once per second. Temperature data is collected point by point and stored in the database. A dynamic adjustment algorithm is used to gradually correct the matching degree between the valve opening and the temperature change. The valve opening correction amplitude is set to increase or decrease by 2% each time. The valve opening of all areas is adjusted in turn, and the temperature change after each adjustment is recorded to generate regional temperature monitoring data.

[0098] Based on regional temperature monitoring data, an error comparison algorithm is used to compare the measured value with the target value. The error between the measured value and the target value is calculated in real time, and the allowable error range is set to ±0.5 degrees Celsius. For areas beyond the error range, the pump speed and valve adjustment parameters are gradually corrected. The pump speed correction range is 50 revolutions per minute for each adjustment, and the valve adjustment correction range is 1% for each adjustment. The real-time data recording module is used to synchronously record all correction operations and result data to generate adjustment effect monitoring results.

[0099] See also Figure 6 ,The specific steps to generate the cooling strategy optimization plan are:

[0100] S501: 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 recorded for analysis. The non-compliant areas are gradually classified, and the classified temperature deviation data is recorded to generate a temperature assessment result;

[0101] S502: Based on the temperature evaluation results, a fuzzy control algorithm is used to gradually increase the cooling intensity. By adjusting the output pressure of the flow control device and the cooling flow rate, the cooling effect of each area is remeasured, and the changes in cooling parameters are recorded item by item to generate a cooling parameter adjustment record.

[0102] S503: Based on the cooling parameter adjustment records, the cooling strategies are optimized one by one. 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.

[0103] Based on the adjustment effect monitoring results, a point-by-point analysis algorithm is used to measure the temperature of each area one by one. The acquisition frequency is set to once per second through the high-frequency temperature acquisition module. The real-time temperature data of each area is recorded. The difference calculation method is used to gradually calculate the deviation between the temperature data and the preset target. The difference calculation range is set to ±2 degrees Celsius. The areas that do not reach the deviation range are gradually classified. The classification standard is set as high deviation area and medium deviation area. The classified temperature deviation data are marked and recorded respectively to generate the temperature assessment results.

[0104] Based on the temperature assessment results, a fuzzy control algorithm is used to gradually increase the cooling intensity. The output pressure adjustment range of the flow control device is set to 0.5 to 2 MPa through a fuzzy rule table. The cooling flow is adjusted in real time according to regional needs. The flow rate change range is set to increase or decrease by 0.5 liters per minute each time. The temperature feedback mechanism is used to measure the cooling effect of each area in real time. The changes in cooling parameters are recorded item by item. The records include real-time flow values, pressure values ​​and temperature values ​​of the corresponding areas, and a cooling parameter adjustment record is generated.

[0105] Based on the cooling parameter adjustment records, a step-by-step optimization algorithm is used to optimize the cooling strategy one by one. The cooling flow and regional allocation scheme are gradually adjusted through the regional allocation mechanism. The allocation scheme parameters include the flow allocation ratio and regional cooling priority. The priority is set to three levels: high, medium, and low. The flow allocation of each level is gradually optimized. The optimization content includes adjusting the allocation ratio to between 10%, 20%, 30% and 50%. The regional cooling strategy configuration table is established item by item, and the data after each optimization is recorded to generate a cooling strategy optimization plan.

[0106] Fuzzy control algorithm, according to the formula:

[0107] in: For the 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 velocity weight coefficient;

[0108] Implementation process: Use temperature sensors to measure the current temperature of the cooling area in real time and with the target temperature Compare and calculate the temperature difference , set the temperature difference adjustment coefficient according to experimental data and historical operation records , quantify the effect of temperature difference on cooling intensity, the initial value can be obtained through experimental fitting and optimized in the subsequent process, and then the current cooling flow rate is measured using a flow sensor , and set the flow adjustment coefficient in combination with the cooling operation status , dynamically adjust its value according to the actual operating conditions through optimization methods, and then calculate the heat distribution unevenness based on the temperature data of multiple points in the area , use standard deviation or maximum temperature difference to quantify the change of heat distribution, set the heat distribution weight coefficient , reflecting the impact of heat distribution on cooling effect, and then measuring the flow rate of the cooling medium through the flow rate sensor , set the flow rate weight coefficient based on the dynamic characteristics of the cooling fluid Finally, all parameters are substituted into the formula to calculate the comprehensive cooling intensity , dynamically adjust the flow, flow rate and pressure parameters of the cooling system based on the calculation results to ensure that the cooling effect reaches the optimal state required for battery operation, and generate cooling parameter adjustment records for system optimization and long-term monitoring.

[0109] See also Figure 7 ,The specific steps to generate a continuous operation control framework are:

[0110] S601: Based on the cooling strategy optimization plan, gradually update the flow and pressure control parameters of each cooling zone. By importing parameters in batches and testing the cooling performance of each zone one by one, record the updated parameter values ​​and generate parameter update results.

[0111] S602: Based on the parameter update results, continuously collect temperature data from each zone. By regularly collecting and recording operating parameters during the cooling process, gradually analyze the changing relationship between temperature and energy consumption data between zones to generate long-term monitoring data.

[0112] S603: Based on long-term monitoring data, gradually revise cooling parameters. By optimizing cooling distribution and flow adjustment in each zone, verify the stability of cooling operations item by item and record the final data to generate a continuous operation control framework.

[0113] Based on the cooling strategy optimization plan, a batch import algorithm was used to gradually update the flow and pressure control parameters of each cooling zone. The updated parameters were divided into 10 groups through the parameter group import method and loaded in batches. Each group of parameters included a flow range set from 2 to 10 liters per minute and a pressure range set from 0.5 to 2 MPa. The cooling performance of each zone was verified one by one using a single zone test method. The test content included measuring the immediate response of the updated parameters to temperature changes and the pressure stability. The updated values ​​of all parameters were gradually recorded to generate the parameter update results.

[0114] Based on the parameter update results, a timed acquisition algorithm is used to continuously collect temperature data from each area. The acquisition interval is set to once every 5 minutes. The temperature sensor network is used to record the flow, pressure, and temperature operating parameters of the cooling process in real time. A point-by-point comparison algorithm is used to gradually analyze the changing trends of temperature data between areas. This is then gradually correlated with 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.

[0115] Based on long-term monitoring data, a step-by-step correction algorithm is used to correct the cooling parameters one by one. The flow adjustment module is used to optimize the cooling distribution in each area. The adjustment range includes the flow 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, including flow 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.

[0116] See also Figure 8 A liquid cooling control system for a battery control device is provided. The liquid cooling control system for the battery control device is used to execute the liquid cooling control method for the battery control device. The system comprises:

[0117] Temperature control zoning module: This module collects temperature and power output data from each battery cell through a sensor network, uses temperature thresholds to screen high-temperature areas and normal operating areas, compares the temperature of the high-temperature areas with safety standards, and uses the comparison results to partition cooling requirements and generate a temperature control zoning strategy.

[0118] Cooling target setting module: Based on the temperature control zoning strategy, it calculates the temperature deviation between the high-temperature area and the overall average temperature, compares the temperature deviation of the high-temperature area with the preset temperature reduction target, sets the temperature reduction target for each area, and generates cooling target setting details by adjusting the dynamic distribution of cooling flow and pressure through real-time monitoring;

[0119] Flow and pressure optimization module: Based on the cooling target setting details, a genetic algorithm is used to generate an initial cooling strategy. The ratio of the liquid cooling pump's pressure and flow value is compared with the cooling demand optimization target. The pressure and flow values ​​of the liquid cooling pump are iteratively adjusted, and the adjusted energy consumption and cooling effect are recorded. The optimized pressure and flow distribution relationship is mapped to the regional cooling strategy table to generate flow and pressure optimization parameters.

[0120] Cooling Adjustment Module: Based on flow and pressure optimization parameters, the module automatically adjusts the pump speed and valve opening, compares the adjusted pump speed value with the regional cooling demand, and records the cooling effect data of each region through real-time monitoring of the changes in the adjusted flow rate and regional temperature to generate adjustment effect monitoring results.

[0121] Cooling strategy evaluation module: Based on the adjustment effect monitoring results, it uses a fuzzy control algorithm to evaluate the temperature control effect of each battery cell, classifies the cooling demand values ​​of the non-compliant areas and the adjusted cooling resource allocation values, rearranges the resource allocation priority of the non-compliant areas, adjusts the resource allocation plan, and generates a cooling strategy optimization plan;

[0122] Continuous control module: Based on the cooling strategy optimization plan, it analyzes historical operating data and current control parameters, compares the cooling allocation pattern in the historical data with the current cooling parameters, classifies the long-term optimization data of regional resource allocation, establishes a long-term liquid cooling control model, and generates a continuous operation control framework.

[0123] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection 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: The sensor network collects temperature and power output data from each battery cell. High-temperature areas and normal operating areas are screened based on temperature thresholds. By comparing real-time data with safety standards, cooling demand zones are determined and a temperature control zoning strategy is generated. Step 2: Based on the temperature control zoning strategy, calculate the deviation between the high-temperature area and the overall average temperature, set a precise temperature reduction 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, to generate flow and pressure optimization parameters; Step 4: According to the flow and 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 achieved, the cooling intensity is increased again, the cooling strategy is optimized, and a cooling strategy optimization plan is generated; Step 6: Based on the cooling strategy optimization plan, update the control parameters of the liquid cooling, optimize the cooling operation through long-term data monitoring, and continuously adjust and generate a continuous operation control framework; 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 states 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 results, 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 liquid cooling pump flow and pressure 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; The genetic algorithm is based on 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.

2. The liquid cooling control method for a battery control device according to claim 1, wherein: The specific steps for generating the temperature control zoning strategy are as follows: The sensor network collects the temperature and power output data of each battery cell. During the classification process, a preset temperature threshold is used to screen high-temperature areas and normal working areas. The classification results are recorded item by item, and the regional temperature data is collected in sequence to generate the temperature zone division results. Based on the temperature zone division results, the deviations between the high temperature zone and the safety standard are compared point by point, the cooling demand range is delineated by accumulating statistical deviations, the cooling demand level of each zone is calibrated item by item, and the cooling demand zoning results are 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 temperature control parameter list 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 for a battery control device according to claim 1, wherein: 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 gradually measured, and the regional target temperature is calculated in combination with the preset target temperature adjustment range. The adjustment target data is recorded in sequence to generate the temperature adjustment target parameters; 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 and compared with the target value range, and the cooling parameters of the regions with large deviations are corrected. The correction of all regions is gradually completed to generate the cooling target setting details.

4. The liquid cooling control method for a battery control device according to claim 1, wherein: The specific steps for generating the adjustment effect monitoring result are: Based on the flow and 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 temperature change value of each zone is measured, the valve opening is gradually corrected to match the temperature change requirements, and the temperature change of each zone is recorded to generate regional temperature monitoring data; Based on the regional temperature monitoring data, the measured values ​​are compared with the target values, and the adjustment parameters of the pump speed and valve are gradually corrected. The verification is completed through real-time data recording to generate the adjustment effect monitoring results.

5. The liquid cooling control method for a battery control device according to claim 1, wherein: The specific steps for 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 a temperature assessment result; Based on the temperature evaluation results, a fuzzy control algorithm is used to gradually increase the cooling intensity, adjust the output pressure of the flow control device and the cooling flow, re-measure the cooling effect of each area, record the changes in cooling parameters item by item, and generate a cooling parameter adjustment record; Based on the cooling parameter adjustment records, the cooling strategies are optimized one by one. 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.

6. The liquid cooling control method for a battery control device according to claim 1, wherein: 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 parameter values ​​are recorded to generate parameter update results. Based on the parameter update results, 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 between the areas is gradually analyzed to generate long-term monitoring data. Based on the long-term monitoring data, the cooling parameters are gradually corrected. 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.

7. A liquid cooling control system for a battery control device, characterized in that: The liquid cooling control method for a battery control device according to any one of claims 1 to 6, wherein the system comprises: Temperature control zoning module: This module collects temperature and power output data from each battery cell through a sensor network, uses temperature thresholds to screen high-temperature areas and normal operating areas, compares the temperature of the high-temperature areas with safety standards, and uses the comparison results to partition cooling requirements and generate a temperature control zoning strategy. Cooling target setting module: Based on the temperature control zoning strategy, it calculates the deviation between the temperature of the high-temperature area and the overall average temperature, compares the temperature deviation of the high-temperature area with the preset temperature reduction target, sets the temperature reduction target for each area, and generates 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. 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, and the adjusted energy consumption and cooling effect are recorded. 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 and pressure optimization parameters, the module adjusts the pump speed and valve opening through automated control, compares the adjusted pump speed value with the regional cooling demand, monitors the changes in the adjusted flow rate and regional temperature in real time, records the cooling effect data of each region, and generates adjustment effect monitoring results; Cooling strategy evaluation module: Based on the adjustment effect monitoring results, a fuzzy control algorithm is used to evaluate the temperature control effect of each battery cell, the cooling demand value of the non-compliant area is classified with the adjusted cooling resource allocation value, 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 plan, 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 long-term liquid cooling control model, and generate a continuous operation control framework.

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