An intelligent heat dissipation control system for a lithium battery pack

Abstract

The present invention relates to the field of heat dissipation control technology, and specifically to an intelligent heat dissipation control system for a lithium battery pack. In the present invention, battery pack and ambient temperature data are acquired in real time, and the heat dissipation mode is automatically adjusted according to temperature changes, so as to effectively maintain the battery pack temperature within a preset range and avoid the impact of excessively high and low temperatures on battery performance. The temperature difference between each area is analyzed through a long-short-term memory network, and the heat dissipation demand is dynamically adjusted according to the temperature data, thereby improving the accuracy and response speed of heat dissipation, reducing the energy consumption of ineffective heat dissipation, and improving the accuracy of temperature control. In the process of heat dissipation efficiency adjustment, a greedy algorithm is used, combined with real-time temperature monitoring data, and by judging the difference between the cooling effect and the heat dissipation target, effective cooling equipment optimization scheduling is performed, thereby improving the efficiency of the heat dissipation system, realizing refined temperature control, and improving the temperature control accuracy and stability of the battery pack and the overall performance of the system.

Description

Technical Field

[0001] The present invention relates to the field of heat dissipation control technology, and in particular to an intelligent heat dissipation control system for a lithium battery pack. Background Art

[0002] The purpose of the field of heat dissipation control technology is to effectively manage the temperature of equipment or systems to ensure that they can remain within the optimal temperature range during operation, thereby improving performance, extending service life, and ensuring safety, avoiding performance degradation, equipment aging, and safety hazards caused by overheating. By designing efficient heat dissipation solutions, the temperature can be effectively lowered, thermal stress can be reduced, equipment loss can be delayed, and safe operation of the equipment can be guaranteed.

[0003] The lithium battery pack intelligent heat dissipation control system is designed to ensure that the battery maintains a safe and efficient operating temperature range during the charge and discharge process by precisely controlling the temperature of the battery pack. The main purpose is to improve the battery's operating efficiency, extend its service life and ensure safety, and dynamically adjust the heat dissipation method to prevent the battery temperature from overheating.

[0004] Existing technologies rely on simple temperature monitoring and a single heat dissipation method. When the temperature changes of the battery pack are more complex, they cannot effectively respond to the heat dissipation needs of different areas. When the temperature differences between different areas within the battery pack are large, and the existing system cannot timely evaluate and adjust the heat dissipation strategy of each area, it is easy to cause excessive or insufficient heat dissipation in some areas, resulting in inefficiency or equipment damage. The temperature control strategy is based on a relatively fixed heat dissipation solution and does not have the ability to adjust it in real time, resulting in delayed heat dissipation control, affecting battery performance and safety. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent heat dissipation control system for lithium battery packs.

[0006] In order to achieve the above objectives, the present invention adopts the following technical solutions: a lithium battery pack intelligent heat dissipation control system, the system comprising:

[0007] Temperature monitoring and assessment module: This module uses sensors to obtain the battery pack and ambient temperature, and determines whether they exceed the preset range. If so, it collects temperature change information, selects a heat dissipation method based on the change characteristics, controls the cooling device on and off, and generates an environmental status assessment result.

[0008] Temperature distribution module: Based on the environmental status assessment results, obtain the temperature data of each area, use the long short-term memory network to analyze the temperature distribution, compare the temperature difference, select the adaptation area for adjustment, dynamically calculate the regional heat dissipation requirements, and generate a temperature distribution plan;

[0009] Cooling equipment scheduling module: Based on the temperature distribution plan, analyzes temperature changes and equipment load, matches the scheduling plan with load and demand, adjusts the air cooling pump speed and water cooling pump mode, and generates a cooling scheduling plan;

[0010] Temperature control scheduling decision module: Based on the heat dissipation scheduling plan, combined with the working status and temperature changes of the battery pack, adjusts the operating status and speed of the air cooling and water cooling equipment to generate a temperature control adjustment plan;

[0011] Cooling system operation module: adjusts the speed and temperature of the air cooling pump and the water cooling pump according to the temperature control adjustment scheme, determines whether the cooling demand is met, performs dynamic adjustment, and generates temperature control status information;

[0012] Heat dissipation efficiency adjustment module: Based on the temperature control status information, a greedy algorithm is used to compress the temperature data, obtain the temperature difference and evaluate the cooling effect, compare the target efficiency with the actual effect, perform dynamic optimization scheduling, and generate a cooling performance optimization plan;

[0013] Temperature control parameter optimization module: Based on the cooling performance optimization plan, it receives temperature fluctuation data in real time, analyzes the operating status in combination with environmental changes, adjusts the mode and parameters, and generates an optimization adjustment plan.

[0014] As a further solution of the present invention, the temperature monitoring and evaluation module includes:

[0015] Temperature acquisition submodule: This module acquires battery pack temperature and ambient temperature data through sensors, records temperature change information in real time, stores the data, and periodically calibrates the sensors to verify the validity of the temperature data, generating a temperature data set.

[0016] Temperature over-limit judgment submodule: Based on the temperature data set, it compares the battery pack temperature with the ambient temperature in real time to determine whether it exceeds the preset range. By comparing the temperature thresholds of the battery and the ambient temperature, it performs temperature over-limit identification and generates an over-limit temperature assessment result;

[0017] The heat dissipation method selection submodule obtains the ambient and battery pack temperature change data based on the over-limit temperature assessment results, analyzes the temperature difference between the ambient and battery, and selects an appropriate heat dissipation method based on the over-limit judgment. By adjusting the heat dissipation plan, the switch status of the air cooling and water cooling equipment is controlled to generate the environmental status assessment results.

[0018] As a further solution of the present invention, the temperature distribution module includes:

[0019] Temperature data acquisition submodule: Based on the environmental status assessment results, real-time temperature data is acquired from sensors in each area. The temperature data is analyzed and processed using a long short-term memory network. The temperature changes in each area of the battery pack are recorded, and the integrity and validity of the sensor data are verified in real time to generate a regional temperature dataset.

[0020] Regional temperature difference comparison submodule: Based on the regional temperature data set, compare the temperature values of different regions, calculate the temperature difference between the regions, find the areas with large temperature differences, select the adapted areas for temperature adjustment, and generate the temperature difference evaluation results;

[0021] Heat dissipation requirement calculation submodule: Based on the temperature difference assessment results, the heat dissipation required for each area is calculated, and the temperature distribution plan is generated by combining the regional temperature difference and the heat dissipation requirements of the battery area.

[0022] As a further solution of the present invention, the specific formula for analyzing and processing temperature data by the long short-term memory network is as follows:

[0023] ,

[0024] in: is the temperature forecast value at the current moment, is the input data at the current moment, is the weight matrix between input data and output, is the weight matrix between the output at the previous moment and the output at the current moment, is the bias term, is the Sigmoid activation function, is the coefficient of temperature change rate, is the time interval parameter, is the sensor calibration coefficient, is the sensor performance parameter, Indicates the hidden state of the last moment, reflecting the battery pack at time The comprehensive representation of the temperature distribution, operating status of the heat dissipation equipment and control instruction information at the current moment is used to support the temperature control decision calculation at the current moment.

[0025] As a further solution of the present invention, the heat dissipation device scheduling module includes:

[0026] Temperature change analysis submodule: Based on the temperature distribution scheme, it obtains temperature data from sensors in each area of the battery pack, records regional temperature fluctuations in real time, calculates the temperature differences between areas, analyzes the temperature rise and fall trends of each area, and generates regional temperature change trends;

[0027] Load status judgment submodule: Based on the temperature change trend of the area, check the current load status of the air cooling and water cooling equipment, analyze whether they can meet the temperature control requirements of each area, evaluate the working status of the air cooling and water cooling equipment, calculate the load situation, and generate the load status evaluation result;

[0028] Equipment scheduling generation submodule: Based on the load state assessment result, a scheduling scheme adapted to the heat dissipation requirement is selected, the speed of the air cooling pump and the working mode of the water cooling pump are adjusted, and a heat dissipation scheduling scheme is generated.

[0029] As a further solution of the present invention, the temperature control scheduling decision module includes:

[0030] Working status acquisition submodule: Based on the heat dissipation scheduling plan, it obtains the current working mode from the battery pack, reads the real-time temperature data of the battery pack and each area, monitors the working status of the battery pack in real time and records the relevant temperature fluctuations, the consistency of temperature data and working mode, and generates battery working status data;

[0031] Temperature judgment submodule: Based on battery operating status data, it compares the battery pack's operating mode with the temperature changes in each area, determines whether the regional temperature has reached the preset adjustment threshold, analyzes the temperature change trend of each area, verifies the area that needs adjustment, and generates a temperature change assessment result;

[0032] Equipment speed regulation operation submodule: Based on the temperature change assessment results, air cooling and water cooling equipment are selected for adjustment. By controlling the speed of the air cooling pump and the working mode of the water cooling pump, the operating status of the equipment is adjusted, the working intensity of the temperature control equipment in each area is adjusted, and a temperature control adjustment plan is generated.

[0033] As a further solution of the present invention, the cooling system operation module includes:

[0034] Equipment adjustment submodule: Based on the temperature control scheme, read the operating status and adjust the speed of the air cooling pump and the temperature setting of the water cooling pump, adjust the working parameters, and generate equipment adjustment results;

[0035] Cooling demand check submodule: Based on the equipment adjustment results, monitor and analyze the temperature changes in the battery area, check the current cooling demand in real time, evaluate whether the existing equipment meets the temperature control requirements of each area, and verify whether the cooling equipment is sufficient by dynamically adjusting the battery area temperature to generate a cooling demand assessment result;

[0036] Temperature control submodule: Based on the cooling demand assessment results, according to the temperature changes in the battery area, dynamically adjust the air cooling pump speed and the water cooling pump working mode, adjust the equipment working mode, monitor the relationship between the equipment output and the temperature changes in the battery area in real time, and generate temperature control status information.

[0037] As a further solution of the present invention, the heat dissipation efficiency adjustment module includes:

[0038] Temperature difference acquisition submodule: Based on the temperature control status information, reads the real-time temperature of the battery area and calculates the difference from the target temperature, and records and stores the temperature difference data to generate a temperature difference data set;

[0039] Cooling effect judgment submodule: Based on the temperature difference data set, compare and analyze the target heat dissipation efficiency and the actual cooling effect, monitor the operating status of the cooling equipment, and generate a cooling effect evaluation result;

[0040] Heat dissipation scheduling optimization submodule: Based on the cooling effect evaluation results, a greedy algorithm is used to compress the temperature monitoring data, while analyzing the operating parameters and cooling requirements, optimizing the scheduling and working mode of the cooling equipment, adjusting the heat dissipation parameters, and generating a cooling performance optimization plan.

[0041] As a further solution of the present invention, the specific formula for compressing temperature monitoring data using the greedy algorithm is as follows:

[0042] ,

[0043] in: is the total heat dissipation efficiency, For the Temperature data for each region, is the ambient temperature, Calculated from the thermal conductivity of the regional material and the heat sink efficiency, For the The fan coefficient of each area, For the The heat transfer coefficient of the region, is the factor affecting the cooling efficiency of the fan speed, is the flow regulation coefficient of the liquid cooling system, is the number of cooling zones.

[0044] As a further solution of the present invention, the temperature control parameter optimization module includes:

[0045] Data acquisition submodule: Based on the cooling performance optimization solution, it obtains temperature data from the battery pack and the environment. It uses sensors to read the temperature changes of various areas in the battery pack in real time, records the ambient temperature fluctuations, and generates a temperature change data set.

[0046] Fluctuation analysis submodule: Based on the temperature change data set, analyze the temperature fluctuation of the battery pack, check the amplitude and frequency of the temperature change, calculate the stability index of the battery pack, identify areas with large fluctuations, and generate temperature fluctuation analysis results;

[0047] Parameter adjustment submodule: Based on the temperature fluctuation analysis results and the actual temperature control requirements of the battery pack, the operating mode and parameters of the heat dissipation equipment are adjusted in real time, and a temperature control parameter optimization plan is generated by adjusting the air cooling pump speed and the water cooling pump working mode.

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

[0049] 1. The present invention obtains real-time battery pack and ambient temperature data and automatically adjusts the heat dissipation method according to temperature changes, effectively maintaining the battery pack temperature within a preset range and preventing excessively high or low temperatures from affecting battery performance.

[0050] 2. This invention uses a long-short-term memory network to analyze the temperature differences between different areas and dynamically adjusts the heat dissipation requirements based on temperature data, thereby improving the accuracy and response speed of heat dissipation and reducing energy consumption caused by ineffective heat dissipation. Furthermore, the use of air-cooling and water-cooling equipment is optimized according to the temperature requirements of different areas, and the speed and working mode of the pump are intelligently adjusted. The heat dissipation equipment operates according to the actual load, reducing resource waste and improving the accuracy of temperature control.

[0051] 3. In the present invention, a greedy algorithm is used in the process of adjusting the heat dissipation efficiency, combined with real-time temperature monitoring data. By judging the difference between the cooling effect and the heat dissipation target, effective cooling equipment optimization scheduling is performed, thereby improving the efficiency of the heat dissipation system, achieving refined temperature control, and improving the temperature control accuracy and stability of the battery pack and the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is the overall flow chart of the intelligent heat dissipation control system for lithium battery packs of the present invention;

[0053] Figure 2 This is a flow chart of the intelligent heat dissipation control system for lithium battery packs of the present invention;

[0054] Figure 3 This is a schematic diagram of the overall framework of the intelligent heat dissipation control system for lithium battery packs of the present invention. DETAILED DESCRIPTION

[0055] 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.

[0056] Example 1

[0057] See also Figure 1 and Figure 2 The present invention provides a technical solution: a lithium battery pack intelligent heat dissipation control system comprising:

[0058] Temperature monitoring and assessment module: This module uses sensors to obtain the battery pack and ambient temperature, and determines whether they exceed the preset range. If so, it collects temperature change information, selects a heat dissipation method based on the change characteristics, controls the cooling device on and off, and generates an environmental status assessment result.

[0059] Temperature distribution module: Based on the environmental status assessment results, it obtains temperature data for each area, uses a long-short-term memory network to analyze the temperature distribution, compares the temperature difference, selects the appropriate area for adjustment, dynamically calculates the regional heat dissipation requirements, and generates a temperature distribution plan;

[0060] Cooling equipment scheduling module: Based on the temperature distribution plan, it analyzes temperature changes and equipment load, matches the scheduling plan with load and demand, adjusts the air cooling pump speed and water cooling pump mode, and generates a cooling scheduling plan;

[0061] Temperature control scheduling decision module: Based on the heat dissipation scheduling plan, combined with the battery pack's operating status and temperature changes, it adjusts the operating status and speed of air cooling and water cooling equipment to generate a temperature control adjustment plan;

[0062] Cooling system operation module: adjusts the speed and temperature of the air cooling pump and water cooling pump according to the temperature control scheme, determines whether the cooling demand is met, performs dynamic adjustments, and generates temperature control status information;

[0063] Heat dissipation efficiency adjustment module: Based on temperature control status information, a greedy algorithm is used to compress temperature data, obtain temperature differences, evaluate cooling effects, compare target efficiency with actual effects, perform dynamic optimization scheduling, and generate cooling performance optimization plans;

[0064] Temperature control parameter optimization module: Based on the cooling performance optimization plan, it receives temperature fluctuation data in real time, analyzes the operating status in combination with environmental changes, adjusts the mode and parameters, and generates an optimization adjustment plan.

[0065] See also Figure 3 , the temperature monitoring and evaluation module includes:

[0066] Temperature acquisition submodule: This module acquires battery pack temperature and ambient temperature data through sensors, records temperature change information in real time, stores the data, and periodically calibrates the sensors to verify the validity of the temperature data, generating a temperature data set.

[0067] Temperature over-limit judgment submodule: Based on the temperature data set, it compares the battery pack temperature with the ambient temperature in real time to determine whether it exceeds the preset range. By comparing the battery and ambient temperature thresholds, it executes the temperature over-limit flag and generates the over-limit temperature assessment result.

[0068] The heat dissipation method selection submodule obtains ambient and battery pack temperature change data based on the over-limit temperature assessment results, analyzes the temperature difference between the ambient and battery temperatures, and selects an appropriate heat dissipation method based on over-limit judgments. By adjusting the heat dissipation solution and controlling the on / off status of air cooling and water cooling equipment, an environmental status assessment result is generated.

[0069] Temperature acquisition submodule: This module uses sensors to acquire battery pack and ambient temperature data, records temperature changes in real time, stores the data, and uses a calibration algorithm to periodically verify the validity of the sensor. During the calibration process, a mean square error (MSE) algorithm is used to compare the temperature data recorded by the sensor with the standard temperature. If the data deviation is within the preset ±0.5°C, a recalibration operation is performed to generate a temperature data set.

[0070] The temperature over-limit judgment submodule compares the battery pack temperature with the ambient temperature in real time based on the temperature data set. A threshold judgment algorithm is used to compare the battery pack temperature with the ambient temperature thresholds, including a preset safety temperature threshold of 65°C for the battery pack and a maximum ambient temperature threshold of 50°C. By setting conditional statements, if the battery pack temperature or the ambient temperature exceeds the preset range, the temperature over-limit identification operation is executed, marking the temperature data as over-limit and generating an over-limit temperature assessment result.

[0071] Heat dissipation mode selection submodule: Based on the over-limit temperature assessment results, the ambient and battery pack temperature change data are obtained. The temperature difference between the battery pack and the environment is analyzed through the temperature difference analysis algorithm. If the battery pack temperature exceeds the ambient temperature by more than 5°C, the air cooling system adjustment method is adopted. Otherwise, the water cooling system adjustment method is adopted. The switching status of the air cooling and water cooling devices is controlled by the state machine algorithm. The air cooling device adjusts the air cooling pump speed through the PWM control signal, and the water cooling device adjusts the water cooling pump flow through the PID control algorithm to generate the environmental status assessment results.

[0072] See also Figure 3 , the temperature distribution module includes:

[0073] Temperature data acquisition submodule: Based on the environmental status assessment results, real-time temperature data is obtained from sensors in each area. The temperature data is analyzed and processed using a long short-term memory network. The temperature changes in each area of the battery pack are recorded, and the sensor data is verified in real time to ensure its integrity and validity, generating a regional temperature dataset.

[0074] Regional temperature difference comparison submodule: Based on the regional temperature dataset, it compares the temperature values of different regions, calculates the temperature difference between the regions, finds the areas with the largest temperature difference, selects the appropriate areas for temperature adjustment, and generates the temperature difference evaluation results;

[0075] Heat dissipation demand calculation submodule: Based on the temperature difference assessment results, it calculates the heat dissipation required for each area and generates a temperature distribution plan based on the regional temperature difference and the heat dissipation requirements of the battery area.

[0076] Temperature data acquisition submodule: Based on the environmental status assessment results, real-time temperature data is obtained from sensors in each area. The long short-term memory network algorithm is used. The parameters include the temperature data of the input layer, the number of recurrent neural network units in the hidden layer is 256, and the output layer is the current temperature value. The temperature data is analyzed and processed, and the temperature changes in each area of the battery pack are recorded. The sensor data is verified in real time using a verification algorithm. The mean square error algorithm is used during the verification process to compare the temperature data recorded by the sensor with the preset standard value to determine the validity of the temperature data. If the error exceeds ±0.5°C, a recalibration operation is performed to generate a regional temperature data set.

[0077] Regional temperature difference comparison submodule: Based on the regional temperature dataset, the temperature values of different regions are compared. A difference calculation algorithm is used to compare the temperatures of each region within the battery pack, and the temperature difference between each region is calculated. The absolute value function is used to calculate the temperature difference between regions. If the temperature difference is greater than 5°C, it is marked as a region with a large temperature difference. The adapted region is selected for temperature adjustment and the temperature difference evaluation result is generated.

[0078] Heat dissipation demand calculation submodule: Based on the temperature difference assessment results, the required heat dissipation for each area is calculated. Combining the regional temperature difference with the heat dissipation requirements of the battery area, through heat conduction analysis, the required heat dissipation for each area is calculated based on the temperature difference and heat dissipation requirements of different areas in the battery pack, and a temperature distribution plan is generated.

[0079] Long short-term memory network, according to the formula:

[0080] ,

[0081] in: is the temperature forecast value at the current moment, is the input data at the current moment, is the weight matrix between input data and output, is the weight matrix between the output at the previous moment and the output at the current moment, is the bias term, is the Sigmoid activation function, is the coefficient of temperature change rate, is the time interval parameter, is the sensor calibration coefficient, is the sensor performance parameter, Indicates the hidden state of the last moment, reflecting the battery pack at time A comprehensive representation of the temperature distribution, operating status of the heat dissipation equipment and control instruction information at the current moment is used to support the temperature control decision calculation;

[0082] Execution process: First, get the current time from the temperature sensor The real-time temperature data reflects the actual temperature of each area in the battery pack, and then the weight matrix and Output of input data and temperature at the last moment By performing weighted summation, the temporal dependency of temperature data is effectively captured, and the bias term Adjust the calculation results, the output range of the model is reasonable, and then consider the rate of change of the internal temperature of the battery, the parameter Multiply by the time interval Correct the effect of time on temperature change to avoid data errors caused by too long or too short collection intervals, and introduce sensor performance parameters , multiplied by the calibration factor , compensate for the errors and instabilities of the sensor, improve the accuracy of temperature prediction, and finally all the weighted summation results are activated by the function Converted into the temperature forecast value at the current moment , used to optimize the heat dissipation control strategy so that the battery operates within the optimal temperature range.

[0083] See also Figure 3 , the temperature control scheduling decision module includes:

[0084] Temperature change analysis submodule: Based on the temperature distribution scheme, it obtains temperature data from sensors in each area of the battery pack, records regional temperature fluctuations in real time, calculates the temperature differences between areas, analyzes the temperature rise and fall trends of each area, and generates regional temperature change trends;

[0085] Load status judgment submodule: Based on the regional temperature change trend, it checks the current load status of air-cooling and water-cooling equipment, analyzes whether they can meet the temperature control requirements of each area, evaluates the working status of air-cooling and water-cooling equipment, calculates the load situation, and generates load status assessment results;

[0086] Equipment scheduling generation submodule: Based on the load status assessment results, select the appropriate scheduling plan according to the heat dissipation requirements, adjust the speed of the air cooling pump and the working mode of the water cooling pump, and generate a heat dissipation scheduling plan;

[0087] The temperature change analysis submodule acquires temperature data from sensors in each area of the battery pack based on the temperature distribution scheme, records regional temperature fluctuations in real time, and calculates the temperature differences between areas. A sliding average algorithm with a window size of 5 is used to smooth the temperature data and reduce noise interference. The temperature rise and fall trends of each area are calculated. A linear regression analysis algorithm is used to analyze the temperature rise and fall trends of each area by setting a model for the relationship between temperature change time and temperature change, generating regional temperature change trends.

[0088] Load status judgment submodule: Based on the regional temperature change trend, the current load status of air-cooling and water-cooling equipment is checked and a load calculation algorithm is used. The air-cooling system outputs the current equipment load by inputting the current speed of the air-cooling pump and the rated power of the air-cooling equipment. The water-cooling system calculates the load status by inputting the current flow rate of the water-cooling pump and the power consumption data of the pump. Combined with the regional temperature difference and heat dissipation requirements, the working status of the air-cooling and water-cooling equipment is evaluated, the load status is calculated, and the load status assessment result is generated.

[0089] Equipment scheduling generation submodule: Based on the load status assessment results, a suitable scheduling scheme is selected according to the heat dissipation requirements. A scheduling optimization algorithm is adopted, in which the speed of the air-cooling pump and the working mode of the water-cooling pump are optimized through a genetic algorithm. The crossover probability is set to 0.8 and the mutation probability is set to 0.1. A suitable scheduling scheme is selected to adjust the speed of the air-cooling pump and switch the working mode of the water-cooling pump. A heat dissipation scheduling scheme is generated based on the temperature change trend and equipment load.

[0090] See also Figure 3 , the temperature control strategy generation module includes:

[0091] Working status acquisition submodule: Based on the heat dissipation scheduling plan, it obtains the current working mode from the battery pack, reads the real-time temperature data of the battery pack and each area, monitors the working status of the battery pack in real time and records the relevant temperature fluctuations, the consistency of temperature data and working mode, and generates battery working status data;

[0092] Temperature judgment submodule: Based on battery operating status data, it compares the battery pack's operating mode with the temperature changes in each area, determines whether the regional temperature has reached the preset adjustment threshold, analyzes the temperature change trend of each area, verifies the area that needs adjustment, and generates a temperature change assessment result;

[0093] Equipment speed control submodule: Based on the temperature change assessment results, select air cooling and water cooling equipment for adjustment. By controlling the speed of the air cooling pump and the working mode of the water cooling pump, the operating status of the equipment is adjusted, the working intensity of the temperature control equipment in each area is adjusted, and a temperature control adjustment plan is generated.

[0094] Working status acquisition submodule: Based on the heat dissipation scheduling plan, the module obtains the current working mode from the battery pack, reads the real-time temperature data of the battery pack and each area, and uses the sensor data acquisition algorithm to set the acquisition frequency to 1 second to monitor the working status of the battery pack in real time and record related temperature fluctuations. The module uses a data consistency verification algorithm to compare the working mode of the battery pack with the acquired temperature data, and generates battery working status data to ensure consistency between the temperature data and the working mode.

[0095] The temperature judgment submodule compares the battery pack's operating mode with the temperature changes in each area based on battery operating status data. It uses a temperature threshold judgment algorithm to set the battery pack temperature upper limit to 65°C and the lower limit to 10°C. Based on the battery pack's operating mode and regional temperature changes, it determines whether the regional temperature has reached the preset adjustment threshold. A trend analysis algorithm is used to identify areas where temperature changes exceed the set threshold based on the temperature change trend of the battery area, and a temperature change assessment result is generated.

[0096] Equipment speed regulation operation submodule: Based on the temperature change evaluation results, air-cooling and water-cooling equipment are selected for appropriate adjustment. The PID control algorithm is used, and the proportional coefficient is set to 1.5, the integral coefficient is set to 0.5, and the differential coefficient is set to 0.1. The speed of the air-cooling pump and the working mode of the water-cooling pump are adjusted. During the adjustment process, the speed change of the air-cooling pump is controlled by PWM modulation technology, and the working mode of the water-cooling pump is adjusted by switch control to adjust the flow rate. The working intensity of the temperature control equipment in each area is adjusted to generate a temperature control adjustment plan.

[0097] See also Figure 3 , the cooling system operation module includes:

[0098] Equipment adjustment submodule: Based on the temperature control scheme, read the operating status and adjust the speed of the air cooling pump and the temperature setting of the water cooling pump, adjust the operating parameters, and generate equipment adjustment results;

[0099] Cooling demand check submodule: Based on the equipment adjustment results, it monitors and analyzes the temperature changes in the battery area, checks the current cooling demand in real time, and evaluates whether the existing equipment meets the temperature control requirements of each area. By dynamically adjusting the battery area temperature, it verifies whether the cooling equipment is sufficient and generates a cooling demand assessment result.

[0100] Temperature control submodule: Based on the cooling demand assessment results and temperature changes in the battery area, it dynamically adjusts the air cooling pump speed and water cooling pump operating mode, adjusts the device operating mode, monitors the relationship between device output and battery area temperature changes in real time, and generates temperature control status information.

[0101] The equipment adjustment submodule reads the operating status and adjusts the air-cooling pump speed and the water-cooling pump temperature setting based on the temperature control scheme. A PID control algorithm is used with a proportional coefficient set to 1.2, an integral coefficient set to 0.8, and a differential coefficient set to 0.3. The air-cooling pump speed is adjusted via a PWM control signal, varying between 1000 RPM and 3000 RPM. The water-cooling pump temperature setting is adjusted via a digital temperature control algorithm, with the temperature range set between 15°C and 30°C. By adjusting the operating parameters, the equipment can operate stably within the preset temperature range, generating equipment adjustment results.

[0102] Cooling demand check submodule: Based on the equipment adjustment results, it monitors and analyzes temperature changes in the battery area, checks the current cooling demand in real time, and uses a cooling load calculation algorithm to evaluate cooling demand based on the real-time temperature of the battery area and the preset temperature threshold. The upper limit of the battery area temperature is set to 60°C and the lower limit is set to 20°C. The module evaluates whether the existing equipment meets the temperature control requirements of each area. The module also evaluates the equipment load based on the regional temperature difference and heat dissipation requirements to generate a cooling demand assessment result.

[0103] Temperature control submodule: Based on the cooling demand assessment results, the air cooling pump speed and the water cooling pump operating mode are dynamically adjusted according to the temperature changes in the battery area. An adaptive adjustment algorithm is used to set the air cooling pump speed adjustment range to 1200RPM to 2500RPM. According to the real-time monitored temperature changes, the water cooling pump operating mode is adjusted and the water cooling pump flow adjustment range is set to 5L / min to 25L / min. A real-time feedback control algorithm is used to monitor the relationship between the device output and the temperature changes in the battery area in real time to generate temperature control status information.

[0104] See also Figure 3 , the heat dissipation efficiency adjustment module includes:

[0105] Temperature difference acquisition submodule: Based on the temperature control status information, it reads the real-time temperature of the battery area and calculates the difference from the target temperature. It also records and stores the temperature difference data to generate a temperature difference data set.

[0106] Cooling effect judgment submodule: Based on the temperature difference data set, it compares and analyzes the target heat dissipation efficiency and the actual cooling effect, monitors the operating status of the cooling equipment, and generates cooling effect evaluation results;

[0107] Heat dissipation scheduling optimization submodule: Based on the cooling effect evaluation results, a greedy algorithm is used to compress the temperature monitoring data, while analyzing the operating parameters and cooling requirements, optimizing the scheduling and working mode of the cooling equipment, adjusting the heat dissipation parameters, and generating a cooling performance optimization plan.

[0108] The specific formula for compressing temperature monitoring data using the greedy algorithm is as follows:

[0109] ,

[0110] in: is the total heat dissipation efficiency, For the Temperature data for each region, is the ambient temperature, Calculated from the thermal conductivity of the regional material and the heat sink efficiency, For the The fan coefficient of each area, For the The heat transfer coefficient of the region, is the factor affecting the cooling efficiency of the fan speed, is the flow regulation coefficient of the liquid cooling system, is the number of cooling zones;

[0111] Execution process: First, obtain the temperature of each area through the temperature sensor Real-time temperature data, taking into account the impact of the external environment, using the ambient temperature To calculate the temperature difference between each area, determine the cooling demand of each area, and then calculate the heat dissipation resistance of each area , reflects the difficulty of heat transfer, usually related to the thermal conductivity of the area and the efficiency of the heat dissipation device, fan speed Will affect the air flow velocity, coefficient It is used to adjust the contribution of fan speed to heat dissipation efficiency. Then the heat transfer capacity of the liquid cooling system will also affect the heat dissipation efficiency. The liquid flow rate and heat transfer coefficient and Together they determine the ability of heat to be conducted through the coolant, and the formula calculates the heat dissipation efficiency of each area , generate cooling performance optimization plan.

[0112] See also Figure 3 , the temperature control parameter optimization module includes:

[0113] Data acquisition submodule: Based on the cooling performance optimization solution, it obtains temperature data from the battery pack and the environment. It uses sensors to read the temperature changes in various areas within the battery pack in real time, records ambient temperature fluctuations, and generates a temperature change data set.

[0114] Fluctuation analysis submodule: Based on the temperature change data set, it analyzes the temperature fluctuation of the battery pack, checks the amplitude and frequency of temperature changes, calculates the stability index of the battery pack, identifies areas with large fluctuations, and generates temperature fluctuation analysis results;

[0115] Parameter adjustment submodule: Based on the temperature fluctuation analysis results and the actual temperature control requirements of the battery pack, the operating mode and parameters of the cooling equipment are adjusted in real time. By adjusting the air cooling pump speed and the water cooling pump operating mode, a temperature control parameter optimization plan is generated.

[0116] Data acquisition submodule: Based on the cooling performance optimization solution, it acquires temperature data from the battery pack and the environment. Sensors read temperature changes in various areas within the battery pack in real time. Using a temperature data acquisition algorithm and setting a sampling frequency of 1 Hz, it monitors temperature fluctuations in various areas within the battery pack in real time and records ambient temperature changes. Using a data storage algorithm, it stores the collected temperature data by timestamp to generate a temperature change dataset.

[0117] Fluctuation analysis submodule: Based on the temperature change data set, the module analyzes the temperature fluctuations of the battery pack. Using the Fourier transform algorithm, the module converts time-domain data into frequency-domain data, analyzes the amplitude and frequency of temperature changes, and sets the frequency resolution to 0.1 Hz. The module then calculates the stability index of the battery pack. The module then uses the volatility calculation formula, combined with the standard deviation and mean of the temperature fluctuations, to identify areas with large fluctuations. The module then uses the peak detection algorithm to mark these areas with large temperature fluctuations, generating the temperature fluctuation analysis results.

[0118] Parameter adjustment submodule: Based on the temperature fluctuation analysis results and the actual temperature control requirements of the battery pack, the operating mode and parameters of the cooling equipment are adjusted in real time. The PID control algorithm is used, and the proportional coefficient is set to 1.8, the integral coefficient is set to 0.6, and the differential coefficient is set to 0.4. The air cooling pump speed is adjusted through the PWM control signal, and the adjustment range is set to 1200RPM to 3500RPM. At the same time, the working mode of the water cooling pump is adjusted, and the flow adjustment range is set to 5L / min to 30L / min to generate a temperature control parameter optimization plan.

[0119] 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 lithium battery pack intelligent heat dissipation control system, characterized in that: The system comprises: Temperature monitoring and assessment module: This module uses sensors to obtain the battery pack and ambient temperature, and determines whether they exceed the preset range. If so, it collects temperature change information, selects a heat dissipation method based on the change characteristics, controls the cooling device on and off, and generates an environmental status assessment result. Temperature distribution module: Based on the environmental status assessment results, obtain the temperature data of each area, use the long short-term memory network to analyze the temperature distribution, compare the temperature difference, select the adaptation area for adjustment, dynamically calculate the regional heat dissipation requirements, and generate a temperature distribution plan; Cooling equipment scheduling module: Based on the temperature distribution plan, analyzes temperature changes and equipment load, matches the scheduling plan with load and demand, adjusts the air cooling pump speed and water cooling pump mode, and generates a cooling scheduling plan; Temperature control scheduling decision module: Based on the heat dissipation scheduling plan, combined with the working status and temperature changes of the battery pack, adjusts the operating status and speed of the air cooling and water cooling equipment to generate a temperature control adjustment plan; Cooling system operation module: adjusts the speed and temperature of the air cooling pump and the water cooling pump according to the temperature control adjustment scheme, determines whether the cooling demand is met, performs dynamic adjustment, and generates temperature control status information; Heat dissipation efficiency adjustment module: Based on the temperature control status information, a greedy algorithm is used to compress the temperature data, obtain the temperature difference and evaluate the cooling effect, compare the target efficiency with the actual effect, perform dynamic optimization scheduling, and generate a cooling performance optimization plan; Temperature control parameter optimization module: Based on the cooling performance optimization plan, it receives temperature fluctuation data in real time, analyzes the operating status in combination with environmental changes, adjusts the mode and parameters, and generates an optimization adjustment plan; The temperature distribution module includes: Temperature data acquisition submodule: Based on the environmental status assessment results, real-time temperature data is obtained from sensors in each area. The temperature data is analyzed and processed using a long short-term memory network. The temperature changes in each area of the battery pack are recorded. The sensor data is verified and calibrated in real time based on the analyzed and processed data to ensure the integrity and validity of the temperature data, and a regional temperature dataset is generated. Regional temperature difference comparison submodule: Based on the regional temperature data set, compare the temperature values of different regions, calculate the temperature difference between the regions, filter the regions according to the temperature difference, select the adapted regions for temperature adjustment, and generate the temperature difference evaluation results; Heat dissipation requirement calculation submodule: Based on the temperature difference assessment results, it calculates the heat dissipation required for each area and generates a temperature distribution plan based on the regional temperature difference and the heat dissipation requirements of the battery area; The specific formula for analyzing and processing temperature data by the long short-term memory network is as follows: ； in: is the temperature forecast value at the current moment, is the input data at the current moment, is the weight matrix between input data and output, is the weight matrix between the output at the previous moment and the output at the current moment, is the bias term, is the Sigmoid activation function, is the coefficient of temperature change rate, is the time interval parameter, is the sensor calibration coefficient, is the sensor performance parameter, Indicates the hidden state of the last moment, reflecting the battery pack at time The comprehensive representation of the temperature distribution, operating status of the heat dissipation equipment and control instruction information at the current moment is used to support the temperature control decision calculation at the current moment.

2. The intelligent heat dissipation control system for lithium battery pack according to claim 1, characterized in that: The temperature monitoring and evaluation module includes: Temperature acquisition submodule: This module acquires battery pack temperature and ambient temperature data through sensors, records temperature change information in real time, stores the data, and periodically calibrates the sensors to verify the validity of the temperature data, generating a temperature data set. Temperature over-limit judgment submodule: Based on the temperature data set, it compares the battery pack temperature with the ambient temperature in real time to determine whether it exceeds the preset range. By comparing the temperature thresholds of the battery and the ambient temperature, it performs temperature over-limit identification and generates an over-limit temperature assessment result; Heat dissipation mode selection submodule: Based on the over-limit temperature assessment results, obtain the temperature change data of the environment and battery pack, analyze the temperature difference between the environment and the battery, combine the over-limit judgment, select the appropriate heat dissipation mode, adjust the heat dissipation plan, control the switch status of the air cooling and water cooling equipment, and generate the environmental status assessment results.

3. The intelligent heat dissipation control system for lithium battery pack according to claim 1, characterized in that: The heat dissipation device scheduling module includes: Temperature change analysis submodule: Based on the temperature distribution scheme, it obtains temperature data from sensors in each area of the battery pack, records regional temperature fluctuations in real time, calculates the temperature differences between areas, analyzes the temperature rise and fall trends of each area, and generates regional temperature change trends; Load status judgment submodule: Based on the temperature change trend of the area, check the current load status of the air cooling and water cooling equipment, analyze whether they can meet the temperature control requirements of each area, evaluate the working status of the air cooling and water cooling equipment, calculate the load situation, and generate the load status evaluation result; Equipment scheduling generation submodule: Based on the load state assessment result, a scheduling scheme adapted to the heat dissipation requirement is selected, the speed of the air cooling pump and the working mode of the water cooling pump are adjusted, and a heat dissipation scheduling scheme is generated.

4. The intelligent heat dissipation control system for lithium battery pack according to claim 1, characterized in that: The temperature control scheduling decision module includes: Working status acquisition submodule: Based on the heat dissipation scheduling plan, it obtains the current working mode from the battery pack, reads the real-time temperature data of the battery pack and each area, monitors the working status of the battery pack in real time and records the relevant temperature fluctuations, the consistency of temperature data and working mode, and generates battery working status data; Temperature judgment submodule: Based on battery operating status data, it compares the battery pack's operating mode with the temperature changes in each area, determines whether the regional temperature has reached the preset adjustment threshold, analyzes the temperature change trend of each area, verifies the area that needs adjustment, and generates a temperature change assessment result; Equipment speed regulation operation submodule: Based on the temperature change assessment results, air cooling and water cooling equipment are selected for adjustment. By controlling the speed of the air cooling pump and the working mode of the water cooling pump, the operating status of the equipment is adjusted, the working intensity of the temperature control equipment in each area is adjusted, and a temperature control adjustment plan is generated.

5. The intelligent heat dissipation control system for lithium battery pack according to claim 1, characterized in that: The cooling system operation module includes: Equipment adjustment submodule: Based on the temperature control scheme, read the operating status and adjust the speed of the air cooling pump and the temperature setting of the water cooling pump, adjust the working parameters, and generate equipment adjustment results; Cooling demand check submodule: Based on the equipment adjustment results, monitor and analyze the temperature changes in the battery area, check the current cooling demand in real time, evaluate whether the existing equipment meets the temperature control requirements of each area, and verify whether the cooling equipment is sufficient by dynamically adjusting the battery area temperature to generate a cooling demand assessment result; Temperature control submodule: Based on the cooling demand assessment results, according to the temperature changes in the battery area, dynamically adjust the air cooling pump speed and the water cooling pump working mode, adjust the equipment working mode, monitor the relationship between the equipment output and the temperature changes in the battery area in real time, and generate temperature control status information.

6. The intelligent heat dissipation control system for lithium battery pack according to claim 1, characterized in that: The heat dissipation efficiency adjustment module includes: Temperature difference acquisition submodule: Based on the temperature control status information, reads the real-time temperature of the battery area and calculates the difference from the target temperature, and records and stores the temperature difference data to generate a temperature difference data set; Cooling effect judgment submodule: Based on the temperature difference data set, compare and analyze the target heat dissipation efficiency and the actual cooling effect, monitor the operating status of the cooling equipment, and generate a cooling effect evaluation result; Heat dissipation scheduling optimization submodule: Based on the cooling effect evaluation results, a greedy algorithm is used to compress the temperature monitoring data, while analyzing the operating parameters and cooling requirements, optimizing the scheduling and working mode of the cooling equipment, adjusting the heat dissipation parameters, and generating a cooling performance optimization plan.

7. The intelligent heat dissipation control system for lithium battery pack according to claim 1, characterized in that: The specific formula for compressing temperature monitoring data using the greedy algorithm is as follows: ； in: is the total heat dissipation efficiency, For the Temperature data for each region, is the ambient temperature, Calculated from the thermal conductivity of the regional material and the heat sink efficiency, For the The fan coefficient of each area, For the The heat transfer coefficient of the region, is the factor affecting the cooling efficiency of the fan speed, is the flow regulation coefficient of the liquid cooling system, is the number of cooling zones.

8. The intelligent heat dissipation control system for lithium battery pack according to claim 1, characterized in that: The temperature control parameter optimization module includes: Data acquisition submodule: Based on the cooling performance optimization solution, it obtains temperature data from the battery pack and the environment. It uses sensors to read the temperature changes of various areas in the battery pack in real time, records the ambient temperature fluctuations, and generates a temperature change data set. Fluctuation analysis submodule: Based on the temperature change data set, analyze the temperature fluctuation of the battery pack, check the amplitude and frequency of the temperature change, calculate the stability index of the battery pack, identify the area according to the fluctuation situation, and generate the temperature fluctuation analysis results; Parameter adjustment submodule: Based on the temperature fluctuation analysis results and the actual temperature control requirements of the battery pack, the operating mode and parameters of the heat dissipation equipment are adjusted in real time, and a temperature control parameter optimization plan is generated by adjusting the air cooling pump speed and the water cooling pump working mode.

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