Immersive liquid-cooled power battery thermal management method and system based on big data

Abstract

The invention provides an immersion type liquid-cooled power battery thermal management method and system based on big data. The method comprises the following steps: monitoring a preset power battery pack in real time, collecting temperature monitoring information, extracting temperature characteristic data, processing according to the temperature characteristic data, obtaining a temperature state index, judging the temperature state of the power battery pack, and if the temperature of the power battery pack is abnormal and too low, determining that the power battery pack is abnormal. If yes, the heat medium system and the heating pack system are started to heat the power battery pack, characteristic data of the heat medium system are obtained and processed to obtain a heat medium efficiency coefficient, characteristic data of the heating pack system are obtained and processed to obtain a heating efficiency index, and according to the temperature state index and the heat medium efficiency coefficient, the power battery pack is obtained. Processing is carried out in combination with the heating efficiency index, the state index of the battery pack after heating is obtained, the state of the power battery pack is judged, corresponding adjustment is carried out, and therefore the immersion type liquid-cooled power battery thermal management technology based on big data is achieved.

Description

Technical Field

[0001] The present application relates to the field of battery thermal management technology, and more specifically, to an immersion liquid-cooled power battery thermal management method and system based on big data. Background Art

[0002] Power battery and other related industries are rapidly heating up, and the industry prospects are very broad. However, at present, the power battery industry is still in the early stages of development, and the industrial chain is relatively long, including key materials, power batteries, key equipment, system integration, demonstration applications and many other links. There are still problems such as scattered R&D forces, small industrialization scale, immature commercialization model, lack of industry technology and product standards. At present, it is urgent to improve the cost performance of power batteries and break the technical bottleneck of battery management system. Only when power batteries have breakthroughs in technology and cost can electric vehicles be industrialized.

[0003] Although power batteries have been widely used, they are prone to thermal stability safety issues due to the presence of multiple chemical and material transfer and electrochemical reactions during the charging and discharging process, as well as the high charge and discharge current density and poor heat dissipation conditions. In addition to affecting the service life, short circuits, high temperatures and other conditions can cause thermal safety issues for batteries. Thermal safety is an important factor affecting use. In order to promote the large-scale commercial development of power batteries, it is necessary to study their thermal safety.

[0004] In view of the above problems, effective technical solutions are urgently needed. Summary of the invention

[0005] The purpose of the present application is to provide a method and system for thermal management of immersed liquid-cooled power batteries based on big data. The method and system can monitor the preset power battery pack in real time and collect temperature monitoring information, extract temperature characteristic data, process to obtain the temperature state index, and judge the temperature state of the power battery pack. The power battery pack with abnormal and too low temperature can be heated, and corresponding adjustments can be made according to the heating effect, so as to realize the technology of thermal management of immersed liquid-cooled power batteries based on big data.

[0006] The present application also provides an immersion liquid-cooled power battery thermal management method based on big data, comprising the following steps: Monitor the preset power battery pack in real time and collect temperature monitoring information, and extract temperature characteristic data; Processing the temperature characteristic data to obtain a temperature state index, and determining the temperature state of the power battery pack; If the temperature of the power battery pack is abnormal and too low, the thermal medium system and the heating pack system are started to heat the power battery pack; Acquiring characteristic data of the thermal medium system and processing to obtain a thermal medium efficiency coefficient; Acquire characteristic data of the heating pack system and process to obtain a heating efficiency index; According to the temperature state index and the heat transfer medium efficiency coefficient, combined with the heating efficiency index, processing is performed to obtain a battery pack state index after heating, and the power battery pack state is determined and adjusted accordingly.

[0007] Optionally, in the big data-based immersion liquid-cooled power battery thermal management method described in the present application, the real-time monitoring of a preset power battery pack and the collection of temperature monitoring information, and the extraction of temperature characteristic data, include: Monitor the preset power battery pack in real time and collect the temperature monitoring information of each single battery, and extract the temperature characteristic data; The temperature characteristic data includes temperature average value data, temperature extreme difference data, temperature standard deviation data and temperature coefficient of variation.

[0008] Optionally, in the big data-based immersion liquid-cooled power battery thermal management method described in the present application, the step of processing the temperature characteristic data to obtain a temperature state index and determining the temperature state of the power battery pack includes: Processing is performed according to the temperature average value data, temperature extreme difference data, temperature standard deviation data and temperature variation coefficient to obtain a temperature state index; Comparing the temperature state index with a preset temperature state threshold, and determining the temperature state of the power battery pack according to the comparison result; If the temperature state index is less than the first preset temperature state threshold, it indicates that the temperature of the power battery pack is abnormal and too low; If the temperature state index is greater than a second preset temperature state threshold, it indicates that the temperature of the power battery pack is abnormal and too high.

[0009] Optionally, in the big data-based immersion liquid-cooled power battery thermal management method described in the present application, the step of acquiring the heating characteristic data of the heat transfer medium system and processing to obtain the heat transfer medium efficiency coefficient includes: Acquiring heating characteristic data of the heat medium system, including maximum heat medium temperature data, heat medium flow data, heat medium pressure data, and heat medium energy efficiency ratio data; The heat medium efficiency coefficient is obtained by processing the maximum heat medium temperature data, the heat medium flow data, the heat medium pressure data and the heat medium energy efficiency ratio data.

[0010] Optionally, in the big data-based immersion liquid-cooled power battery thermal management method described in the present application, the acquiring characteristic data of the heating pack system and processing to obtain a heating efficiency index includes: Acquiring characteristic data of the heating pack system, including maximum temperature data, stable temperature data, and rated power data; The heating efficiency index is obtained by processing the maximum temperature data, the stable temperature data and the rated power data.

[0011] Optionally, in the big data-based immersion liquid-cooled power battery thermal management method described in the present application, the temperature state index and the heat transfer medium efficiency coefficient are processed in combination with the heating efficiency index to obtain a battery pack state index after heating, and the power battery pack state is judged and adjusted accordingly, including: According to the temperature state index and the heat medium efficiency coefficient, combined with the heating efficiency index, a battery pack state index after heating is obtained; The heated battery pack state index is compared with a preset heated state threshold, and the power battery pack state is determined according to the threshold comparison result and adjusted accordingly.

[0012] Optionally, in the big data-based immersion liquid-cooled power battery thermal management method described in the present application, it also includes: If the temperature of the power battery pack is abnormal and too high, the cooling medium system and the radiator system are started to cool the power battery pack; Acquiring characteristic data of the cold media system and processing to obtain a cold media efficiency coefficient; Acquiring characteristic data of the radiator system and processing to obtain a heat dissipation efficiency index; According to the temperature state index and the cooling medium efficiency coefficient, combined with the heat dissipation efficiency index, processing is performed to obtain the battery pack state index after cooling, and the power battery pack state is determined and adjusted accordingly.

[0013] In a second aspect, the present application provides an immersion liquid-cooled power battery thermal management system based on big data, the system comprising: a memory and a processor, the memory comprising a program of an immersion liquid-cooled power battery thermal management method based on big data, and the program of the immersion liquid-cooled power battery thermal management method based on big data is executed by the processor to implement the following steps: Monitor the preset power battery pack in real time and collect temperature monitoring information, and extract temperature characteristic data; Processing the temperature characteristic data to obtain a temperature state index, and determining the temperature state of the power battery pack; If the temperature of the power battery pack is abnormal and too low, the thermal medium system and the heating pack system are started to heat the power battery pack; Acquiring characteristic data of the thermal medium system and processing to obtain a thermal medium efficiency coefficient; Acquire characteristic data of the heating pack system and process to obtain a heating efficiency index; According to the temperature state index and the heat transfer medium efficiency coefficient, combined with the heating efficiency index, processing is performed to obtain a battery pack state index after heating, and the power battery pack state is determined and adjusted accordingly.

[0014] Optionally, in the big data-based immersion liquid-cooled power battery thermal management system described in the present application, the real-time monitoring of the preset power battery pack and the collection of temperature monitoring information, and the extraction of temperature characteristic data, include: Monitor the preset power battery pack in real time and collect the temperature monitoring information of each single battery, and extract the temperature characteristic data; The temperature characteristic data includes temperature average value data, temperature extreme difference data, temperature standard deviation data and temperature coefficient of variation.

[0015] Optionally, in the immersion liquid-cooled power battery thermal management system based on big data described in the present application, the processing according to the temperature characteristic data to obtain a temperature state index and judge the temperature state of the power battery pack includes: Processing is performed according to the temperature average value data, temperature extreme difference data, temperature standard deviation data and temperature variation coefficient to obtain a temperature state index; Comparing the temperature state index with a preset temperature state threshold, and determining the temperature state of the power battery pack according to the comparison result; If the temperature state index is less than the first preset temperature state threshold, it indicates that the temperature of the power battery pack is abnormal and too low; If the temperature state index is greater than a second preset temperature state threshold, it indicates that the temperature of the power battery pack is abnormal and too high.

[0016] As can be seen from the above, the immersed liquid-cooled power battery thermal management method and system based on big data provided by the present application monitors the preset power battery pack in real time and collects temperature monitoring information, extracts temperature characteristic data, processes the temperature characteristic data, obtains the temperature state index, and judges the temperature state of the power battery pack. If the temperature of the power battery pack is abnormal and too low, the heat medium system and the heating pack system are started to heat the power battery pack, obtain the characteristic data of the heat medium system, process to obtain the heat medium efficiency coefficient, obtain the characteristic data of the heating pack system, process to obtain the heating efficiency index, and process according to the temperature state index and the heat medium efficiency coefficient in combination with the heating efficiency index to obtain the battery pack state index after heating, judge the power battery pack state and make corresponding adjustments, thereby realizing the technology of immersed liquid-cooled power battery thermal management based on big data.

[0017] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A flow chart of an immersion liquid-cooled power battery thermal management method based on big data provided in an embodiment of the present application; Figure 2 A flow chart of extracting temperature characteristic data of an immersion liquid-cooled power battery thermal management method based on big data provided in an embodiment of the present application; Figure 3 A flow chart of determining the temperature state of a power battery pack in a thermal management method for an immersion liquid-cooled power battery based on big data provided in an embodiment of the present application; Figure 4 A flow chart of obtaining a heat transfer medium efficiency coefficient for an immersion liquid-cooled power battery thermal management method based on big data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0021] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0022] Please refer to Figure 1 , Figure 1 The following is a flow chart of a thermal management method for an immersed liquid-cooled power battery based on big data in some embodiments of the present application. The thermal management method for an immersed liquid-cooled power battery based on big data is used in terminal devices, such as computers, mobile phone terminals, etc. The thermal management method for an immersed liquid-cooled power battery based on big data includes the following steps: S11. Real-time monitoring of a preset power battery pack, collecting temperature monitoring information, and extracting temperature characteristic data; S12, processing the temperature characteristic data to obtain a temperature state index, and determining the temperature state of the power battery pack; S13, if the temperature of the power battery pack is abnormal and too low, starting the thermal medium system and the heating pack system to heat the power battery pack; S14, acquiring characteristic data of the thermal medium system, and processing to obtain a thermal medium efficiency coefficient; S15, acquiring characteristic data of the heating pack system, and processing to obtain a heating efficiency index; S16, processing is performed according to the temperature state index and the heat medium efficiency coefficient in combination with the heating efficiency index to obtain a battery pack state index after heating, determine the power battery pack state and make corresponding adjustments.

[0023] It should be noted that although power batteries have been widely used, because there are multiple chemical, material transfer and electrochemical reactions in the charging and discharging process, and the charging and discharging current density is large and the heat dissipation conditions are poor, it is easy to cause thermal stability safety problems. In addition to affecting the service life, short circuits, high temperatures and other conditions will cause thermal safety problems of the battery. Thermal safety is an important factor affecting the use. In order to promote the large-scale commercial development of power batteries, it is necessary to study their thermal safety. In order to ensure that the temperature of the cells in various parts of the entire battery pack is uniform and constant during operation, it is necessary to develop a new wide-temperature range power battery based on an immersion liquid-cooled power battery thermal management system to ensure that the power battery can perform at its best under different temperature environments. First, the preset power battery pack is monitored in real time and temperature monitoring information is collected, and temperature characteristic data is extracted. The temperature characteristic data is processed to obtain a temperature state index, and the temperature state of the power battery pack is judged. If the temperature of the power battery pack is abnormal and too low, the thermal medium system and the heating pack system are started to heat the power battery pack, the characteristic data of the thermal medium system are obtained, and the thermal medium efficiency coefficient is obtained by processing. The characteristic data of the heating pack system is obtained, and the heating efficiency index is obtained by processing. According to the temperature state index and the thermal medium efficiency coefficient, combined with the heating efficiency index, the battery pack state index after heating is obtained, the power battery pack state is judged and corresponding adjustments are made, thereby realizing the technology of thermal management of immersion liquid-cooled power batteries based on big data.

[0024] Please refer to Figure 2 , Figure 2 The flowchart of extracting temperature characteristic data of the immersion liquid-cooled power battery thermal management method based on big data in some embodiments of the present application. According to an embodiment of the present invention, the real-time monitoring of the preset power battery pack and the collection of temperature monitoring information, and the extraction of temperature characteristic data, include: S21, real-time monitoring of the preset power battery pack and collecting temperature monitoring information of each single battery, and extracting temperature characteristic data; S22. The temperature characteristic data includes temperature average value data, temperature extreme difference data, temperature standard deviation data and temperature variation coefficient.

[0025] It should be noted that the power battery pack contains many single cells. The temperature of each single cell plays a decisive role in the overall temperature of the battery pack. Therefore, it is necessary to monitor the temperature monitoring information of each single cell in real time and extract temperature characteristic data, including temperature average, temperature range, temperature standard deviation and temperature variation coefficient. Among them, the temperature range is the difference between the highest temperature and the lowest temperature of all single cells, and the temperature variation coefficient is the ratio of the temperature standard deviation to the average temperature.

[0026] Please refer to Figure 3 , Figure 3The present invention is a flowchart of determining the temperature state of a power battery pack in a method for thermal management of an immersed liquid-cooled power battery based on big data in some embodiments of the present application. According to an embodiment of the present invention, the temperature characteristic data is processed to obtain a temperature state index, and the temperature state of the power battery pack is determined, including: S31, processing the temperature average value data, the temperature extreme difference data, the temperature standard deviation data and the temperature variation coefficient to obtain a temperature state index; S32, comparing the temperature state index with a preset temperature state threshold, and determining the temperature state of the power battery pack according to the comparison result; S33: If the temperature state index is less than a first preset temperature state threshold, it indicates that the temperature of the power battery pack is abnormal and too low; S34. If the temperature state index is greater than a second preset temperature state threshold, it indicates that the temperature of the power battery pack is abnormal and too high.

[0027] It should be noted that the temperature state of the power battery pack needs to be judged by the overall temperature of the power battery pack and whether the temperature of each area in the battery pack is balanced. Therefore, the temperature state index is obtained by processing the temperature average value data, temperature extreme difference data, temperature standard deviation data and temperature variation coefficient of all single batteries, and then compared with the preset threshold value to judge the temperature state of the power battery pack; Wherein, the calculation formula of the temperature state index is: ; in, is the temperature state index, , , , They are the temperature average value data, temperature range data, temperature standard deviation data, and temperature coefficient of variation. , , , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset power battery thermal management database).

[0028] Please refer to Figure 4 , Figure 4 The flowchart of obtaining the heat medium efficiency coefficient of the immersion liquid-cooled power battery thermal management method based on big data in some embodiments of the present application. According to the embodiment of the present invention, the step of obtaining the heating characteristic data of the heat medium system and processing to obtain the heat medium efficiency coefficient includes: S41, obtaining heating characteristic data of the heat medium system, including maximum heat medium temperature data, heat medium flow data, heat medium pressure data and heat medium energy efficiency ratio data; S42: Process the data on the maximum heat medium temperature, the heat medium flow rate, the heat medium pressure, and the heat medium energy efficiency ratio to obtain a heat medium efficiency coefficient.

[0029] It should be noted that when the temperature of the power battery pack is abnormal and too low, the thermal medium system and the heating pack system are started to heat the power battery pack. First, the heating characteristic data of the thermal medium system needs to be obtained, and then the thermal medium efficiency coefficient of the thermal medium system is obtained by processing; The calculation formula of the thermal medium efficiency coefficient is: ; in, is the thermal media efficiency coefficient, , , , They are the maximum heat medium temperature data, heat medium flow data, heat medium pressure data, and heat medium energy efficiency ratio data. is the thermal media compensation factor, , , , It is a preset characteristic coefficient (the compensation factor and characteristic coefficient are obtained by querying the preset power battery thermal management database).

[0030] According to an embodiment of the present invention, the step of acquiring characteristic data of the heating pack system and processing to obtain a heating efficiency index includes: Acquiring characteristic data of the heating pack system, including maximum temperature data, stable temperature data, and rated power data; The heating efficiency index is obtained by processing the maximum temperature data, the stable temperature data and the rated power data.

[0031] It should be noted that, in addition to knowing the heat medium efficiency of the heat medium system, it is also necessary to know the heating efficiency of the heating pack system. Therefore, characteristic data of the heating pack system is obtained, including the maximum temperature, stable temperature and rated power data, wherein the stable temperature refers to a relatively stable temperature reached by the heating pack after it continues to heat for a period of time. Then, the heating efficiency index of the heating pack system is obtained by processing the above data; The calculation formula of the heating efficiency index is: ; in, is the heating efficiency index, , , They are maximum temperature data, stable temperature data, and rated power data. , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset power battery thermal management database).

[0032] According to an embodiment of the present invention, the temperature state index and the heat medium efficiency coefficient are processed in combination with the heating efficiency index to obtain the battery pack state index after heating, judge the power battery pack state and make corresponding adjustments, including: According to the temperature state index and the heat medium efficiency coefficient, combined with the heating efficiency index, a battery pack state index after heating is obtained; The heated battery pack state index is compared with a preset heated state threshold, and the power battery pack state is determined according to the threshold comparison result and adjusted accordingly.

[0033] It should be noted that after the heating work of the thermal medium system and the heating pack system, the temperature of the power battery pack will change, and it is necessary to further judge the temperature state after heating. The temperature state index and the thermal medium efficiency coefficient are combined with the heating efficiency index to obtain the battery pack state index after heating, and then compare it with the preset state threshold to judge the power battery pack state and make corresponding adjustments; The calculation formula of the battery pack state index after heating is: ; in, is the battery pack status index after heating, , , They are temperature state index, heat transfer efficiency coefficient, and heating efficiency index. is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset power battery thermal management database).

[0034] According to an embodiment of the present invention, it also includes: If the temperature of the power battery pack is abnormal and too high, the cooling medium system and the radiator system are started to cool the power battery pack; Acquiring characteristic data of the cold media system and processing to obtain a cold media efficiency coefficient; Acquiring characteristic data of the radiator system and processing to obtain a heat dissipation efficiency index; According to the temperature state index and the cooling medium efficiency coefficient, combined with the heat dissipation efficiency index, processing is performed to obtain the battery pack state index after cooling, and the power battery pack state is determined and adjusted accordingly.

[0035] It should be noted that if the temperature of the power battery pack is abnormal and too high, the cooling media system and the radiator system will be started to cool the power battery pack. The temperature state index and the cooling media efficiency coefficient will be processed and combined with the heat dissipation efficiency index to obtain the battery pack state index after cooling, and the state of the power battery pack after cooling will be further judged and adjusted accordingly.

[0036] According to an embodiment of the present invention, the cooling characteristic data of the cooling medium system is processed to obtain the cooling medium efficiency coefficient, specifically including: Obtain cooling characteristic data of the refrigeration media system, including minimum refrigerant temperature data, refrigerant flow data, refrigerant pressure data, and refrigerant energy efficiency ratio data; Processing the minimum refrigerant temperature data, the refrigerant flow data, the refrigerant pressure data and the refrigerant energy efficiency ratio data to obtain a refrigerant efficiency coefficient; The calculation formula of the cooling media efficiency coefficient is: ; in, is the cooling media efficiency coefficient, , , , They are the minimum refrigerant temperature data, refrigerant flow data, refrigerant pressure data, and refrigerant energy efficiency ratio data. , , , , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset power battery thermal management database).

[0037] It should be noted that when the temperature of the power battery pack is abnormal and too high, the cooling media system and the radiator system are started to cool the power battery pack. In order to evaluate the cooling effect, the cooling characteristic data of the cooling media system is first obtained, including the minimum refrigerant temperature data, refrigerant flow data, refrigerant pressure data and refrigerant energy efficiency ratio data, and then the above data are processed to obtain the refrigerant efficiency coefficient of the refrigerant system.

[0038] According to an embodiment of the present invention, the step further includes: obtaining characteristic data of the radiator system and processing to obtain a heat dissipation efficiency index, specifically including: Acquiring characteristic data of the radiator system, including heat dissipation power data, thermal resistance data, and temperature rise data; Processing the heat dissipation power data, thermal resistance data and temperature rise data to obtain a heat dissipation efficiency index; The calculation formula of the heat dissipation efficiency index is: ; in, is the heat dissipation efficiency index, , , They are heat dissipation power data, thermal resistance data, and temperature rise data. , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset power battery thermal management database).

[0039] It should be noted that while knowing the cooling efficiency of the cooling media system, it is also necessary to know the heat dissipation efficiency of the radiator system. Therefore, the characteristic data of the radiator system is obtained, including heat dissipation power data, thermal resistance data and temperature rise data, among which the temperature rise data refers to the difference between the surface temperature of the radiator and the ambient temperature. The above data is then processed to obtain the heat dissipation efficiency index of the radiator system.

[0040] According to an embodiment of the present invention, the method further includes: performing processing according to the temperature state index and the cooling medium efficiency coefficient in combination with the heat dissipation efficiency index to obtain a battery pack state index after cooling, judging the power battery pack state and making corresponding adjustments, specifically including: According to the temperature state index and the cooling medium efficiency coefficient, combined with the heat dissipation efficiency index, a battery pack state index after cooling is obtained; Comparing the battery pack state index after cooling with a preset state threshold after cooling, judging the power battery pack state according to the threshold comparison result and making corresponding adjustments; The calculation formula of the battery pack state index after cooling is: ; in, is the battery pack status index after cooling down, , , They are temperature state index, cooling media efficiency coefficient, and heat dissipation efficiency index. , , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset power battery thermal management database).

[0041] It should be noted that after the cooling work of the cooling media system and the radiator system, the temperature of the power battery pack will change, and it is necessary to further judge the temperature state after cooling. The temperature state index and the cooling media efficiency coefficient are processed in combination with the heat dissipation efficiency index to obtain the battery pack state index after cooling, and then compare it with the preset state threshold to judge the power battery pack state and make corresponding adjustments.

[0042] In a second aspect, the present invention further discloses an immersion liquid-cooled power battery thermal management system based on big data, comprising a memory and a processor, wherein the memory comprises an immersion liquid-cooled power battery thermal management method program based on big data, and when the immersion liquid-cooled power battery thermal management method program based on big data is executed by the processor, the following steps are implemented: Monitor the preset power battery pack in real time and collect temperature monitoring information, and extract temperature characteristic data; Processing the temperature characteristic data to obtain a temperature state index, and determining the temperature state of the power battery pack; If the temperature of the power battery pack is abnormal and too low, the thermal medium system and the heating pack system are started to heat the power battery pack; Acquiring characteristic data of the thermal medium system and processing to obtain a thermal medium efficiency coefficient; Acquire characteristic data of the heating pack system and process to obtain a heating efficiency index; According to the temperature state index and the heat transfer medium efficiency coefficient, combined with the heating efficiency index, processing is performed to obtain a battery pack state index after heating, and the power battery pack state is determined and adjusted accordingly.

[0043] It should be noted that although power batteries have been widely used, because there are multiple chemical, material transfer and electrochemical reactions in the charging and discharging process, and the charging and discharging current density is large and the heat dissipation conditions are poor, it is easy to cause thermal stability safety problems. In addition to affecting the service life, short circuits, high temperatures and other conditions will cause thermal safety problems of the battery. Thermal safety is an important factor affecting the use. In order to promote the large-scale commercial development of power batteries, it is necessary to study their thermal safety. In order to ensure that the temperature of the cells in various parts of the entire battery pack is uniform and constant during operation, it is necessary to develop a new wide-temperature range power battery based on an immersion liquid-cooled power battery thermal management system to ensure that the power battery can perform at its best under different temperature environments. First, the preset power battery pack is monitored in real time and temperature monitoring information is collected, and temperature characteristic data is extracted. The temperature characteristic data is processed to obtain a temperature state index, and the temperature state of the power battery pack is judged. If the temperature of the power battery pack is abnormal and too low, the thermal medium system and the heating pack system are started to heat the power battery pack, the characteristic data of the thermal medium system are obtained, and the thermal medium efficiency coefficient is obtained by processing. The characteristic data of the heating pack system is obtained, and the heating efficiency index is obtained by processing. According to the temperature state index and the thermal medium efficiency coefficient, combined with the heating efficiency index, the battery pack state index after heating is obtained, the power battery pack state is judged and corresponding adjustments are made, thereby realizing the technology of thermal management of immersion liquid-cooled power batteries based on big data.

[0044] According to an embodiment of the present invention, real-time monitoring of a preset power battery pack and collecting temperature monitoring information, and extracting temperature characteristic data, includes: Monitor the preset power battery pack in real time and collect the temperature monitoring information of each single battery, and extract the temperature characteristic data; The temperature characteristic data includes temperature average value data, temperature extreme difference data, temperature standard deviation data and temperature coefficient of variation.

[0045] It should be noted that the power battery pack contains many single cells. The temperature of each single cell plays a decisive role in the overall temperature of the battery pack. Therefore, it is necessary to monitor the temperature monitoring information of each single cell in real time and extract temperature characteristic data, including temperature average, temperature range, temperature standard deviation and temperature variation coefficient. Among them, the temperature range is the difference between the highest temperature and the lowest temperature of all single cells, and the temperature variation coefficient is the ratio of the temperature standard deviation to the average temperature.

[0046] According to an embodiment of the present invention, the step of processing the temperature characteristic data to obtain a temperature state index and determining the temperature state of the power battery pack includes: Processing is performed according to the temperature average value data, temperature extreme difference data, temperature standard deviation data and temperature variation coefficient to obtain a temperature state index; Comparing the temperature state index with a preset temperature state threshold, and determining the temperature state of the power battery pack according to the comparison result; If the temperature state index is less than the first preset temperature state threshold, it indicates that the temperature of the power battery pack is abnormal and too low; If the temperature state index is greater than a second preset temperature state threshold, it indicates that the temperature of the power battery pack is abnormal and too high.

[0047] It should be noted that the temperature state of the power battery pack needs to be judged by the overall temperature of the power battery pack and whether the temperature of each area in the battery pack is balanced. Therefore, the temperature state index is obtained by processing the temperature average value data, temperature extreme difference data, temperature standard deviation data and temperature variation coefficient of all single batteries, and then compared with the preset threshold value to judge the temperature state of the power battery pack; Wherein, the calculation formula of the temperature state index is: ; in, is the temperature state index, , , , They are the temperature average value data, temperature range data, temperature standard deviation data, and temperature coefficient of variation. , , , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset power battery thermal management database).

[0048] According to an embodiment of the present invention, the step of acquiring the heating characteristic data of the thermal medium system and processing to obtain the thermal medium efficiency coefficient includes: Acquiring heating characteristic data of the heat medium system, including maximum heat medium temperature data, heat medium flow data, heat medium pressure data, and heat medium energy efficiency ratio data; The heat medium efficiency coefficient is obtained by processing the maximum heat medium temperature data, the heat medium flow data, the heat medium pressure data and the heat medium energy efficiency ratio data.

[0049] It should be noted that when the temperature of the power battery pack is abnormal and too low, the thermal medium system and the heating pack system are started to heat the power battery pack. First, the heating characteristic data of the thermal medium system needs to be obtained, and then the thermal medium efficiency coefficient of the thermal medium system is obtained by processing; The calculation formula of the thermal medium efficiency coefficient is: ; in, is the thermal media efficiency coefficient, , , , They are the maximum heat medium temperature data, heat medium flow data, heat medium pressure data, and heat medium energy efficiency ratio data. is the thermal media compensation factor, , , , It is a preset characteristic coefficient (the compensation factor and characteristic coefficient are obtained by querying the preset power battery thermal management database).

[0050] According to an embodiment of the present invention, the step of acquiring characteristic data of the heating pack system and processing to obtain a heating efficiency index includes: Acquiring characteristic data of the heating pack system, including maximum temperature data, stable temperature data, and rated power data; The heating efficiency index is obtained by processing the maximum temperature data, the stable temperature data and the rated power data.

[0051] It should be noted that, in addition to knowing the heat medium efficiency of the heat medium system, it is also necessary to know the heating efficiency of the heating pack system. Therefore, characteristic data of the heating pack system is obtained, including the maximum temperature, stable temperature and rated power data, wherein the stable temperature refers to a relatively stable temperature reached by the heating pack after it continues to heat for a period of time. Then, the heating efficiency index of the heating pack system is obtained by processing the above data; The calculation formula of the heating efficiency index is: ; in, is the heating efficiency index, , , They are maximum temperature data, stable temperature data, and rated power data. , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset power battery thermal management database).

[0052] According to an embodiment of the present invention, the temperature state index and the heat medium efficiency coefficient are processed in combination with the heating efficiency index to obtain the battery pack state index after heating, judge the power battery pack state and make corresponding adjustments, including: According to the temperature state index and the heat medium efficiency coefficient, combined with the heating efficiency index, a battery pack state index after heating is obtained; The heated battery pack state index is compared with a preset heated state threshold, and the power battery pack state is determined according to the threshold comparison result and adjusted accordingly.

[0053] It should be noted that after the heating work of the thermal medium system and the heating pack system, the temperature of the power battery pack will change, and it is necessary to further judge the temperature state after heating. The temperature state index and the thermal medium efficiency coefficient are combined with the heating efficiency index to obtain the battery pack state index after heating, and then compare it with the preset state threshold to judge the power battery pack state and make corresponding adjustments; The calculation formula of the battery pack state index after heating is: ; in, is the battery pack status index after heating, , , They are temperature state index, heat transfer efficiency coefficient, and heating efficiency index. is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset power battery thermal management database).

[0054] According to an embodiment of the present invention, it also includes: If the temperature of the power battery pack is abnormal and too high, the cooling medium system and the radiator system are started to cool the power battery pack; Acquiring characteristic data of the cold media system and processing to obtain a cold media efficiency coefficient; Acquiring characteristic data of the radiator system and processing to obtain a heat dissipation efficiency index; According to the temperature state index and the cooling medium efficiency coefficient, combined with the heat dissipation efficiency index, processing is performed to obtain the battery pack state index after cooling, and the power battery pack state is determined and adjusted accordingly.

[0055] It should be noted that if the temperature of the power battery pack is abnormal and too high, the cooling media system and the radiator system will be started to cool the power battery pack. The temperature state index and the cooling media efficiency coefficient will be processed and combined with the heat dissipation efficiency index to obtain the battery pack state index after cooling, and the state of the power battery pack after cooling will be further judged and adjusted accordingly.

[0056] According to an embodiment of the present invention, the cooling characteristic data of the cooling medium system is processed to obtain the cooling medium efficiency coefficient, specifically including: Obtain cooling characteristic data of the refrigeration media system, including minimum refrigerant temperature data, refrigerant flow data, refrigerant pressure data, and refrigerant energy efficiency ratio data; Processing the minimum refrigerant temperature data, the refrigerant flow data, the refrigerant pressure data and the refrigerant energy efficiency ratio data to obtain a refrigerant efficiency coefficient; The calculation formula of the cooling media efficiency coefficient is: ; in, is the cooling media efficiency coefficient, , , , They are the minimum refrigerant temperature data, refrigerant flow data, refrigerant pressure data, and refrigerant energy efficiency ratio data. , , , , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset power battery thermal management database).

[0057] It should be noted that when the temperature of the power battery pack is abnormal and too high, the cooling media system and the radiator system are started to cool the power battery pack. In order to evaluate the cooling effect, the cooling characteristic data of the cooling media system is first obtained, including the minimum refrigerant temperature data, refrigerant flow data, refrigerant pressure data and refrigerant energy efficiency ratio data, and then the above data are processed to obtain the refrigerant efficiency coefficient of the refrigerant system.

[0058] According to an embodiment of the present invention, the step further includes: obtaining characteristic data of the radiator system and processing to obtain a heat dissipation efficiency index, specifically including: Acquiring characteristic data of the radiator system, including heat dissipation power data, thermal resistance data, and temperature rise data; Processing the heat dissipation power data, thermal resistance data and temperature rise data to obtain a heat dissipation efficiency index; The calculation formula of the heat dissipation efficiency index is: ; in, is the heat dissipation efficiency index, , , They are heat dissipation power data, thermal resistance data, and temperature rise data. , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset power battery thermal management database).

[0059] It should be noted that while knowing the cooling efficiency of the cooling media system, it is also necessary to know the heat dissipation efficiency of the radiator system. Therefore, the characteristic data of the radiator system is obtained, including heat dissipation power data, thermal resistance data and temperature rise data, among which the temperature rise data refers to the difference between the surface temperature of the radiator and the ambient temperature. The above data is then processed to obtain the heat dissipation efficiency index of the radiator system.

[0060] According to an embodiment of the present invention, the method further includes: performing processing according to the temperature state index and the cooling medium efficiency coefficient in combination with the heat dissipation efficiency index to obtain a battery pack state index after cooling, judging the power battery pack state and making corresponding adjustments, specifically including: According to the temperature state index and the cooling medium efficiency coefficient, combined with the heat dissipation efficiency index, a battery pack state index after cooling is obtained; Comparing the battery pack state index after cooling with a preset state threshold after cooling, judging the power battery pack state according to the threshold comparison result and making corresponding adjustments; The calculation formula of the battery pack state index after cooling is: ; in, is the battery pack status index after cooling down, , , They are temperature state index, cooling media efficiency coefficient, and heat dissipation efficiency index. , , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset power battery thermal management database).

[0061] It should be noted that after the cooling work of the cooling media system and the radiator system, the temperature of the power battery pack will change, and it is necessary to further judge the temperature state after cooling. The temperature state index and the cooling media efficiency coefficient are processed in combination with the heat dissipation efficiency index to obtain the battery pack state index after cooling, and then compare it with the preset state threshold to judge the power battery pack state and make corresponding adjustments.

[0062] The invention discloses an immersion liquid-cooled power battery thermal management method and system based on big data. The method and system monitor a preset power battery pack in real time, collect temperature monitoring information, extract temperature characteristic data, process the temperature characteristic data, obtain a temperature state index, and judge the temperature state of the power battery pack. If the temperature of the power battery pack is abnormal and too low, the heat medium system and the heating pack system are started to heat the power battery pack, obtain characteristic data of the heat medium system, process the heat medium efficiency coefficient, obtain characteristic data of the heating pack system, process the heating efficiency index, process the temperature state index and the heat medium efficiency coefficient in combination with the heating efficiency index, obtain the battery pack state index after heating, judge the power battery pack state and make corresponding adjustments, thereby realizing the technology of immersion liquid-cooled power battery thermal management based on big data.

[0063] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0064] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0065] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0066] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, the aforementioned program can be stored in a readable storage medium, and when the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0067] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A thermal management method for an immersion liquid-cooled power battery based on big data, characterized in that: The following steps are involved: Monitor the preset power battery pack in real time and collect temperature monitoring information, and extract temperature characteristic data; Processing the temperature characteristic data to obtain a temperature state index, and determining the temperature state of the power battery pack; If the temperature of the power battery pack is abnormal and too low, the thermal medium system and the heating pack system are started to heat the power battery pack; Acquiring characteristic data of the thermal medium system and processing to obtain a thermal medium efficiency coefficient; Acquire characteristic data of the heating pack system and process to obtain a heating efficiency index; According to the temperature state index and the heat transfer medium efficiency coefficient, combined with the heating efficiency index, processing is performed to obtain a battery pack state index after heating, and the power battery pack state is determined and adjusted accordingly.

2. The immersion liquid-cooled power battery thermal management method based on big data according to claim 1 is characterized in that: The real-time monitoring of the preset power battery pack and the collection of temperature monitoring information and the extraction of temperature characteristic data include: Monitor the preset power battery pack in real time and collect the temperature monitoring information of each single battery, and extract the temperature characteristic data; The temperature characteristic data includes temperature average value data, temperature extreme difference data, temperature standard deviation data and temperature coefficient of variation.

3. The immersion liquid-cooled power battery thermal management method based on big data according to claim 2 is characterized in that: The processing according to the temperature characteristic data to obtain a temperature state index and determine the temperature state of the power battery pack includes: Processing is performed according to the temperature average value data, temperature extreme difference data, temperature standard deviation data and temperature variation coefficient to obtain a temperature state index; Comparing the temperature state index with a preset temperature state threshold, and determining the temperature state of the power battery pack according to the comparison result; If the temperature state index is less than the first preset temperature state threshold, it indicates that the temperature of the power battery pack is abnormal and too low; If the temperature state index is greater than a second preset temperature state threshold, it indicates that the temperature of the power battery pack is abnormal and too high.

4. The immersion liquid-cooled power battery thermal management method based on big data according to claim 3 is characterized in that: The step of acquiring the heating characteristic data of the heat medium system and processing the data to obtain the heat medium efficiency coefficient includes: Acquiring heating characteristic data of the heat medium system, including maximum heat medium temperature data, heat medium flow data, heat medium pressure data, and heat medium energy efficiency ratio data; The heat medium efficiency coefficient is obtained by processing the maximum heat medium temperature data, the heat medium flow data, the heat medium pressure data and the heat medium energy efficiency ratio data.

5. The big data-based immersion liquid-cooled power battery thermal management method according to claim 4, characterized in that: The step of acquiring characteristic data of the heating pack system and processing the data to obtain a heating efficiency index comprises: Acquiring characteristic data of the heating pack system, including maximum temperature data, stable temperature data, and rated power data; The heating efficiency index is obtained by processing the maximum temperature data, the stable temperature data and the rated power data.

6. The immersion liquid-cooled power battery thermal management method based on big data according to claim 5 is characterized in that: The step of processing the temperature state index and the heat transfer medium efficiency coefficient in combination with the heating efficiency index to obtain a battery pack state index after heating, determining the power battery pack state and making corresponding adjustments includes: According to the temperature state index and the heat medium efficiency coefficient, combined with the heating efficiency index, a battery pack state index after heating is obtained; The heated battery pack state index is compared with a preset heated state threshold, and the power battery pack state is determined according to the threshold comparison result and adjusted accordingly.

7. The method for thermal management of an immersion liquid-cooled power battery based on big data according to claim 6 further comprises: If the temperature of the power battery pack is abnormal and too high, the cooling medium system and the radiator system are started to cool the power battery pack; Acquire characteristic data of the cold media system, and process to obtain a cold media efficiency coefficient; Acquiring characteristic data of the radiator system and processing to obtain a heat dissipation efficiency index; According to the temperature state index and the cooling medium efficiency coefficient, combined with the heat dissipation efficiency index, processing is performed to obtain the battery pack state index after cooling, and the power battery pack state is determined and adjusted accordingly.

8. An immersion liquid-cooled power battery thermal management system based on big data, characterized in that: The system includes: a memory and a processor, wherein the memory includes a program of an immersion liquid-cooled power battery thermal management method based on big data, and when the program of the immersion liquid-cooled power battery thermal management method based on big data is executed by the processor, the following steps are implemented: Monitor the preset power battery pack in real time and collect temperature monitoring information, and extract temperature characteristic data; Processing the temperature characteristic data to obtain a temperature state index, and determining the temperature state of the power battery pack; If the temperature of the power battery pack is abnormal and too low, the thermal medium system and the heating pack system are started to heat the power battery pack; Acquiring characteristic data of the thermal medium system and processing to obtain a thermal medium efficiency coefficient; Acquire characteristic data of the heating pack system and process to obtain a heating efficiency index; According to the temperature state index and the heat transfer medium efficiency coefficient, combined with the heating efficiency index, processing is performed to obtain a battery pack state index after heating, and the power battery pack state is determined and adjusted accordingly.

9. The immersion liquid-cooled power battery thermal management system based on big data according to claim 8, characterized in that: The real-time monitoring of the preset power battery pack and the collection of temperature monitoring information and the extraction of temperature characteristic data include: Monitor the preset power battery pack in real time and collect the temperature monitoring information of each single battery, and extract the temperature characteristic data; The temperature characteristic data includes temperature average value data, temperature extreme difference data, temperature standard deviation data and temperature coefficient of variation.

10. The immersion liquid-cooled power battery thermal management system based on big data according to claim 9, characterized in that: The processing according to the temperature characteristic data to obtain a temperature state index and determine the temperature state of the power battery pack includes: Processing is performed according to the temperature average value data, temperature extreme difference data, temperature standard deviation data and temperature variation coefficient to obtain a temperature state index; Comparing the temperature state index with a preset temperature state threshold, and determining the temperature state of the power battery pack according to the comparison result; If the temperature state index is less than the first preset temperature state threshold, it indicates that the temperature of the power battery pack is abnormal and too low; If the temperature state index is greater than a second preset temperature state threshold, it indicates that the temperature of the power battery pack is abnormal and too high.

Images