Battery pack temperature field construction method and device, electronic equipment and storage medium
By collecting multiple temperature points and current values in the lithium-ion battery module, combining interpolation polynomial solution and convolutional neural network model, identifying the distribution results of the temperature field of the battery pack, the problem of incomplete capture of the temperature distribution within the battery pack is solved, and high-precision temperature monitoring and reduction of the risk of thermal runaway is achieved.
Patent Information
- Application Number
- CN202411661886.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-20
- Publication Date
- 2025-03-14
AI Technical Summary
The temperature sampling points arranged in the module of the lithium-ion battery are sparse, and the temperature distribution inside the entire battery pack cannot be completely captured, increasing the risk of thermal runaway from the battery.
By collecting multiple temperature points and current values on the battery management system BMS, interpolated polynomial solution obtains the target polynomial coefficients and inputs them into the trained convolutional neural network model to identify the distribution results of the battery pack temperature field.
It realizes a more refined and accurate estimation of the internal temperature field of the battery pack, optimizes the performance of the BMS, reduces the risk of thermal runaway from the battery, and provides real-time temperature monitoring and early warning functions.
Smart Images
Figure CN119619852A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of lithium battery technology, and in particular to a method, device, electronic device and storage medium for constructing a temperature field of a battery pack. Background Art
[0002] Lithium-ion batteries are very sensitive to temperature. Usually, the temperature sampling points arranged in the battery pack module are sparse. Due to the limited number of sampling points, it may not be possible to fully capture the temperature distribution inside the entire battery pack, resulting in incomplete and inaccurate data analysis. If the sampling points fail to cover the hot spots inside the battery pack, these potential overheating areas may be overlooked, increasing the risk of thermal runaway of the battery. Summary of the invention
[0003] The present application provides a method, device, electronic device and storage medium for constructing a battery pack temperature field to solve the problem that the temperature sampling points arranged in the lithium-ion battery module are sparse, the temperature distribution inside the entire battery pack cannot be fully captured, and the risk of thermal runaway of the battery is increased.
[0004] The first aspect of the present application provides a method for constructing a battery pack temperature field, comprising the following steps: collecting multiple temperature points and current values on a battery management system BMS; performing interpolation polynomial solutions on the multiple temperature points and current values to obtain target polynomial coefficients, and inputting the target polynomial coefficients into a trained convolutional neural network model for recognition to obtain a distribution result of the battery pack temperature field, wherein the trained convolutional neural model is obtained by calculating a first polynomial coefficient and a second polynomial coefficient using a target polynomial equation according to the position and temperature value of each single battery in the battery pack, and is trained based on the first polynomial coefficient and the second polynomial coefficient.
[0005] Through the above scheme, a more detailed and accurate estimation of the temperature field inside the battery pack can be made through the trained model based on multiple temperature points and current values. By combining the solution of interpolation polynomials and CNN prediction, the temperature field of the battery pack can be monitored quickly and accurately, thereby optimizing the performance of the BMS.
[0006] Optionally, before using the trained convolutional neural model to collect multiple temperature points and current values on the battery management system BMS, it includes: obtaining the position and temperature value of each single battery in the battery pack, and according to the position information and temperature value of each single battery, using basis functions to establish the target polynomial equation, and charging and discharging the battery pack to collect all temperature point data and the average current value per minute; substituting the known temperature point data of the battery pack into the polynomial equation to obtain the first polynomial coefficient, and substituting all temperature point data into the polynomial equation to obtain the second polynomial coefficient; using the first polynomial coefficient, the second polynomial coefficient and the average current value as training data to train the convolutional neural model to obtain the trained convolutional neural model.
[0007] Optionally, the basis function is:
[0008]
[0009] The target polynomial equation is:
[0010]
[0011] Among them, l i (x, y) is the Lagrangian basis function, (x, y) is the position of the rectangle corresponding to each monomer, x i is the horizontal coordinate value of the position of the ith single cell, y i is the ordinate value of the position of the i-th single cell, T i is the temperature value of the i-th single cell.
[0012] Through the above scheme, the target polynomial equation established by the basis function can accurately predict the temperature field inside the battery pack based on the position and temperature value of the single battery. The first polynomial coefficient and the second polynomial coefficient, combined with the average current value per minute, provide sufficient information for the training of the convolutional neural model.
[0013] Optionally, after obtaining the distribution results of the battery pack temperature field, the method includes: monitoring the temperature field and thermal management status of the battery pack according to the distribution results of the battery pack temperature field, and issuing a fault warning when the temperature of the battery pack exceeds a preset high temperature threshold or the temperature of the battery pack is lower than a preset low temperature threshold.
[0014] Through the above solution, real-time temperature monitoring and early warning can be achieved to improve the safety of the battery system.
[0015] The second aspect of the present application provides a device for constructing a battery pack temperature field, including: an acquisition module, used to collect multiple temperature points and current values on a battery management system BMS; a construction module, used to perform interpolation polynomial solutions on the multiple temperature points and current values to obtain target polynomial coefficients, and input the target polynomial coefficients into a trained convolutional neural network model for recognition to obtain a distribution result of the battery pack temperature field, wherein the trained convolutional neural model is obtained by calculating a first polynomial coefficient and a second polynomial coefficient using a target polynomial equation according to the position and temperature value of each single battery in the battery pack, and is trained based on the first polynomial coefficient and the second polynomial coefficient.
[0016] Optionally, before using the trained convolutional neural model to collect multiple temperature points and current values on the battery management system BMS, the construction module is also used to: obtain the position and temperature value of each single battery in the battery pack, and establish the target polynomial equation using basis functions based on the position and temperature value of each single battery, and charge and discharge the battery pack to collect all temperature point data and the average current value per minute; substitute the known temperature point data of the battery pack into the polynomial equation to obtain the first polynomial coefficient, and substitute all temperature point data into the polynomial equation to obtain the second polynomial coefficient; use the first polynomial coefficient, the second polynomial coefficient and the average current value as training data to train the convolutional neural model to obtain the trained convolutional neural model.
[0017] Optionally, the basis function is:
[0018]
[0019] The target polynomial equation is:
[0020]
[0021] Among them, l i (x, y) is the Lagrangian basis function, (x, y) is the position of the rectangle corresponding to each monomer, x i is the horizontal coordinate value of the position of the ith single cell, y i is the ordinate value of the position of the i-th single cell, T i is the temperature value of the i-th single cell.
[0022] Optionally, after obtaining the distribution results of the battery pack temperature field, the construction module is also used to: monitor the temperature field and thermal management status of the battery pack according to the distribution results of the battery pack temperature field, and issue a fault warning when the temperature of the battery pack exceeds a preset high temperature threshold or the temperature of the battery pack is lower than a preset low temperature threshold.
[0023] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for constructing a battery pack temperature field as described in the above embodiment.
[0024] The fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the method for constructing a battery pack temperature field as described in the above embodiment.
[0025] In the above implementation, multiple temperature points and current values on the battery management system BMS are collected, and the interpolation polynomials of the multiple temperature points and current values are solved to obtain the target polynomial coefficients, and the target polynomial coefficients are input into the trained convolutional neural network model for identification to obtain the distribution results of the battery pack temperature field. In this way, the problem that the temperature sampling points of lithium-ion batteries arranged in the module are sparse and the temperature distribution inside the entire battery pack cannot be fully captured, which increases the risk of thermal runaway of the battery, etc., can be solved. The temperature of each area of the battery pack can be effectively and accurately estimated to prevent thermal runaway of the battery during use and optimize temperature fault diagnosis.
[0026] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS
[0027] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:
[0028] Figure 1 A flowchart of a method for constructing a battery pack temperature field according to an embodiment of the present application;
[0029] Figure 2 is a flow chart of a method for constructing a battery pack temperature field according to an embodiment of the present application;
[0030] Figure 3 A schematic diagram of the position of a battery pack cell according to an embodiment of the present application;
[0031] Figure 4 This is an example diagram of a device for constructing a battery pack temperature field according to an embodiment of the present application;
[0032] Figure 5 Schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION
[0033] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.
[0034] The following describes the construction method, device, electronic device and storage medium of the battery pack temperature field of the embodiment of the present application with reference to the accompanying drawings. In view of the problem mentioned in the above background technology that the temperature sampling points arranged in the module of the lithium-ion battery are sparse, and the temperature distribution inside the entire battery pack cannot be fully captured, which increases the risk of thermal runaway of the battery, the present application provides a method for constructing the temperature field of the battery pack, in which multiple temperature points and current values on the battery management system BMS are collected, and the interpolation polynomials of the multiple temperature points and current values are solved to obtain the target polynomial coefficients, and the target polynomial coefficients are input into the trained convolutional neural network model for identification to obtain the distribution result of the battery pack temperature field. As a result, the problem that the temperature sampling points arranged in the module of the lithium-ion battery are sparse, and the temperature distribution inside the entire battery pack cannot be fully captured, which increases the risk of thermal runaway of the battery, etc., can be effectively and accurately estimated. The temperature of each area of the battery pack can prevent the battery from thermal runaway during use and optimize temperature fault diagnosis.
[0035] Specifically, Figure 1 A schematic flow chart of a method for constructing a battery pack temperature field provided in an embodiment of the present application.
[0036] like Figure 1 As shown, the method for constructing the battery pack temperature field includes the following steps:
[0037] In step S101 , multiple temperature points and current values on the battery management system BMS are collected.
[0038] In step S102, an interpolation polynomial is solved for multiple temperature points and current values to obtain target polynomial coefficients, and the target polynomial coefficients are input into a trained convolutional neural network model for recognition to obtain a distribution result of the battery pack temperature field, wherein the trained convolutional neural model is obtained by calculating the first polynomial coefficient and the second polynomial coefficient using the target polynomial equation according to the position and temperature value of each single battery in the battery pack, and is trained based on the first polynomial coefficient and the second polynomial coefficient.
[0039] Optionally, in some embodiments, before using the trained convolutional neural model to collect multiple temperature points and current values on the battery management system BMS, it includes: obtaining the position and temperature value of each single battery in the battery pack, and according to the position information and temperature value of each single battery, using basis functions to establish a target polynomial equation, and charging and discharging the battery pack to collect all temperature point data and the average current value per minute; substituting the known temperature point data of the battery pack into the polynomial equation to obtain the first polynomial coefficient, and substituting all temperature point data into the polynomial equation to obtain the second polynomial coefficient; using the first polynomial coefficient, the second polynomial coefficient and the average current value as training data to train the convolutional neural model to obtain a trained convolutional neural model.
[0040] Optionally, in some embodiments, the basis function is:
[0041]
[0042] The target polynomial equation is:
[0043]
[0044] Among them, l i (x, y) is the Lagrangian basis function, (x, y) is the position of the rectangle corresponding to each monomer, x i is the horizontal coordinate value of the position of the ith single cell, y i is the ordinate value of the position of the i-th single cell, T i is the temperature value of the i-th single cell.
[0045] It should be understood that if Figure 2 As shown:
[0046] In the first step, the embodiment of the present application establishes an initial battery pack temperature field according to the temperature sampling points to obtain a polynomial equation between the battery position and the battery temperature;
[0047] The second step is to place temperature measurement points at each cell position of the battery pack, conduct charge and discharge experiments on the battery pack under various working conditions, and record the experimental data of each temperature measurement point and the 1-minute current average value;
[0048] The third step is to calculate the temperature according to the known temperature point data T org And all temperature point data T all , substituting into the polynomial equation, we get the first polynomial coefficient K org and the second polynomial coefficient K all ;
[0049] The fourth step is to convert the first polynomial coefficient K org The current average value C is input to the neural network, and the second polynomial coefficient K allAs a feature set of sample sets to train the CNN model;
[0050] In the fifth step, the trained convolutional neural model is deployed to the controller, and the target polynomial coefficient K is calculated according to the temperature points and current values of the BMS, and finally the optimized battery pack temperature field is obtained.
[0051] Specifically, according to the cell position and temperature value of the battery pack, a polynomial equation for the battery pack temperature field is established. The battery pack is a rectangular structure, such as Figure 3 As shown in the figure, one of the intersection points of the rectangle is taken as the origin (0, 0), the position of the rectangle corresponding to each cell is (x, y), and the distance between each adjacent cell is 1. Assuming that the battery pack has n+1 cells, the corresponding position data are (x0, y0, T0), (x1, y1, T1), (x n ,y n , T n ).
[0052] Further construct a binary n-degree polynomial P(x, y) to pass through these points, construct basis functions, and for each monomer i = 0, 1, 2, ..., n, construct the Lagrangian basis function l i (x, y) is as follows:
[0053]
[0054] Construct the interpolating polynomial equation:
[0055]
[0056] For any (x, y) value that is not at a data point, we can calculate the approximate function value by substituting (x, y) into the difference polynomial P(x, y) and find K P(x，y) Polynomial coefficient matrix:
[0057] K P =[k0, ..., k n ]
[0058] Add a temperature measurement point to each cell in the battery pack. The added temperature measurement point covers the temperature of each cell and serves as the data input for establishing an accurate temperature field for the battery pack. Perform charge and discharge experiments on the battery pack under different working conditions and record the temperature data of each temperature point (T0~T n ), 1 minute average current value C avg ,in,
[0059]
[0060] Mark all temperature points of the battery pack as Get the first polynomial coefficient according to the polynomial equation Assuming that the recorded data has i moments, there are i polynomial coefficients in total
[0061] Filter out the original temperature point data and mark it as Get the second polynomial coefficients from the polynomial equation Assume that the recorded data has i moments and i polynomial coefficients
[0062] Furthermore, a convolutional neural network (CNN) model is constructed, which includes a convolutional layer, a pooling layer, and a fully connected layer.
[0063] calculate and In addition, considering that the heat generated by battery operation is the main factor affecting temperature, the 1-minute average current value C is introduced. avg , then the input layer is:
[0064]
[0065] For input X i Perform convolution operation, extract the features of the coefficients, obtain the sequence features of the state representative vector, input the sequence features into a fully connected softmax layer, and the corresponding labels are:
[0066]
[0067] The trained convolutional neural network model is deployed on the controller. The temperature points and current values can be collected in real time on the BMS. At this time, the temperature points are sparse points and do not contain all temperature points. The target polynomial coefficient K is obtained by solving the interpolation polynomial of the sparse points. org , the target polynomial coefficient K org Input into the trained convolutional neural network model for recognition, and obtain the distribution result K of the optimized battery pack temperature field all , complete the online construction of the battery pack temperature field.
[0068] Optionally, in some embodiments, after obtaining the distribution results of the battery pack temperature field, it includes: monitoring the temperature field and thermal management status of the battery pack according to the distribution results of the battery pack temperature field, and issuing a fault warning when the temperature of the battery pack exceeds a preset high temperature threshold or the temperature of the battery pack is lower than a preset low temperature threshold.
[0069] Among them, the preset high temperature threshold and the preset low temperature threshold can be thresholds pre-set by the user, can be thresholds obtained through a limited number of experiments, or can be thresholds obtained through a limited number of computer simulations, and are not specifically limited here.
[0070] When the battery pack temperature exceeds the preset high temperature threshold, the system should immediately issue a warning signal to remind the operator to pay attention to the temperature condition of the battery pack. When the battery pack temperature is lower than the preset low temperature threshold, the system should issue a low temperature alarm signal to remind the operator to suspend charging or discharging operations and wait until the battery temperature returns to normal before continuing.
[0071] According to the method for constructing the battery pack temperature field proposed in the embodiment of the present application, multiple temperature points and current values on the battery management system BMS are collected, and the target polynomial coefficients are obtained by interpolation polynomial solution for the multiple temperature points and current values. The target polynomial coefficients are input into the trained convolutional neural network model for identification to obtain the distribution result of the battery pack temperature field. In this way, the problem that the temperature sampling points of the lithium-ion batteries arranged in the module are sparse, the temperature distribution inside the entire battery pack cannot be fully captured, and the risk of thermal runaway of the battery is increased is solved. The temperature of each area of the battery pack can be effectively and accurately estimated to prevent thermal runaway of the battery during use and optimize temperature fault diagnosis.
[0072] Next, a device for constructing a battery pack temperature field according to an embodiment of the present application will be described with reference to the accompanying drawings.
[0073] Figure 4 It is a block diagram of a device for constructing a battery pack temperature field according to an embodiment of the present application.
[0074] like Figure 4 As shown, the device 10 for constructing the battery pack temperature field includes: a collection module 100 and a construction module 200 .
[0075] Among them, the acquisition module 100 is used to collect multiple temperature points and current values on the battery management system BMS; the construction module 200 is used to solve the interpolation polynomial for multiple temperature points and current values to obtain the target polynomial coefficients, and input the target polynomial coefficients into the trained convolutional neural network model for identification to obtain the distribution results of the battery pack temperature field, wherein the trained convolutional neural model is calculated by using the target polynomial equation according to the position and temperature value of each single battery in the battery pack, and is trained based on the first polynomial coefficient and the second polynomial coefficient.
[0076] Optionally, in some embodiments, before using the trained convolutional neural model to collect multiple temperature points and current values on the battery management system BMS, the construction module 200 is also used to: obtain the position and temperature value of each single battery in the battery pack, and according to the position and temperature value of each single battery, establish a target polynomial equation using a basis function, and charge and discharge the battery pack to collect all temperature point data and the average current value per minute; substitute the known temperature point data of the battery pack into the polynomial equation to obtain the first polynomial coefficient, and substitute all temperature point data into the polynomial equation to obtain the second polynomial coefficient; use the first polynomial coefficient, the second polynomial coefficient and the average current value as training data to train the convolutional neural model to obtain a trained convolutional neural model.
[0077] Optionally, in some embodiments, the basis function is:
[0078]
[0079] The target polynomial equation is:
[0080]
[0081] Among them, l i (x, y) is the Lagrangian basis function, (x, y) is the position of the rectangle corresponding to each monomer, x i is the horizontal coordinate value of the position of the ith single cell, y i is the ordinate value of the position of the i-th single cell, T i is the temperature value of the i-th single cell.
[0082] Optionally, in some embodiments, after obtaining the distribution results of the battery pack temperature field, the construction module 200 is also used to: monitor the temperature field and thermal management status of the battery pack according to the distribution results of the battery pack temperature field, and issue a fault warning when the temperature of the battery pack exceeds a preset high temperature threshold or the temperature of the battery pack is lower than a preset low temperature threshold.
[0083] It should be noted that the above explanation of the embodiment of the method for constructing the battery pack temperature field is also applicable to the device for constructing the battery pack temperature field of this embodiment, and will not be repeated here.
[0084] According to the device for constructing the battery pack temperature field proposed in the embodiment of the present application, multiple temperature points and current values on the battery management system BMS are collected, and the interpolation polynomials of the multiple temperature points and current values are solved to obtain the target polynomial coefficients, and the target polynomial coefficients are input into the trained convolutional neural network model for identification to obtain the distribution results of the battery pack temperature field. In this way, the problem that the temperature sampling points of the lithium-ion batteries arranged in the module are sparse and the temperature distribution inside the entire battery pack cannot be fully captured, which increases the risk of thermal runaway of the battery, etc., can be solved. The temperature of each area of the battery pack can be effectively and accurately estimated to prevent thermal runaway of the battery during use and optimize temperature fault diagnosis.
[0085] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:
[0086] A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .
[0087] When the processor 502 executes the program, the method for constructing the battery pack temperature field provided in the above embodiment is implemented.
[0088] Furthermore, the electronic device further comprises:
[0089] The communication interface 503 is used for communication between the memory 501 and the processor 502 .
[0090] The memory 501 is used to store computer programs that can be executed on the processor 502 .
[0091] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.
[0092] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.
[0093] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.
[0094] The processor 502 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.
[0095] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for constructing a battery pack temperature field as described above.
[0096] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.
[0097] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.
[0098] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.
[0099] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable storage medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable storage medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable storage media include the following: an electrical connection with one or N wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable storage medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.
[0100] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.
[0101] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.
[0102] In addition, each functional unit in each embodiment of the present application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.
[0103] The computer-readable storage medium mentioned above may be a read-only memory, a disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.
Claims
1. A method for constructing a battery pack temperature field, characterized in that: The following steps are involved: Collect multiple temperature points and current values on the battery management system BMS; An interpolation polynomial is solved for the multiple temperature points and current values to obtain target polynomial coefficients, and the target polynomial coefficients are input into a trained convolutional neural network model for identification to obtain a distribution result of the battery pack temperature field, wherein the trained convolutional neural model is obtained by calculating the first polynomial coefficient and the second polynomial coefficient using a target polynomial equation according to the position and temperature value of each single battery in the battery pack, and is trained based on the first polynomial coefficient and the second polynomial coefficient.
2. The method according to claim 1, characterized in that Before using the trained convolutional neural model to collect multiple temperature points and current values on the battery management system BMS, it includes: Obtaining the position and temperature value of each single battery in the battery pack, and establishing the target polynomial equation using basis functions according to the position and temperature value of each single battery, and charging and discharging the battery pack to collect all temperature point data and average current value per minute; Substituting the known temperature point data of the battery pack into the polynomial equation to obtain the first polynomial coefficient, and substituting all the temperature point data into the polynomial equation to obtain the second polynomial coefficient; The first polynomial coefficient, the second polynomial coefficient and the average current value are used as training data to train a convolutional neural model to obtain the trained convolutional neural model.
3. The method according to claim 2, characterized in that The basis functions are: The target polynomial equation is: Among them, l i (x, y) is the Lagrangian basis function, (x, y) is the position of the rectangle corresponding to each monomer, x i is the horizontal coordinate value of the position of the ith single cell, y i is the ordinate value of the position of the i-th single cell, T i is the temperature value of the i-th single cell.
4. The method according to claim 1, characterized in that: After obtaining the distribution results of the battery pack temperature field, including: The temperature field and thermal management status of the battery pack are monitored according to the distribution results of the battery pack temperature field, and a fault warning is issued when the temperature of the battery pack exceeds a preset high temperature threshold or the temperature of the battery pack is lower than a preset low temperature threshold.
5. A device for constructing a battery pack temperature field, characterized in that: include: The acquisition module is used to collect multiple temperature points and current values on the battery management system BMS; A construction module is provided for solving an interpolation polynomial for the multiple temperature points and current values to obtain target polynomial coefficients, and inputting the target polynomial coefficients into a trained convolutional neural network model for identification to obtain a distribution result of the battery pack temperature field, wherein the trained convolutional neural model is obtained by calculating a first polynomial coefficient and a second polynomial coefficient using a target polynomial equation according to the position and temperature value of each single battery in the battery pack, and is trained based on the first polynomial coefficient and the second polynomial coefficient.
6. The device according to claim 5, characterized in that Before using the trained convolutional neural model to collect multiple temperature points and current values on the battery management system BMS, the building module is also used to: Obtaining the position and temperature value of each single battery in the battery pack, and establishing the target polynomial equation using basis functions according to the position and temperature value of each single battery, and charging and discharging the battery pack to collect all temperature point data and average current value per minute; Substituting the known temperature point data of the battery pack into the polynomial equation to obtain the first polynomial coefficient, and substituting all the temperature point data into the polynomial equation to obtain the second polynomial coefficient; The first polynomial coefficient, the second polynomial coefficient and the average current value are used as training data to train a convolutional neural model to obtain the trained convolutional neural model.
7. The device according to claim 6, characterized in that The basis functions are: The target polynomial equation is: Among them, l i (x, y) is the Lagrangian basis function, (x, y) is the position of the rectangle corresponding to each monomer, x i is the horizontal coordinate value of the position of the ith single cell, y i is the ordinate value of the position of the i-th single cell, T i is the temperature value of the i-th single cell.
8. The device according to claim 5, characterized in that After obtaining the distribution result of the battery pack temperature field, the construction module is also used to: The temperature field and thermal management status of the battery pack are monitored according to the distribution results of the battery pack temperature field, and a fault warning is issued when the temperature of the battery pack exceeds a preset high temperature threshold or the temperature of the battery pack is lower than a preset low temperature threshold.
9. An electronic device, characterized in that: Including memory and processor; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the method for constructing the battery pack temperature field as described in any one of claims 1 to 4.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement a method for constructing a battery pack temperature field as described in any one of claims 1 to 4.