CN115203909A - Thermal simulation analysis method and device for liquid cooling energy storage system and storage medium - Google Patents
Thermal simulation analysis method and device for liquid cooling energy storage system and storage medium Download PDFInfo
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Abstract
The invention discloses a thermal simulation analysis method, equipment and a storage medium for a liquid cooling energy storage system, relates to the technical field of modeling simulation, is applied to the liquid cooling energy storage system, and comprises the following steps: establishing a liquid cooling plate simulation model according to preset electric core data; establishing a battery cluster flow simulation model according to the liquid cooling plate simulation model and preset battery cluster data; establishing a battery pile flow simulation model according to the battery cluster flow simulation model and preset battery pile data; calculating to obtain flow distribution differences of a plurality of pipeline inlets in the liquid cooling energy storage system according to the battery cluster flow simulation model and the battery stack flow simulation model; obtaining a temperature difference fitting curve according to the plurality of flow distribution differences and the corresponding plurality of maximum temperatures; and calculating to obtain the maximum temperature difference of the cell stack in the liquid cooling energy storage system according to the temperature difference fitting curve. By the simulation analysis method, risk point evaluation is provided for the early-stage thermal management design, and the system revision design and project development cycle time is reduced.
Description
Technical Field
The invention relates to the technical field of modeling simulation, in particular to a thermal simulation analysis method, equipment and a storage medium for a liquid cooling energy storage system.
Background
The energy storage technology is an important link of a smart grid and is one of support technologies of the smart grid technology, a battery module is formed by a plurality of battery cores, a battery cluster is formed by the battery module, a battery stack is formed by the battery cluster, and the battery stack finally forms the whole liquid-cooled energy storage system. The basic unit of the liquid cooling energy storage system is an electric core, the temperature difference of the electric core in the liquid cooling energy storage system has larger influence on the system service life, SOH and system balance, and in order to reduce the temperature difference of the electric core of the system and prolong the service time of the system, the temperature difference of different electric cores in the liquid cooling energy storage system needs to be controlled within a reasonable range.
Along with the development of the liquid cooling energy storage system towards the direction of high multiplying power and high energy density, the traditional air cooling heat dissipation does not meet the heat dissipation requirement of a system battery core, and an efficient liquid cooling heat dissipation mode needs to be adopted, so that the liquid cooling heat dissipation has the advantages of low system self-power consumption, low noise, good battery core temperature uniformity, large battery core heat productivity and high temperature uniformity requirement, and the liquid cooling heat dissipation mode can be gradually and widely applied to the heat management of the liquid cooling energy storage system. At present, the heat management risk points of the liquid cooling type liquid cooling energy storage system are mostly stopped at a testing stage, and the system is already shaped, so that the improved space of the system is small, and the temperature control testing period of the system is long.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides a thermal simulation analysis method, equipment and a storage medium for a liquid cooling energy storage system, which are used for providing risk point evaluation for early thermal management design and reducing the time of system revision design and project development cycle.
According to the embodiment of the first aspect of the invention, the thermal simulation analysis method for the liquid cooling energy storage system is applied to the liquid cooling energy storage system, and comprises the following steps:
establishing a liquid cooling plate simulation model according to preset electric core data;
establishing a battery cluster flow simulation model according to the liquid cooling plate simulation model and preset battery cluster data;
establishing a battery stack flow simulation model according to the battery cluster flow simulation model and preset battery stack data;
calculating to obtain flow distribution differences of a plurality of pipeline inlets in the liquid cooling energy storage system according to the battery cluster flow simulation model and the battery stack flow simulation model;
calculating to obtain a plurality of highest temperatures which correspond to the flow distribution differences one by one according to the liquid cooling plate simulation model;
obtaining a temperature difference fitting curve according to the plurality of flow distribution differences and the corresponding plurality of maximum temperatures;
and calculating to obtain the maximum temperature difference of the cell stack in the liquid cooling energy storage system according to the temperature difference fitting curve.
According to one or more technical schemes provided in the embodiment of the invention, the method has at least the following beneficial effects: the method comprises the steps of establishing a liquid cooling plate simulation model through preset cell data, establishing a battery cluster flow simulation model according to the liquid cooling plate simulation model and preset battery cluster data, and establishing a battery stack flow simulation model according to the battery cluster flow simulation model and the preset battery stack data; calculating to obtain flow distribution differences of a plurality of pipeline inlets in the liquid cooling energy storage system according to the battery cluster flow simulation model and the battery stack flow simulation model; calculating to obtain a plurality of highest temperatures which correspond to a plurality of flow distribution differences one by one; obtaining a temperature difference fitting curve according to the plurality of flow distribution differences and the corresponding plurality of maximum temperatures; calculating to obtain the maximum temperature difference of the cell stack in the liquid cooling energy storage system according to the temperature difference fitting curve; and evaluating whether the design of the liquid cooling energy storage system reaches the standard or not through the maximum temperature difference of the cell stack in the liquid cooling energy storage system. Through the arrangement, risk point evaluation is provided for the early-stage heat management design, the system revision design and project development cycle time is reduced, and meanwhile, the reliability of the heat management of the liquid cooling energy storage system is improved.
According to some embodiments of the present invention, the cell data includes a cell heating parameter, a cell distribution parameter, and a cell demand parameter, and the establishing of the liquid cooling plate simulation model according to the preset cell data includes:
obtaining a preliminary liquid cooling plate simulation model according to the electric core heating parameters, the electric core distribution parameters and the electric core demand parameters;
and continuously optimizing the flow size and the flow channel form of the primary liquid cooling plate simulation model until the primary liquid cooling plate simulation model meets the preset liquid cooling condition to obtain a liquid cooling plate simulation model.
According to some embodiments of the invention, the liquid cooling conditions comprise at least one of:
the maximum simulation temperature of the battery module in the primary liquid cooling plate simulation model is less than or equal to the preset maximum temperature value of the battery module;
the maximum simulation temperature difference of the battery module in the preliminary liquid cooling plate simulation model is smaller than or equal to a preset maximum temperature difference value of the battery module;
the simulation pressure drop value of the inlet and the outlet of the liquid cooling plate in the preliminary liquid cooling plate simulation model is smaller than the preset pressure drop value of the inlet and the outlet of the liquid cooling plate;
and the simulation temperature difference of the inlet and the outlet of the liquid cooling plate in the preliminary liquid cooling plate simulation model is less than or equal to the preset temperature difference of the inlet and the outlet of the liquid cooling plate.
According to some embodiments of the present invention, the battery cluster data includes battery module distribution parameters and battery module demand parameters, and the establishing of the battery cluster flow simulation model according to the liquid cooling plate simulation model and the preset battery cluster data includes:
obtaining the pipe diameter data of the inlet and the outlet of the plurality of battery modules according to the liquid cooling plate simulation model;
obtaining a preliminary battery cluster flow simulation model according to the battery module distribution parameters, the battery module demand parameters and the pipe diameter data of the inlet and the outlet of the plurality of battery modules;
and continuously optimizing the size and the type of the flow pipe of the primary battery cluster flow simulation model until the primary battery cluster flow simulation model meets the preset battery cluster conditions to obtain a battery cluster flow simulation model.
According to some embodiments of the invention, the battery cluster condition comprises at least one of:
the inlet flow distribution difference simulated by each battery module in the preliminary battery cluster flow simulation model is smaller than or equal to the inlet flow distribution difference preset by each battery module;
and the simulation pressure drop value of the inlet and the outlet of the battery cluster in the preliminary battery cluster flow simulation model is smaller than the preset pressure drop value of the inlet and the outlet of the battery cluster.
According to some embodiments of the present invention, the cell stack data includes a cell cluster distribution parameter and a cell cluster demand parameter, and the establishing a cell stack flow simulation model according to the cell cluster flow simulation model and preset cell stack data includes:
obtaining a plurality of battery cluster inlet and outlet pipe diameter data according to the battery cluster flow simulation model;
obtaining a primary cell stack flow simulation model according to the cell cluster distribution parameters, the cell cluster demand parameters and the pipe diameter data of the inlet and the outlet of the plurality of cell clusters;
and continuously optimizing the size and the type of the flow pipe of the primary cell stack flow simulation model until the primary cell stack flow simulation model meets the preset cell stack conditions to obtain the cell stack flow simulation model.
According to some embodiments of the invention, the stack condition comprises at least one of:
the inlet flow distribution difference of each battery cluster simulation in the preliminary battery stack flow simulation model is smaller than or equal to the inlet flow distribution difference preset by each battery cluster;
and the simulation pressure drop value of the inlet and the outlet of the cell stack in the preliminary cell stack flow simulation model is smaller than the preset pressure drop value of the inlet and the outlet of the cell stack.
According to some embodiments of the present invention, the calculating the maximum temperature difference of the cell stack in the liquid-cooled energy storage system according to the temperature difference fitting curve includes:
calculating to obtain a first difference value of inlet flow distribution among the battery modules in each battery cluster in the liquid-cooled energy storage system according to the battery cluster flow simulation model;
calculating to obtain a second difference value of inlet flow distribution among the battery clusters in the battery stack according to the battery stack flow simulation model;
calculating to obtain a third difference value of inlet flow distribution among the plurality of battery modules in the battery stack according to the first difference value and the second difference value;
and calculating to obtain the maximum temperature difference corresponding to the third difference value according to the temperature difference fitting curve.
According to the second aspect of the invention, the thermal simulation analysis device of the liquid cooling energy storage system comprises: the thermal simulation analysis method for the liquid cooling energy storage system comprises a memory, a processor and a computer program which is stored on the memory and can be run on the processor, wherein when the processor executes the computer program, the thermal simulation analysis method for the liquid cooling energy storage system is realized.
According to a third aspect of the present invention, there is provided a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the method for thermal simulation analysis of a liquid-cooled energy storage system according to the first aspect.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the example serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a schematic flow chart of a thermal simulation analysis method of a liquid cooling energy storage system according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a thermal simulation analysis method of a liquid cooling energy storage system according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of a thermal simulation analysis method of a liquid cooling energy storage system according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart of a simulation model for designing a liquid cooling plate according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a battery module according to an embodiment of the present invention;
fig. 6 is a schematic flow chart of a thermal simulation analysis method of a liquid cooling energy storage system according to an embodiment of the present invention;
fig. 7 is a schematic flow chart of a thermal simulation analysis method of a liquid cooling energy storage system according to an embodiment of the present invention;
fig. 8 is a schematic flow chart of a design battery cluster flow simulation model according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of a cell cluster manifold according to an embodiment of the present invention;
fig. 10 is a schematic flow chart of a thermal simulation analysis method of a liquid cooling energy storage system according to an embodiment of the present invention;
fig. 11 is a schematic flowchart of a thermal simulation analysis method of a liquid-cooling energy storage system according to an embodiment of the present invention.
Fig. 12 is a schematic flow chart of a design battery cluster flow simulation model according to an embodiment of the present invention;
FIG. 13 is a schematic diagram of the construction of a cell stack manifold according to an embodiment of the present invention;
fig. 14 is a schematic flow chart of a thermal simulation analysis method of a liquid-cooled energy storage system according to an embodiment of the present invention;
fig. 15 is a schematic structural diagram of a liquid-cooled energy storage system according to an embodiment of the invention;
FIG. 16 is a schematic diagram of a temperature difference fit curve provided by an embodiment of the present invention.
Reference numerals:
cell positive pole 110, cell negative pole 120, cell 130, liquid cooling plate 140, battery module external joint 150, battery cluster tributary pipe 160, battery cluster collecting pipe 170, battery pile collecting pipe 180, battery cluster 190.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It should be noted that although functional blocks are partitioned in a schematic diagram of an apparatus and a logical order is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the partitioning of blocks in the apparatus or the order in the flowchart. The terms first, second and the like in the description and in the claims, and the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The energy storage technology is an important link of a smart grid and is one of support technologies of the smart grid technology, a battery module is formed by a plurality of battery cores, a battery cluster is formed by the battery module, a battery stack is formed by the battery cluster, and the battery stack finally forms the whole liquid-cooled energy storage system. The basic unit of the liquid cooling energy storage system is an electric core, the temperature difference of the electric core in the liquid cooling energy storage system has larger influence on the system service life, SOH and system balance, and in order to reduce the temperature difference of the electric core of the system and prolong the service time of the system, the temperature difference of different electric cores in the liquid cooling energy storage system needs to be controlled within a reasonable range.
Along with the development of the liquid cooling energy storage system towards the direction of high multiplying power and high energy density, the traditional air cooling heat dissipation does not meet the heat dissipation requirement of a system battery core, and an efficient liquid cooling heat dissipation mode needs to be adopted, wherein the liquid cooling heat dissipation mode has the advantages of low system self-power consumption, low noise, good battery core temperature uniformity, large battery core heat productivity and high temperature uniformity requirement, and the liquid cooling heat dissipation mode can be gradually widely applied to the heat management of the liquid cooling energy storage system. At present, the heat management risk points of the liquid cooling type liquid cooling energy storage system are mostly stopped at a testing stage, and the system is already shaped, so that the improved space of the system is small, and the temperature control testing period of the system is long.
Based on this, the embodiment of the invention provides a thermal simulation analysis method, equipment and a storage medium for a liquid cooling energy storage system, which provide risk point assessment for early thermal management design and reduce the system revision design and project development cycle time.
It should be noted that, referring to fig. 15, a plurality of battery cells 130 form a battery module, the battery module forms a battery cluster 190, the battery cluster 190 forms a battery stack, and the battery stack finally forms the whole liquid-cooled energy storage system.
The embodiments of the present invention will be further explained with reference to the drawings.
An embodiment of the first aspect of the present invention specifically provides a thermal simulation analysis method for a liquid cooling energy storage system, as shown in fig. 1. The thermal simulation analysis method of the liquid cooling energy storage system is applied to the liquid cooling energy storage system, and comprises the following steps:
step S100, establishing a liquid cooling plate simulation model according to preset cell data;
it should be noted that, the electrical core data are used for characterizing the performance parameters of the electrical core 130 and the distribution parameters of the electrical core 130, exemplarily, refer to fig. 4, the preset electrical core data include the number of the electrical core 130 in the battery module and the arrangement form of the electrical core 130, the temperature rise requirement of the inlet and outlet cooling liquid, the maximum temperature rise requirement of the electrical core 130, and the heating parameters of the electrical core 130, wherein the heating parameters of the electrical core 130 include the material property of the electrical core 130, the thermal conductivity, the density, the specific heat capacity, and the heating power of the electrical core 130 obtained according to the experimental test result.
Step S200, establishing a battery cluster flow simulation model according to the liquid cooling plate simulation model and preset battery cluster data;
it should be noted that the battery cluster data is used to represent battery module distribution parameters and battery module demand parameters in the battery cluster 190, for example, referring to fig. 8 and 9, the preset battery cluster data includes the number of battery modules in the battery cluster 190, the arrangement of the battery modules, and the requirement for the inlet flow rate of the battery modules, in step S200, the inlet and outlet pipe diameters of the battery modules may be obtained according to the liquid-cooled plate simulation model, the type of the battery cluster manifold 170 and the size of the battery cluster manifold 170 are obtained according to the number of battery modules in the battery cluster 190, the arrangement of the battery modules, the inlet and outlet pipe diameters of the battery modules, and the requirement for the inlet flow rate of the battery modules, and the battery cluster flow rate simulation model is established accordingly.
Step S300, establishing a battery pile flow simulation model according to the battery pile flow simulation model and preset battery pile data;
it should be noted that the stack data is used to represent the distribution parameters of the cell clusters 190 in the stack and the demand parameters of the cell clusters 190, for example, referring to fig. 12 and 13, the preset stack data includes the number of the cell clusters 190 in the stack, the arrangement mode of the cell clusters 190, and the inlet flow requirements of the cell clusters 190, in step S300, the inlet pipe diameter data of the cell clusters 190 may be obtained according to the stack flow simulation model, the types and sizes of the cell stack collecting pipes 180 may be obtained according to the number of the cell clusters 190 in the stack, the arrangement mode of the cell clusters 190, the inlet pipe diameter data of the cell clusters 190, and the inlet flow requirements of the cell clusters 190, and the stack flow simulation model is established accordingly.
Step S400, calculating to obtain the flow distribution difference of a plurality of pipeline inlets in the liquid cooling energy storage system according to the battery cluster flow simulation model and the battery stack flow simulation model;
it should be noted that the flow distribution difference of the inlets of the plurality of pipes includes the inlet flow distribution difference between each battery module in the battery cluster 190 and the inlet flow distribution difference between each battery cluster 190 in the battery stack. The flow distribution difference represents a difference in inlet flow distribution between different battery modules, a difference in inlet flow distribution between different battery clusters 190.
Step S500, calculating to obtain a plurality of highest temperatures corresponding to flow distribution differences one by one according to the simulation model of the liquid cooling plate;
step S600, obtaining a temperature difference fitting curve according to a plurality of flow distribution differences and a plurality of corresponding maximum temperatures;
it should be noted that the temperature difference fitting curve is used to represent the inlet flow difference values between different battery modules in the battery stack 190, the inlet flow difference values between different battery stacks 190 in the battery stack, and the maximum temperature variation value Δ T corresponding to the inlet flow difference values between different battery modules in the battery stack. Specifically, through the steps S400 and S500, in the battery module simulation stage, the maximum temperatures corresponding to the inlet flows of the inlets of the different pipelines need to be simulated and analyzed to obtain the maximum temperature variation value Δ T corresponding to the difference value α of the different inlet flows, and according to the difference of the inlet flow distribution between the different battery modules in the battery stack 190 and the difference of the inlet flow distribution between the different battery modules in the battery stack 190, the difference of the inlet flow distribution between the different battery modules in the battery stack is calculated to obtain the difference of the inlet flow distribution between the different battery modules in the battery stack, and a temperature difference fitting curve is established accordingly.
It should be noted that, according to the battery cluster flow simulation model, the difference of the inlet flow distribution among the battery modules in the battery cluster 190 can be preliminarily determined, and the first inlet flow distribution difference data of the inlet flow distribution among the battery modules in the battery cluster 190 is obtained through the following calculation formula:
α i1 ＝(Q i1 -Q i2 )/Q i2 ，
wherein alpha is i1 First inlet flow distribution difference data, Q, representing inlet flow distribution among battery modules within the same battery cluster 190 i1 Represents the inlet flow, Q, of a battery module within the same battery cluster 190 i2 Indicates the inlet flow of another battery module in the same battery cluster 190, wherein Q i1 >Q i2 。
In addition, according to the stack flow simulation model, the difference of the inlet flow distribution among the cell clusters 190 in the stack can be preliminarily judged, and the second inlet flow distribution difference data of the inlet flow distribution among the cell clusters 190 in the stack can be obtained through the following calculation formula:
α i2 ＝(Q i3 -Q i4 )/Q i4 ，
wherein alpha is i2 Second inlet flow distribution difference data, Q, representing inlet flow distribution among the various cell clusters 190 within the stack i3 Represents the inlet flow, Q, of a cell cluster 190 within the stack i4 Represents the inlet flow, Q, of another cell cluster 190 in the stack i3 >Q i4 . According to each battery in the designed battery cluster 190The first inlet flow distribution difference data of inlet flow distribution between modules and the second inlet flow distribution difference data of inlet flow distribution on each cell cluster 190 between cell stacks can obtain the third inlet flow distribution difference data corresponding to inlet flow distribution difference between all the cell modules in the whole cell stack through the following calculation formula:
α i3 ＝α i1 +α i2 ，
wherein alpha is i3 Third inlet flow distribution difference data representing inlet flow distribution among the respective battery modules in the stack.
And step S700, calculating to obtain the maximum temperature difference of the cell stack in the liquid cooling energy storage system according to the temperature difference fitting curve.
It should be noted that the maximum inlet flow distribution difference data of the inlet flow distribution among the battery modules in the battery cluster 190, that is, the first difference value, is obtained by comparing the data of the plurality of first inlet flow distribution difference data, and the calculation formula of the first difference value is as follows:
α 1 ＝(Q 1 -Q 2 )/Q 2 ，
wherein alpha is 1 A first value of difference, Q, representing the inlet flow distribution between battery modules within the same battery cluster 190 1 Represents the maximum inlet flow, Q, of the battery modules in the same battery cluster 190 2 Indicating a minimum flow rate of the battery module inlet within the same battery cluster 190.
In addition, the multiple second inlet flow distribution difference data are compared to obtain the maximum inlet flow distribution difference data of the inlet flow distribution between the cell clusters 190 in the cell stack, that is, a second difference value, and a calculation formula of the second difference value is as follows:
α 2 ＝(Q 3 -Q 4 )/Q 4 ，
wherein alpha is 2 A second difference, Q, representing the inlet flow distribution between the clusters 190 in the stack 3 Represents the maximum inlet flow, Q, of the cell cluster 190 in the stack 4 Indicating the minimum inlet flow of the cell cluster 190 within the stack. According to the designed battery clusterThe first difference value of inlet flow distribution between the 190 interior battery modules and the second difference value of inlet flow distribution between the 190 interior battery cluster of battery stack can obtain the biggest difference data of inlet flow distribution difference between each battery module in the whole battery stack through the following computational formula, and this difference data is expressed with the third difference value:
α 3 ＝α 1 +α 2 ，
wherein alpha is 3 Representing a third difference value. Obtaining the maximum temperature difference of the cell stack in a temperature difference fitting curve according to the third difference value; in addition, according to the difference of the inlet flow distribution of each battery module, whether the temperature difference and the highest temperature in the whole battery stack meet the preset requirements can be obtained. In this embodiment, the maximum temperature difference between the battery modules is required to be less than or equal to 3 ℃, and the maximum temperature of the battery module is required to be less than or equal to the value obtained by adding 10 ℃ to the ambient temperature; in other embodiments, the maximum temperature difference between the battery modules and the required value of the maximum temperature of the battery modules may also be set to other values, and are not limited to the embodiments of the present invention.
The method comprises the steps of establishing a liquid cooling plate simulation model through preset cell data, establishing a battery cluster flow simulation model according to the liquid cooling plate simulation model and preset battery cluster data, and establishing a battery stack flow simulation model according to the battery cluster flow simulation model and the preset battery stack data; calculating to obtain flow distribution differences of a plurality of pipeline inlets in the liquid cooling energy storage system according to the battery cluster flow simulation model and the battery stack flow simulation model; calculating to obtain a plurality of highest temperatures which correspond to a plurality of flow distribution differences one by one; obtaining a temperature difference fitting curve according to the plurality of flow distribution differences and the corresponding plurality of maximum temperatures; calculating to obtain the maximum temperature difference of the cell stack in the liquid cooling energy storage system according to the temperature difference fitting curve; and evaluating whether the design of the liquid cooling energy storage system reaches the standard or not through the maximum temperature difference of the cell stack in the liquid cooling energy storage system. Through the arrangement, risk point evaluation is provided for the early-stage heat management design, the system revision design and project development cycle time is reduced, and meanwhile, the reliability of the heat management of the liquid cooling energy storage system is improved.
In the related technology, the heat management risk points of the liquid cooling type liquid cooling energy storage system are mostly stopped at the testing stage, and because the system is shaped, the system is small in improvement space, the system is long in temperature control testing period, and labor and material resources are consumed for version change. Therefore, the method provides risk point evaluation and improvement scheme for the early-stage heat management design through the liquid cooling energy storage system thermal simulation analysis method, reduces the system revision design and project development cycle time, saves the cost, and improves the reliability of the system heat management.
It should be noted that, referring to fig. 5, fig. 9 and fig. 13, the thermal management simulation of the liquid-cooled energy storage system of the present invention is mainly divided into three layers: the design of the liquid cooling plate 140 of the bottom battery module is to establish a liquid cooling plate simulation model, the design of the middle battery cluster collecting pipe 170 is to establish a battery cluster flow simulation model, and the design of the top battery stack collecting pipe 180 is to establish a battery stack flow simulation model.
In this embodiment, in the battery module simulation stage, the maximum temperature variation value of the battery module corresponding to the inlet flow between different battery modules needs to be simulated and analyzed to obtain the maximum temperature variation value Δ T of the battery module corresponding to different flow difference values α, and the maximum inlet flow difference value α of the battery module in the battery stack in the liquid-cooled energy storage system is determined according to the maximum inlet flow difference value α of the battery module 3 Obtaining the maximum temperature difference of the cell stack in the liquid cooling energy storage system, namely the maximum temperature change value delta T 3 . And evaluating whether the design of the liquid cooling energy storage system reaches the standard or not through the maximum temperature difference of the cell stack in the liquid cooling energy storage system. Through the arrangement, risk point evaluation is provided for the early-stage heat management design, the system version changing design and project development cycle time are reduced, and the reliability of the heat management of the liquid cooling energy storage system is improved.
Referring to fig. 2, 4 and 5, it can be understood that the cell data includes a cell 130 heating parameter, a cell 130 distribution parameter and a cell 130 demand parameter, and step S100 includes, but is not limited to, the following steps:
step S110, obtaining a preliminary liquid cooling plate simulation model according to the heating parameters of the electric core 130, the distribution parameters of the electric core 130 and the demand parameters of the electric core 130;
and step S120, continuously optimizing the flow size and the flow channel form of the primary liquid cooling plate simulation model until the primary liquid cooling plate simulation model meets preset liquid cooling conditions, so as to obtain a liquid cooling plate simulation model.
It should be noted that, the heat generation parameters of the battery cell 130 include material properties, thermal conductivity, density, specific heat capacity of the battery cell 130 and the heat generation power of the battery cell 130, and the distribution parameters of the battery cell 130 include: the quantity of electric core 130 and the form of arranging of electric core 130 in the battery module, electric core 130 demand parameter includes: the inlet and outlet cooling liquid temperature rise requirement and the maximum temperature rise requirement of the battery cell 130.
It should be noted that, according to the number of the battery cells 130 in the battery module and the arrangement form of the battery cells 130, the module weight can be obtained, and structural parameters such as the overall dimension, the thickness and the like of the liquid cooling plate 140 are established accordingly; the total heat productivity of the battery module is obtained through the heating parameters of the battery core 130, and the inlet flow of the battery module is constructed according to the total heat productivity of the battery module, the temperature rise requirement of the inlet and outlet cooling liquid and the maximum temperature rise requirement of the battery core 130. According to the obtained primary liquid cooling plate simulation model, according to the input flow data, whether the highest simulation temperature of the battery module, the maximum simulation temperature difference of the battery module, the simulation pressure drop value of the inlet and the outlet of the liquid cooling plate 140 and the simulation temperature difference of the inlet and the outlet of the liquid cooling plate 140 meet the preset liquid cooling condition or not is obtained through simulation, and if the maximum simulation temperature of the battery module, the primary liquid cooling plate simulation model meeting the liquid cooling condition is used as the liquid cooling plate simulation model; and if the liquid cooling condition is not met, polling and optimizing the primary liquid cooling plate simulation model until the primary liquid cooling plate simulation model meets the preset liquid cooling condition, and taking the primary liquid cooling plate simulation model meeting the liquid cooling condition as the liquid cooling plate simulation model.
It should be noted that, when the preliminary liquid cooling plate simulation model does not satisfy the liquid cooling condition, the maximum simulation temperature of the battery module, the maximum simulation temperature difference of the battery module, the simulation pressure drop value of the inlet and the outlet of the liquid cooling plate 140, and the simulation temperature difference of the inlet and the outlet of the liquid cooling plate 140, which are obtained according to the preliminary liquid cooling plate simulation model, are compared with the liquid cooling condition, so that a first optimization parameter can be obtained, and the flow passage form and the flow volume of the liquid cooling plate 140 are optimized according to the first optimization parameter until the preliminary liquid cooling plate simulation model satisfies the preset liquid cooling condition.
It should be noted that the flow channel form of the liquid cooling plate simulation model is the flow channel form of the liquid cooling plate 140, and the flow channel form of the liquid cooling plate 140 includes a series connection type and a parallel connection type.
It should be noted that, referring to fig. 5, the battery module simulation model formed by the liquid cooling plate simulation model includes a battery cell positive electrode 110, a battery cell negative electrode 120, a battery cell 130, a liquid cooling plate 140, and a battery module external joint 150.
Referring to fig. 3, 4 and 5, it can be understood that the liquid cooling condition in step S102 includes, but is not limited to, at least one of the following steps:
step S121, the maximum simulation temperature of the battery module in the primary liquid cooling plate simulation model is less than or equal to the preset maximum temperature value of the battery module;
step S122, the maximum simulation temperature difference of the battery module in the preliminary liquid cooling plate simulation model is smaller than or equal to the preset maximum temperature difference value of the battery module;
step S123, the simulation pressure drop value of the inlet and the outlet of the liquid cooling plate 140 in the preliminary liquid cooling plate simulation model is smaller than the preset pressure drop value of the inlet and the outlet of the liquid cooling plate 140;
step S124, the simulation temperature difference of the inlet and the outlet of the liquid cooling plate 140 in the preliminary liquid cooling plate simulation model is smaller than or equal to the preset temperature difference of the inlet and the outlet of the liquid cooling plate 140.
For example, the preset maximum temperature value of the battery module = ambient temperature +10 ℃, and if the ambient temperature is 25 ℃, the maximum simulation temperature of the battery module needs to be less than or equal to 35 ℃; the maximum temperature difference value of the battery module is preset = the maximum temperature of the battery module-the minimum temperature of the battery module =3 ℃, and the maximum simulation temperature difference of the battery module is required to be less than or equal to 3 ℃; the preset pressure drop value of the inlet and outlet of the liquid cooling plate 140 is 20KPa, and the simulation pressure drop value of the inlet and outlet of the liquid cooling plate 140 is required to be less than 20KPa; the preset temperature difference value of the inlet and the outlet of the liquid cooling plate 140 = the outlet temperature of the liquid cooling plate 140-the inlet temperature of the liquid cooling plate 140 =2 ℃, and at this time, the simulation temperature difference of the inlet and the outlet of the liquid cooling plate 140 is required to be less than or equal to 2 ℃. And if the simulation result does not satisfy the liquid cooling condition, polling and optimizing the primary liquid cooling plate simulation model until the primary liquid cooling plate simulation model meets the preset liquid cooling condition, and performing battery module test verification by taking the primary liquid cooling plate simulation model meeting the liquid cooling condition as the liquid cooling plate simulation model.
Referring to fig. 6, 8 and 9, it can be understood that the battery cluster data includes a battery module distribution parameter and a battery module demand parameter, and the step S200 includes, but is not limited to, the following steps:
step S210, obtaining pipe diameter data of an inlet and an outlet of a plurality of battery modules according to the liquid cooling plate simulation model;
step S220, obtaining a primary battery cluster flow simulation model according to the distribution parameters of the battery modules, the demand parameters of the battery modules and the pipe diameter data of the inlet and the outlet of the plurality of battery modules;
step S230, continuously optimizing the size and type of the flow pipe of the primary battery cluster flow simulation model until the primary battery cluster flow simulation model meets the preset battery cluster conditions to obtain a battery cluster flow simulation model.
It should be noted that the distribution parameters of the battery modules include the number of the battery modules in the battery cluster 190 and the arrangement mode of the battery modules, and the demand parameters of the battery modules include the inlet flow requirements of the battery modules. The type of the battery cluster collecting pipe 170 and the size of the battery cluster collecting pipe 170 are obtained according to the number of the battery modules in the battery cluster 190, the arrangement mode of the battery modules, the inlet and outlet pipe diameter data of the plurality of battery modules and the inlet flow requirement of the battery modules, and a battery cluster flow simulation model is established according to the type of the battery cluster collecting pipe 170 and the size of the battery cluster collecting pipe 170.
It should be noted that it is necessary to determine whether the inlet flow distribution difference of each battery module simulation and the inlet and outlet simulation pressure drop values of the battery cluster 190 obtained by simulation satisfy the preset battery cluster conditions according to the preliminary battery cluster flow simulation model, and if so, the preliminary battery cluster flow simulation model that satisfies the battery cluster conditions is used as the battery cluster flow simulation model; and if the battery cluster condition is not met, polling and optimizing the primary battery cluster flow simulation model until the primary battery cluster flow simulation model meets the battery cluster condition, and taking the primary battery cluster flow simulation model meeting the battery cluster condition as the battery cluster flow simulation model.
It should be noted that, when the preliminary cell cluster flow simulation model does not satisfy the cell cluster condition, the inlet flow distribution difference of each cell module simulation obtained according to the preliminary cell cluster flow simulation model simulation and the inlet and outlet simulation pressure drop values of the cell cluster 190 are compared with the cell cluster condition to obtain a second optimization parameter, and according to the second optimization parameter, the size of the cell cluster collecting pipe 170 and the type of the cell cluster collecting pipe 170 are optimized until the preliminary cell cluster flow simulation model satisfies the preset cell cluster condition.
It should be noted that the types of cell cluster headers 170 may be circular, square, polygonal, and triangular, and the flow distributions corresponding to the cell cluster headers 170 with different shapes also have differences.
Referring to fig. 7, 8 and 9, it can be understood that the battery cluster condition in step S230 includes, but is not limited to, at least one of the following steps:
step S231, inlet flow distribution differences simulated by each battery module in the preliminary battery cluster flow simulation model are smaller than or equal to inlet flow distribution differences preset by each battery module;
step S232, the simulation pressure drop value of the inlet and the outlet of the battery cluster 190 in the preliminary battery cluster flow simulation model is smaller than the preset pressure drop value of the inlet and the outlet of the battery cluster 190.
For example, if the difference in the inlet flow distribution preset for each battery module is 5%, the inlet flow distribution difference simulated for each battery module needs to be less than or equal to 5%; the preset pressure drop value of the inlet and outlet of the battery pack 190 is 25KPa, and the simulation pressure drop value of the inlet and outlet of the battery pack 190 is required to be less than 25KPa. If the simulation result of the preliminary battery cluster flow simulation model does not meet the battery cluster condition, the preliminary battery cluster flow simulation model needs to be polled and optimized until the preliminary battery cluster flow simulation model meets the battery cluster condition, and the preliminary battery cluster flow simulation model meeting the battery cluster condition is used as the battery cluster flow simulation model for battery cluster 190 test verification.
Referring to fig. 9, the size of battery cluster branch pipe 160 is smaller than the size of battery cluster header pipe 170, but the volume of battery cluster branch pipe 160 may be larger than the area of battery cluster header pipe 170 or smaller than the area of battery cluster header pipe 170.
Referring to fig. 10, 12 and 13, it can be understood that the stack data includes a cell cluster 190 distribution parameter and a cell cluster 190 demand parameter, and step S300 includes, but is not limited to, the following steps:
step S310, obtaining pipe diameter data of an inlet and an outlet of a plurality of battery clusters 190 according to a battery cluster flow simulation model;
step S320, obtaining a primary cell stack flow simulation model according to the distribution parameters of the cell clusters 190, the demand parameters of the cell clusters 190 and the pipe diameter data of the inlets and the outlets of the plurality of cell clusters 190;
and step S330, continuously optimizing the size and the type of the flow pipe of the primary cell stack flow simulation model until the primary cell stack flow simulation model meets the preset cell stack conditions to obtain the cell stack flow simulation model.
It should be noted that the distribution parameters of the battery clusters 190 include the number of the battery clusters 190 in the battery stack and the arrangement mode of the battery clusters 190, and the demand parameters of the battery clusters 190 include the inlet flow requirement of the battery clusters 190; the type and size of the cell stack collecting pipe 180 are obtained according to the number of the cell clusters 190 in the cell stack, the arrangement mode of the cell clusters 190, the pipe diameter data of the inlet and the outlet of the plurality of cell clusters 190 and the inlet flow requirement of each cell cluster 190, and a cell stack flow simulation model is established according to the type and size.
It should be noted that it is necessary to determine whether the inlet flow distribution difference and the stack inlet/outlet simulation pressure drop values of the simulation of each cell cluster 190 obtained by the simulation satisfy the preset stack conditions according to the preliminary stack flow simulation model, and if so, the preliminary stack flow simulation model that satisfies the stack conditions is used as the stack flow simulation model; and if the condition of the cell stack is not met, polling and optimizing the primary cell stack flow simulation model until the primary cell stack flow simulation model meets the condition of the cell stack, and taking the primary cell stack flow simulation model meeting the condition of the cell stack as the cell stack flow simulation model.
It should be noted that, when the preliminary cell stack flow simulation model does not satisfy the cell stack conditions, the inlet flow distribution difference of the simulation of each cell cluster 190 and the cell stack inlet and outlet simulation pressure drop values obtained according to the preliminary cell stack flow simulation model are compared with the cell stack conditions to obtain third optimization parameters, and according to the third optimization parameters, the size of the cell stack manifold 180 and the type of the cell stack manifold 180 are optimized until the preliminary cell stack flow simulation model satisfies the preset cell stack conditions.
Referring to fig. 11, 12 and 13, it can be understood that the stack condition in step S330 includes, but is not limited to, at least one of the following steps:
step S331, the inlet flow distribution difference simulated by each cell cluster 190 in the preliminary cell stack flow simulation model is less than or equal to the inlet flow distribution difference preset by each cell cluster 190;
in step S332, the simulation pressure drop value of the inlet and the outlet of the cell stack in the preliminary cell stack flow simulation model is smaller than the preset pressure drop value of the inlet and the outlet of the cell stack.
For example, the difference of the inlet flow distribution preset for each battery cluster 190 is 15%, and at this time, the difference of the inlet flow distribution simulated for each battery cluster 190 is required to be less than or equal to 15%; the preset pressure drop value of the inlet and the outlet of the cell stack is 30KPa, and the simulation pressure drop value of the inlet and the outlet of the cell stack is required to be less than 30KPa. And if the simulation result of the primary cell stack flow simulation model does not meet the cell stack conditions, polling and optimizing the primary cell stack flow simulation model until the primary cell stack flow simulation model meets the cell stack conditions, and taking the primary cell stack flow simulation model meeting the cell stack conditions as the cell stack flow simulation model for cell stack test verification.
Referring to fig. 13, the size of cell stack manifold 180 is larger than the size of cell cluster manifold 170, but the volume of cell stack manifold 180 may be larger than the area of cell cluster manifold 170 or smaller than the area of cell cluster manifold 170.
Referring to fig. 14, it can be understood that step S700, includes but is not limited to the following steps:
step S710, calculating to obtain a first difference value of inlet flow distribution among the battery modules in each battery cluster 190 in the liquid-cooled energy storage system according to the battery cluster flow simulation model;
step S720, calculating to obtain a second difference value of inlet flow distribution among the battery clusters 190 in the battery stack according to the battery stack flow simulation model;
step 730, calculating a third difference value of inlet flow distribution among a plurality of battery modules in the battery stack according to the first difference value and the second difference value;
and step S740, calculating to obtain the maximum temperature difference corresponding to the third difference value according to the temperature difference fitting curve.
It should be noted that, the calculation formula of the first difference value of the inlet flow distribution on each battery module in the battery cluster 190 is as follows:
α 1 ＝(Q 1 -Q 2 )/Q 2 ，
wherein alpha is 1 A first difference, Q, representing the inlet flow distribution among the battery modules in the same battery cluster 190 1 Represents the maximum inlet flow, Q, of the battery modules in the same battery cluster 190 2 Indicating a minimum flow rate of the battery module inlet within the same battery cluster 190. The second difference in inlet flow distribution between the cell clusters 190 in the stack is calculated as follows:
α 2 ＝(Q 3 -Q 4 )/Q 4 ，
wherein alpha is 2 A second difference, Q, representing the inlet flow distribution between the clusters 190 in the stack 3 Represents the maximum inlet flow, Q, of the cell cluster 190 in the stack 4 Indicating the minimum inlet flow of the cell cluster 190 within the stack. The calculation formula of the third difference value of the inlet flow distribution among the battery modules in the battery stack is as follows:
α 3 ＝α 1 +α 2 ，
wherein alpha is 3 And the third difference value represents the inlet flow distribution of each battery module in the battery stack, namely the maximum difference data of the inlet flow distribution difference among the battery modules in the whole battery stack. According to the difference value alpha of the maximum inlet flow among the battery modules in the battery stack in the liquid cooling energy storage system 3 And obtaining the maximum temperature difference of the cell stack in the liquid cooling energy storage system, namely the change value delta T of the maximum temperature by using the temperature difference fitting curve 3 And evaluating whether the design of the liquid cooling energy storage system reaches the standard or not through the maximum temperature difference of the cell stack in the liquid cooling energy storage system. Through the arrangement, risk point assessment is provided for early-stage thermal management design, and system change is reducedAnd the plate design and project development cycle time are prolonged, and the reliability of the heat management of the liquid cooling energy storage system is improved.
In addition, an embodiment of a second aspect of the present invention further provides a thermal simulation analysis device for a liquid cooling energy storage system, where the thermal simulation analysis device for a liquid cooling energy storage system includes: a memory, a processor, and a computer program stored on the memory and executable on the processor.
The processor and memory may be connected by a bus or other means.
The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The non-transitory software program and the instructions required for implementing the thermal simulation analysis method for the liquid-cooled energy storage system according to the first embodiment are stored in the memory, and when being executed by the processor, the thermal simulation analysis method for the liquid-cooled energy storage system according to the first embodiment is performed, for example, the method steps S100 to S700 in fig. 1, the method steps S110 to S120 in fig. 2, the method steps S121 to S124 in fig. 3, the method steps S210 to S230 in fig. 6, the method steps S231 to S232 in fig. 7, the method steps S310 to S330 in fig. 10, the method steps S331 to S332 in fig. 11, and the method steps S710 to S740 in fig. 14 are performed.
The above described embodiments of the device are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may fall into one place, or may also be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
Furthermore, a computer-readable storage medium is provided in an embodiment of the present invention, and the computer-readable storage medium stores computer-executable instructions, which are executed by a processor or a controller, for example, by a processor in the above apparatus embodiment, and enable the processor to perform the thermal simulation analysis method for the liquid-cooled energy storage system in the above embodiment, for example, to perform the method steps S100 to S700 in fig. 1, the method steps S110 to S120 in fig. 2, the method steps S121 to S124 in fig. 3, the method steps S210 to S230 in fig. 6, the method steps S231 to S232 in fig. 7, the method steps S310 to S330 in fig. 10, the method steps S331 to S332 in fig. 11, and the method steps S710 to S740 in fig. 14, which are described above.
One of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
While the preferred embodiments of the present invention have been described in detail, it will be understood by those skilled in the art that the foregoing and various other changes, omissions and deviations in the form and detail thereof may be made without departing from the scope of this invention.
Claims (10)
1. The thermal simulation analysis method of the liquid cooling energy storage system is characterized by being applied to the liquid cooling energy storage system and comprising the following steps:
establishing a liquid cooling plate simulation model according to preset electric core data;
establishing a battery cluster flow simulation model according to the liquid cooling plate simulation model and preset battery cluster data;
establishing a battery stack flow simulation model according to the battery cluster flow simulation model and preset battery stack data;
calculating to obtain flow distribution differences of a plurality of pipeline inlets in the liquid cooling energy storage system according to the battery cluster flow simulation model and the battery stack flow simulation model;
calculating to obtain a plurality of highest temperatures which are in one-to-one correspondence with the plurality of flow distribution differences according to the liquid cooling plate simulation model;
obtaining a temperature difference fitting curve according to the plurality of flow distribution differences and the corresponding plurality of maximum temperatures;
and calculating to obtain the maximum temperature difference of the cell stack in the liquid cooling energy storage system according to the temperature difference fitting curve.
2. The liquid-cooled energy storage system thermal simulation analysis method of claim 1, wherein the cell data includes cell heating parameters, cell distribution parameters, and cell demand parameters, and the establishing of the liquid-cooled panel simulation model according to the preset cell data includes:
obtaining a preliminary liquid cooling plate simulation model according to the electric core heating parameters, the electric core distribution parameters and the electric core demand parameters;
and continuously optimizing the flow size and the flow channel form of the primary liquid cooling plate simulation model until the primary liquid cooling plate simulation model meets the preset liquid cooling condition to obtain the liquid cooling plate simulation model.
3. The liquid-cooled energy storage system thermal simulation analysis method of claim 2, wherein the liquid-cooled conditions include at least one of:
the maximum simulation temperature of the battery module in the primary liquid cooling plate simulation model is less than or equal to the preset maximum temperature value of the battery module;
the maximum simulation temperature difference of the battery module in the preliminary liquid cooling plate simulation model is smaller than or equal to the preset maximum temperature difference of the battery module;
the simulation pressure drop value of the inlet and the outlet of the liquid cooling plate in the preliminary liquid cooling plate simulation model is smaller than the preset pressure drop value of the inlet and the outlet of the liquid cooling plate;
and the simulation temperature difference of the inlet and the outlet of the liquid cooling plate in the preliminary liquid cooling plate simulation model is less than or equal to the preset temperature difference of the inlet and the outlet of the liquid cooling plate.
4. The method for thermal simulation analysis of a liquid-cooled energy storage system according to claim 1, wherein the battery cluster data includes battery module distribution parameters and battery module demand parameters, and the establishing of the battery cluster flow simulation model according to the liquid-cooled board simulation model and the preset battery cluster data comprises:
obtaining inlet and outlet pipe diameter data of a plurality of battery modules according to the liquid cooling plate simulation model;
obtaining a preliminary battery cluster flow simulation model according to the battery module distribution parameters, the battery module demand parameters and the pipe diameter data of the inlet and the outlet of the plurality of battery modules;
and continuously optimizing the size and the type of the flow pipe of the primary battery cluster flow simulation model until the primary battery cluster flow simulation model meets the preset battery cluster conditions to obtain a battery cluster flow simulation model.
5. The liquid-cooled energy storage system thermal simulation analysis method of claim 4, wherein the battery cluster condition comprises at least one of:
the inlet flow distribution difference simulated by each battery module in the preliminary battery cluster flow simulation model is smaller than or equal to the inlet flow distribution difference preset by each battery module;
and the simulation pressure drop value of the inlet and the outlet of the battery cluster in the preliminary battery cluster flow simulation model is smaller than the preset pressure drop value of the inlet and the outlet of the battery cluster.
6. The method for thermal simulation analysis of a liquid-cooled energy storage system according to claim 1, wherein the stack data comprises a cluster distribution parameter and a cluster demand parameter, and the establishing of the stack flow simulation model according to the cluster flow simulation model and the preset stack data comprises:
obtaining a plurality of battery cluster inlet and outlet pipe diameter data according to the battery cluster flow simulation model;
obtaining a primary cell stack flow simulation model according to the cell cluster distribution parameters, the cell cluster demand parameters and the pipe diameter data of the inlet and the outlet of the plurality of cell clusters;
and continuously optimizing the size and the type of the flow pipe of the primary cell stack flow simulation model until the primary cell stack flow simulation model meets the preset cell stack conditions to obtain the cell stack flow simulation model.
7. The liquid-cooled energy storage system thermal simulation analysis method of claim 6, wherein the stack conditions include at least one of:
the inlet flow distribution difference of each battery cluster simulation in the preliminary battery stack flow simulation model is smaller than or equal to the inlet flow distribution difference preset by each battery cluster;
and the simulation pressure drop value of the inlet and the outlet of the cell stack in the preliminary cell stack flow simulation model is smaller than the preset pressure drop value of the inlet and the outlet of the cell stack.
8. The method for thermal simulation analysis of a liquid-cooled energy storage system according to claim 1, wherein the step of calculating the maximum temperature difference of the cell stack in the liquid-cooled energy storage system according to the temperature difference fitting curve comprises:
calculating to obtain a first difference value of inlet flow distribution among the battery modules in each battery cluster in the liquid-cooled energy storage system according to the battery cluster flow simulation model;
calculating to obtain a second difference value of inlet flow distribution among the battery clusters in the battery stack according to the battery stack flow simulation model;
calculating to obtain a third difference value of inlet flow distribution among the plurality of battery modules in the battery stack according to the first difference value and the second difference value;
and calculating to obtain the maximum temperature difference corresponding to the third difference value according to the temperature difference fitting curve.
9. A liquid cooling energy storage system thermal simulation analytical equipment, characterized by includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements the method for thermal simulation analysis of a liquid-cooled energy storage system according to any of claims 1 to 8.
10. A computer-readable storage medium characterized by: the computer-readable storage medium stores computer-executable instructions for causing a computer to perform the method for thermal simulation analysis of a liquid-cooled energy storage system of any of claims 1 to 8.
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| CN116345014A (en) * | 2023-05-31 | 2023-06-27 | 苏州精控能源科技有限公司 | Large energy storage system thermal management method, electronic equipment and storage medium |
| CN116609685A (en) * | 2023-03-02 | 2023-08-18 | 北京双登慧峰聚能科技有限公司 | Monitoring method and system applied to liquid cooling energy storage system |
| CN117454601A (en) * | 2023-10-09 | 2024-01-26 | 中汽研汽车检验中心(广州)有限公司 | Method for determining parameters of cooling water pump of thermal management system, electronic equipment and medium |
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| CN114284593A (en) * | 2021-12-17 | 2022-04-05 | 重庆长安汽车股份有限公司 | Thermal management system for intelligently adjusting temperature of power battery |
| CN114284593B (en) * | 2021-12-17 | 2023-07-14 | 重庆长安汽车股份有限公司 | Heat management system capable of intelligently adjusting temperature of power battery |
| CN116609685A (en) * | 2023-03-02 | 2023-08-18 | 北京双登慧峰聚能科技有限公司 | Monitoring method and system applied to liquid cooling energy storage system |
| CN116345014A (en) * | 2023-05-31 | 2023-06-27 | 苏州精控能源科技有限公司 | Large energy storage system thermal management method, electronic equipment and storage medium |
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