Large battery entropy thermal coefficient prediction method and device based on soft package battery

Abstract

The embodiment of the invention discloses a large battery entropy thermal coefficient prediction method and device based on a soft package battery. The method comprises the following steps: determining a test soft package battery corresponding to a target large battery through a battery type system, and determining a soft package entropy thermal coefficient corresponding to each battery charge state of the test soft package battery based on an open-circuit voltage test method, and finally, according to the battery current ratio, the battery volume ratio and each soft package entropy thermal coefficient, determining the corresponding large battery entropy thermal coefficient of the target large battery in each battery charge state. The large battery entropy thermal coefficient corresponding to the battery current ratio, the battery volume ratio and the soft package entropy thermal coefficient is indirectly calculated, the scheme limitation that a large battery needs to be actually manufactured in the design stage is avoided, a large amount of research and development cost is saved, the large battery entropy thermal coefficient can be rapidly obtained through the prediction method, and the prediction efficiency is improved. And the real-time performance of subsequent calculation steps is enhanced while the prediction precision requirement is met, and the use satisfaction degree of the user is improved.

Description

Technical Field

[0001] This specification relates to the field of battery heat generation technology, and in particular to a method for predicting the entropy thermal coefficient of large batteries. Background Technology

[0002] Chemical batteries, due to their advantages of high voltage, low self-discharge rate, and high energy density, have been widely used in energy storage and power battery fields. However, due to their inherent characteristics, chemical batteries generate heat during charging and discharging, resulting in unavoidable thermal effects. They must be used within safe temperature ranges; otherwise, thermal abuse and thermal runaway may occur. Therefore, it is necessary to obtain the entropy thermal coefficient of chemical batteries to calculate the heat generation power and explore thermal management methods. Currently, the entropy thermal coefficient of batteries is mainly obtained through actual battery testing. However, actual testing requires the production of large batteries during the product development and design phase, which not only wastes human, material, and financial resources but also prolongs the development cycle and reduces user satisfaction. Summary of the Invention

[0003] This specification provides an embodiment of a method, apparatus, and electronic device for predicting the entropy thermal coefficient of large batteries based on pouch cells. The technical solution is as follows:

[0004] In a first aspect, embodiments of this specification provide a method for predicting the entropy thermal coefficient of a large battery based on a pouch cell, the method comprising:

[0005] The test pouch cell corresponding to the target large battery is determined based on the battery type system.

[0006] The entropy thermal coefficient of the test soft-pack battery under each state of charge was determined based on the open-circuit voltage test method.

[0007] The entropy-thermal coefficients of the target large battery under each battery state of charge are determined based on the battery current ratio, battery volume ratio, and the entropy-thermal coefficients of each pouch cell. The battery current ratio is determined by the target large battery current parameters and the test pouch cell current parameters, and the battery volume ratio is determined by the target large battery volume parameters and the test pouch cell volume parameters.

[0008] Secondly, a device for predicting the entropy thermal coefficient of a large battery based on a pouch cell is provided, the device comprising:

[0009] The determination module is used to determine the test pouch cell corresponding to the target large battery based on the battery type system.

[0010] The test module is used to determine the entropy thermal coefficient of the test pouch battery under each state of charge, based on the open-circuit voltage test method.

[0011] The calculation module is used to determine the entropy-thermal coefficient of the target large battery under each battery state of charge based on the battery current ratio, battery volume ratio, and the entropy-thermal coefficient of each of the pouch cells. The battery current ratio is determined by the target large battery current parameters and the test pouch cell current parameters, and the battery volume ratio is determined by the target large battery volume parameters and the test pouch cell volume parameters.

[0012] Thirdly, an electronic device is provided, including a device processor and a memory;

[0013] The device processor is connected to the memory;

[0014] The memory is used to store executable program code;

[0015] The device processor runs a program corresponding to the executable program code stored in the memory to perform the steps of the method provided as in the first aspect or any possible implementation thereof.

[0016] Fourthly, a computer-readable storage medium is provided having a computer program stored thereon, the computer-readable storage medium storing instructions that, when executed on a computer or device processor, cause the computer or device processor to perform the method provided as in the first aspect or any possible implementation thereof.

[0017] The beneficial effects of the technical solutions provided in some embodiments of this specification include at least the following:

[0018] In one or more embodiments of this specification, the test pouch cell corresponding to the target large battery can be determined first based on the battery type system. Then, the entropy thermal coefficient of the test pouch cell under each battery state of charge can be determined based on the open-circuit voltage test method. Finally, the entropy thermal coefficient of the target large battery under each battery state of charge can be determined based on the battery current ratio, battery volume ratio, and entropy thermal coefficient of each pouch cell. By indirectly calculating the entropy thermal coefficient of the corresponding large battery using the battery current ratio, battery volume ratio, and pouch cell entropy thermal coefficient, the limitations of the scheme requiring actual fabrication of the large battery during the design phase are avoided, saving a significant amount of R&D costs. Furthermore, this prediction method can quickly obtain the entropy thermal coefficient of the large battery, achieving the required prediction accuracy while enhancing the real-time performance of subsequent calculation steps, thus improving user satisfaction. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating a method for predicting the entropy thermal coefficient of a large battery based on a pouch cell, provided in the embodiments of this specification;

[0021] Figure 2 A test voltage-temperature fitting curve is shown in the embodiment of this specification for a method to predict the entropy thermal coefficient of a large battery based on a pouch cell.

[0022] Figure 3 A schematic diagram of the structure of a large battery entropy thermal coefficient prediction device based on a pouch battery provided in the embodiments of this specification;

[0023] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this specification. Detailed Implementation

[0024] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0025] The terms "first," "second," "third," etc., in the description, claims, and accompanying drawings are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0026] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the described elements without departing from the scope of this specification. Various processes or components may be appropriately omitted, substituted, or added to the examples. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.

[0027] Please see Figure 1 , Figure 1This document presents an overall flowchart of a method for predicting the entropy thermal coefficient of a large battery based on a pouch cell, as provided in an embodiment of this specification.

[0028] like Figure 1 As shown, the method for predicting the entropy thermal coefficient of a large battery based on a pouch cell can include at least the following steps:

[0029] Step 101: Determine the test pouch cell corresponding to the target large battery based on the battery type system.

[0030] In the embodiments of this specification, since calculating the heat generation power of a large battery requires first determining the entropy thermal coefficient of the large battery under its state of charge, but the entropy thermal coefficient of a large battery can only be directly obtained after actual manufacturing, while the entropy thermal coefficient of a smaller pouch battery is easier to obtain, and when the battery types of large and pouch batteries are the same, there is a certain relationship between the entropy thermal coefficients of the large battery and the pouch battery respectively, in order to avoid manufacturing the large battery during the product design stage, thus wasting manpower, material resources, and financial resources, the entropy thermal coefficient can be indirectly obtained through a pouch battery of the same type as the large battery. Specifically, when it is determined that the entropy thermal coefficient of the target large battery needs to be predicted under each battery state of charge, it is necessary to first determine the test pouch battery corresponding to the target large battery based on the battery type system, so that the entropy thermal coefficient of the large battery can be calculated subsequently using the pouch thermal coefficient of the test pouch battery.

[0031] In one possible implementation, determining the test pouch cell corresponding to the target large battery based on the battery type system includes:

[0032] Determine the target category system corresponding to the target large battery;

[0033] The test pouch battery corresponding to the target category system is determined based on the category database.

[0034] In the embodiments of this specification, to determine the test pouch battery corresponding to the target large battery based on the battery type system, a type database can first be constructed using historical test records. For a single target type system, there may be multiple pouch battery models with different capacity sizes in the historical test records. The pouch battery model with the most cumulative test counts under that target type system can be identified statistically. The pouch battery model with the most cumulative test counts reflects the ease of testing or the highest testing accuracy of that model, and therefore can be used as the test pouch battery. Thus, each battery type system in the constructed type database has a corresponding test pouch battery. Next, the target type system corresponding to the target large battery is directly determined by parsing the prediction command or receiving the command information from the target terminal. Then, the target type system is further queried in the constructed type database to obtain its corresponding test pouch battery.

[0035] Step 102: Determine the entropy thermal coefficient of the test pouch battery under each state of charge based on the open-circuit voltage test method.

[0036] In the embodiments of this specification, in order to indirectly predict the entropy thermal coefficient of the target large battery under various states of charge by testing the pouch cell, it is necessary to first determine the entropy thermal coefficient of the pouch cell under each state of charge. Specifically, when determining the entropy thermal coefficient of the pouch cell using the open-circuit voltage test method, the state of charge of the pouch cell can be fixed first, and the ambient temperature can be changed to measure the open-circuit voltage of the pouch cell under different ambient temperatures.

[0037] Optionally, when further determining the entropy-thermal coefficient of the soft package, the maximum temperature difference corresponding to different ambient temperatures and the maximum voltage difference corresponding to different open-circuit voltages can be determined first, and then the ratio of the maximum voltage difference to the maximum temperature difference can be calculated to obtain the entropy-thermal coefficient of the soft package.

[0038] Optionally, the acquired ambient temperatures and their corresponding open-circuit voltages can be used to construct a fitting curve, and the entropy-thermal coefficient of the soft package can be determined by calculating the slope of the fitting curve.

[0039] In one possible implementation, determining the entropy thermal coefficient of the test pouch battery at each state of charge based on the open-circuit voltage test method includes:

[0040] For any given state of charge of a battery, determine the test open-circuit voltage corresponding to different test ambient temperatures;

[0041] The entropy-thermal coefficient of the test pouch battery under the battery's state of charge is determined based on the test ambient temperature and the corresponding open-circuit voltage.

[0042] In the embodiments of this specification, for the same test pouch battery, different states of charge (SOCs) correspond to different pouch entropy thermal coefficients. Therefore, when determining the pouch entropy thermal coefficient corresponding to each SOC, the SOC of the pouch battery can be fixed first. As an example, the SOC of the pouch battery can be divided into 10%, 50%, or 80%, etc. Then, for any given SOC, the ambient temperature is continuously changed, and the open-circuit voltage corresponding to this ambient temperature is collected by a voltage sensor. Next, each ambient temperature and its corresponding open-circuit voltage are paired to form data pairs, and the pouch entropy thermal coefficient corresponding to the test pouch battery is determined by a pouch entropy thermal coefficient calculation model or curve fitting method. Optionally, when using the pouch entropy thermal coefficient calculation model, a pouch entropy thermal coefficient calculation model can be pre-built using polynomial functions or deep learning algorithms, and the polynomial coefficients can be determined or the pouch entropy thermal coefficient calculation model can be trained using a large number of historical calculation records. Then, the real-time measured data pairs are input into the model to obtain the pouch entropy thermal coefficient. Optionally, when using the curve fitting method, a fitting curve can be constructed based on each data pair, and the entropy-heat coefficient of the soft package can be determined by calculating the slope of the fitting curve.

[0043] In one possible implementation, determining the entropy-thermal coefficient of the test pouch battery corresponding to the battery's state of charge based on each of the test ambient temperatures and the test open-circuit voltage corresponding to each of the test ambient temperatures includes:

[0044] Based on the curve fitting method, test voltage-temperature fitting curves corresponding to each of the test environment temperatures and each of the test open-circuit voltages are constructed;

[0045] Calculate the slope of the test voltage-temperature fitting curve to obtain the entropy-thermal coefficient of the test pouch battery under the battery's charged state.

[0046] In the embodiments described in this specification, such as Figure 2 As shown, each test ambient temperature and its corresponding open-circuit voltage are paired to form data pairs. A test voltage-temperature coordinate system is constructed with voltage as the ordinate and temperature as the abscissa. Test points are then marked on this coordinate system for each data pair. Curve fitting is then performed on all marked test points to obtain the results shown below. Figure 2 The test voltage-temperature fitting curves are shown below. Each test voltage-temperature fitting curve corresponds to a test state of charge.

[0047] Next, the slope of the test voltage-temperature fitting curve is calculated based on the curve slope, and this curve slope is used as the entropy thermal coefficient of the soft-pack battery.

[0048] Step 103: Determine the entropy coefficient of the target large battery under each battery charge state based on the battery current ratio, battery volume ratio, and the entropy thermal coefficient of each soft pack.

[0049] The battery current ratio is determined by the target large battery current parameter and the test soft-pack battery current parameter, and the battery volume ratio is determined by the target large battery volume parameter and the test soft-pack battery volume parameter.

[0050] In the embodiments of this specification, after determining the entropy thermal coefficient of the test pouch battery for each state of charge, since the entropy thermal coefficient is highly correlated with the battery current and battery volume, it is necessary to further obtain the target large battery current parameter, the test pouch battery current parameter, the target large battery volume parameter, and the test pouch battery volume parameter. Next, the battery current ratio is determined using the target large battery current parameter and the test pouch battery current parameter, and the battery volume ratio is determined using the target large battery volume parameter and the test pouch battery volume parameter. Further, for any given battery state of charge, the entropy thermal coefficient of the target large battery for that state of charge is determined based on its corresponding battery current ratio, battery volume ratio, and pouch entropy thermal coefficient. Specifically, this can be achieved by constructing a multivariate parameter calculation model, setting the battery current ratio, battery volume ratio, and pouch entropy thermal coefficient as three different variable parameters, and then training and deriving the multivariate parameter model based on historical actual test data to obtain the prediction calculation model corresponding to these three variable parameters. Finally, the predictive calculation model is used to calculate the actual battery current ratio, battery volume ratio, and entropy-thermal coefficient of the pouch cell, obtaining the entropy-thermal coefficient of the target large battery under its state of charge. Similarly, for other battery states of charge, the entropy-thermal coefficients of the target large battery under each state of charge are calculated and statistically analyzed to obtain the entropy-thermal coefficients of the target large battery under different states of charge, so as to subsequently calculate the heat generation power of the target large battery under different states of charge.

[0051] In one possible implementation, before determining the entropy-thermal coefficient of the target large battery under each battery state of charge based on the battery current ratio, battery volume ratio, and the entropy-thermal coefficient of each of the pouch cells, the method further includes:

[0052] The ratio of the test soft-pack battery current parameter to the target large battery current parameter is calculated to obtain the battery current ratio.

[0053] The battery volume ratio is obtained by calculating the ratio between the target large battery volume parameters and the test soft-pack battery volume parameters.

[0054] In the embodiments of this specification, after obtaining the target large battery current parameters, the test pouch battery current parameters, the target large battery volume parameters, and the test pouch battery volume parameters, the ratio of the test pouch battery current parameters to the target large battery current parameters can be calculated to obtain the battery current ratio. Then, the ratio of the target large battery volume parameters to the test pouch battery volume parameters can be calculated to obtain the battery volume ratio, which is used for subsequent calculation of the large battery entropy thermal coefficient.

[0055] In one possible implementation, determining the entropy-thermal coefficient of the target large battery at each battery state of charge based on the battery current ratio, battery volume ratio, and the entropy-thermal coefficient of each of the pouch cells includes:

[0056] The ratio of battery current and the ratio of battery volume are multiplied to obtain the product result.

[0057] The entropy-thermal coefficients of each soft-pack battery are multiplied together to obtain the entropy-thermal coefficients of the target large battery under each battery's state of charge.

[0058] In the embodiments of this specification, when determining the entropy thermal coefficient of the target large battery for each battery state of charge based on the battery current ratio, battery volume ratio, and entropy thermal coefficient of each pouch cell, the battery current ratio and battery volume ratio are first multiplied to obtain the ratio product result. Then, for any battery state of charge, the calculated ratio product result is multiplied by the pouch entropy thermal coefficient corresponding to that battery state of charge to obtain the entropy thermal coefficient of the target large battery for that battery state of charge. For other battery states of charge, the calculation and statistics are performed similarly to obtain the entropy thermal coefficient of the target large battery for each battery state of charge. Specifically, for any battery state of charge, the formula for calculating the entropy thermal coefficient of the target large battery is as follows:

[0059]

[0060] in, The entropy thermal coefficient of a large battery is V / K; To test the current parameters of the pouch battery, A; For the target large battery current parameter, A; To test the volume parameters of the pouch battery, ; For the target large battery volume parameters, ; V / K represents the entropy-heat coefficient of the soft package.

[0061] In one possible implementation, the method further includes:

[0062] For any of the battery states of charge, the simulated temperature rise curve and the measured temperature rise curve corresponding to the entropy thermal coefficient of the large battery are determined based on the heat generation temperature rise test method.

[0063] The entropy-heat correction coefficient is determined based on the simulated temperature rise curve and the measured temperature rise curve.

[0064] The entropy-heat coefficient of the large battery is corrected according to the entropy-heat correction coefficient to obtain the optimized entropy-heat coefficient of the large battery.

[0065] In the embodiments of this specification, to improve the prediction accuracy of the entropy-thermal coefficient of a large battery, the determined entropy-thermal coefficient can be corrected using a method of comparison between actual measurement and simulation. Specifically, for any state of charge of a large battery, the actual temperature rise curve can be obtained by actual measurement of the test large battery, and then simulation can be performed based on the corresponding calculated entropy-thermal coefficient to obtain the corresponding simulated temperature rise curve. Next, the simulated temperature rise curve and the measured temperature rise curve are compared, and the entropy-thermal correction coefficient is continuously adjusted to minimize the error between the simulated and measured temperature rise curves, thus obtaining the optimal entropy-thermal correction coefficient. Finally, the determined entropy-thermal correction coefficient is multiplied with the entropy-thermal coefficient of the large battery to obtain the optimized entropy-thermal coefficient of the large battery, thereby improving the accuracy of subsequent heat generation power calculation.

[0066] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0067] Please refer to the following. Figure 3 , Figure 3 A schematic diagram of a large battery entropy thermal coefficient prediction device based on a pouch cell provided in an embodiment of this specification is shown. It should be noted that... Figure 3 The large battery entropy thermal coefficient prediction device based on pouch cells shown is used to perform the functions described in this application. Figure 1 The methods shown in the embodiments are for illustrative purposes only, illustrating the parts relevant to the embodiments of this application. For specific technical details not disclosed, please refer to this application. Figure 1 The example shown.

[0068] like Figure 3 As shown, the large battery entropy thermal coefficient prediction device based on pouch cells may include at least:

[0069] Module 301 is used to determine the test soft-pack battery corresponding to the target large battery according to the battery type system;

[0070] Test module 302 is used to determine the entropy thermal coefficient of the test soft pack battery under each state of charge based on the open circuit voltage test method.

[0071] The calculation module 303 is used to determine the entropy-thermal coefficient of the target large battery under each battery charge state based on the battery current ratio, battery volume ratio, and the entropy-thermal coefficient of each of the soft-pack batteries. The battery current ratio is determined by the target large battery current parameters and the test soft-pack battery current parameters, and the battery volume ratio is determined by the target large battery volume parameters and the test soft-pack battery volume parameters.

[0072] In one possible implementation, the determining module 301 is specifically used for:

[0073] Determine the target category system corresponding to the target large battery;

[0074] The test pouch battery corresponding to the target category system is determined based on the category database.

[0075] In one possible implementation, the test module 302 is specifically used for:

[0076] For any given state of charge of a battery, determine the test open-circuit voltage corresponding to different test ambient temperatures;

[0077] The entropy-thermal coefficient of the test pouch battery under the battery's state of charge is determined based on the test ambient temperature and the corresponding open-circuit voltage.

[0078] In one possible implementation, the test module 302 is further configured to:

[0079] Based on the curve fitting method, test voltage-temperature fitting curves corresponding to each of the test environment temperatures and each of the test open-circuit voltages are constructed;

[0080] Calculate the slope of the test voltage-temperature fitting curve to obtain the entropy-thermal coefficient of the test pouch battery under the battery's charged state.

[0081] In one possible implementation, the computing module 303 is specifically used for:

[0082] The ratio of the test soft-pack battery current parameter to the target large battery current parameter is calculated to obtain the battery current ratio.

[0083] The battery volume ratio is obtained by calculating the ratio between the target large battery volume parameters and the test soft-pack battery volume parameters.

[0084] In one possible implementation, the computing module 303 is further configured to:

[0085] The ratio of battery current and the ratio of battery volume are multiplied to obtain the product result.

[0086] The entropy-thermal coefficients of each soft-pack battery are multiplied together to obtain the entropy-thermal coefficients of the target large battery under each battery's state of charge.

[0087] In one possible implementation, the computing module 303 is further configured to:

[0088] For any of the battery states of charge, the simulated temperature rise curve and the measured temperature rise curve corresponding to the entropy thermal coefficient of the large battery are determined based on the heat generation temperature rise test method.

[0089] The entropy-heat correction coefficient is determined based on the simulated temperature rise curve and the measured temperature rise curve.

[0090] The entropy-heat coefficient of the large battery is corrected according to the entropy-heat correction coefficient to obtain the optimized entropy-heat coefficient of the large battery.

[0091] Those skilled in the art will clearly understand that the technical solutions of the embodiments of this application can be implemented by means of software and / or hardware. In this specification, "unit" and "module" refer to software and / or hardware that can independently complete or cooperate with other components to complete a specific function, wherein the hardware may be, for example, a field-programmable gate array (FPGA), an integrated circuit (IC), etc.

[0092] Each processing unit and / or module in the embodiments of this application can be implemented by an analog circuit that implements the functions described in the embodiments of this application, or by software that executes the functions described in the embodiments of this application.

[0093] Please refer to the following. Figure 4 , Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification is shown.

[0094] like Figure 4 As shown, the electronic device 400 may include: at least one device processor 401, at least one network interface 404, user interface 403, memory 405, and at least one communication bus 402.

[0095] The communication bus 402 can be used to realize the connection and communication of the above components.

[0096] The user interface 403 may include buttons, and the optional user interface may also include a standard wired interface or a wireless interface.

[0097] Among them, network interface 404 may include, but is not limited to, Bluetooth module, NFC module, Wi-Fi module, etc.

[0098] The device processor 401 may include one or more processing cores. The device processor 401 connects to various parts within the electronic device 400 using various interfaces and lines. It executes various functions and processes data of the electronic device 400 by running or executing instructions, programs, code sets, or instruction sets stored in the memory 405, and by calling data stored in the memory 405. Optionally, the device processor 401 may be implemented using at least one hardware form of DSP, FPGA, or PLA. The device processor 401 may integrate one or more of the following: CPU, GPU, and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the device processor 401 and may be implemented as a separate chip.

[0099] The memory 405 may include RAM or ROM. Optionally, the memory 405 may include a non-transitory computer-readable medium. The memory 405 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 405 may also be at least one storage device located remotely from the aforementioned device processor 401. Figure 4 As shown, the memory 405, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and program instructions.

[0100] Specifically, the device processor 401 can be used to call the large battery entropy thermal coefficient prediction application based on pouch batteries stored in the memory 405, and specifically perform the following operations:

[0101] The test pouch cell corresponding to the target large battery is determined based on the battery type system.

[0102] The entropy thermal coefficient of the test soft-pack battery under each state of charge was determined based on the open-circuit voltage test method.

[0103] The entropy-thermal coefficients of the target large battery under each battery state of charge are determined based on the battery current ratio, battery volume ratio, and the entropy-thermal coefficients of each pouch cell. The battery current ratio is determined by the target large battery current parameters and the test pouch cell current parameters, and the battery volume ratio is determined by the target large battery volume parameters and the test pouch cell volume parameters.

[0104] As an optional embodiment of this specification, the step of determining the test pouch battery corresponding to the target large battery according to the battery type system includes:

[0105] Determine the target category system corresponding to the target large battery;

[0106] The test pouch battery corresponding to the target category system is determined based on the category database.

[0107] As an optional embodiment of this specification, the determination of the entropy thermal coefficient of the test pouch battery under each state of charge based on the open-circuit voltage test method includes:

[0108] For any given state of charge of a battery, determine the test open-circuit voltage corresponding to different test ambient temperatures;

[0109] The entropy-thermal coefficient of the test pouch battery under the battery's state of charge is determined based on the test ambient temperature and the corresponding open-circuit voltage.

[0110] As an optional embodiment of this specification, determining the entropy-thermal coefficient of the test pouch battery corresponding to the battery's state of charge based on each of the test ambient temperatures and the test open-circuit voltage corresponding to each of the test ambient temperatures includes:

[0111] Based on the curve fitting method, test voltage-temperature fitting curves corresponding to each of the test environment temperatures and each of the test open-circuit voltages are constructed;

[0112] Calculate the slope of the test voltage-temperature fitting curve to obtain the entropy-thermal coefficient of the test pouch battery under the battery's charged state.

[0113] As an optional embodiment of this specification, before determining the entropy-thermal coefficient of the target large battery under each battery state of charge based on the battery current ratio, battery volume ratio, and the entropy-thermal coefficient of each of the pouch cells, the method further includes:

[0114] The ratio of the test soft-pack battery current parameter to the target large battery current parameter is calculated to obtain the battery current ratio.

[0115] The battery volume ratio is obtained by calculating the ratio between the target large battery volume parameters and the test soft-pack battery volume parameters.

[0116] As an optional embodiment of this specification, determining the entropy-thermal coefficient of the target large battery under each battery state of charge based on the battery current ratio, battery volume ratio, and the entropy-thermal coefficient of each of the pouch cells includes:

[0117] The ratio of battery current and the ratio of battery volume are multiplied to obtain the product result.

[0118] The entropy-thermal coefficients of each soft-pack battery are multiplied together to obtain the entropy-thermal coefficients of the target large battery under each battery's state of charge.

[0119] As an optional embodiment of this specification, the method further includes:

[0120] For any of the battery states of charge, the simulated temperature rise curve and the measured temperature rise curve corresponding to the entropy thermal coefficient of the large battery are determined based on the heat generation temperature rise test method.

[0121] The entropy-heat correction coefficient is determined based on the simulated temperature rise curve and the measured temperature rise curve.

[0122] The entropy-heat coefficient of the large battery is corrected according to the entropy-heat correction coefficient to obtain the optimized entropy-heat coefficient of the large battery.

[0123] This specification also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0124] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0125] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0126] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0127] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0128] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0129] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0130] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0131] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

Claims

1. A method for predicting the entropy thermal coefficient of a large battery based on a pouch cell, characterized in that, The method includes: The test pouch cell corresponding to the target large battery is determined based on the battery type system. The entropy thermal coefficient of the test soft-pack battery under each state of charge was determined based on the open-circuit voltage test method. The entropy-thermal coefficients of the target large battery under each battery state of charge are determined based on the battery current ratio, battery volume ratio, and the entropy-thermal coefficients of each pouch cell. The battery current ratio is determined by the target large battery current parameters and the test pouch cell current parameters, and the battery volume ratio is determined by the target large battery volume parameters and the test pouch cell volume parameters.

2. The method according to claim 1, characterized in that, The step of determining the test pouch cell corresponding to the target large battery based on the battery type system includes: Determine the target category system corresponding to the target large battery; The test pouch battery corresponding to the target category system is determined based on the category database.

3. The method according to claim 1, characterized in that, The determination of the entropy thermal coefficient of the test pouch battery under each state of charge based on the open-circuit voltage test method includes: For any given state of charge of a battery, determine the test open-circuit voltage corresponding to different test ambient temperatures; The entropy-thermal coefficient of the test pouch battery under the battery's state of charge is determined based on the test ambient temperature and the corresponding open-circuit voltage.

4. The method according to claim 3, characterized in that, The determination of the entropy-thermal coefficient of the test pouch battery under the battery's state of charge, based on the test ambient temperature and the corresponding open-circuit voltage, includes: Based on the curve fitting method, test voltage-temperature fitting curves corresponding to each of the test environment temperatures and each of the test open-circuit voltages are constructed; Calculate the slope of the test voltage-temperature fitting curve to obtain the entropy-thermal coefficient of the test pouch battery under the battery's charged state.

5. The method according to claim 1, characterized in that, Before determining the entropy-thermal coefficient of the target large battery under each battery state of charge based on the battery current ratio, battery volume ratio, and the entropy-thermal coefficient of each pouch cell, the method further includes: The ratio of the test soft-pack battery current parameter to the target large battery current parameter is calculated to obtain the battery current ratio. The battery volume ratio is obtained by calculating the ratio between the target large battery volume parameters and the test soft-pack battery volume parameters.

6. The method according to claim 1, characterized in that, The step of determining the entropy-thermal coefficient of the target large battery under each battery state of charge based on the battery current ratio, battery volume ratio, and the entropy-thermal coefficient of each pouch cell includes: The ratio of battery current and the ratio of battery volume are multiplied to obtain the product result. The entropy-thermal coefficients of each soft-pack battery are multiplied together to obtain the entropy-thermal coefficients of the target large battery under each battery's state of charge.

7. The method according to claim 1, characterized in that, The method further includes: For any of the battery states of charge, the simulated temperature rise curve and the measured temperature rise curve corresponding to the entropy thermal coefficient of the large battery are determined based on the heat generation temperature rise test method. The entropy-heat correction coefficient is determined based on the simulated temperature rise curve and the measured temperature rise curve. The entropy-heat coefficient of the large battery is corrected according to the entropy-heat correction coefficient to obtain the optimized entropy-heat coefficient of the large battery.

8. A device for predicting the entropy thermal coefficient of a large battery based on a pouch cell, characterized in that, The device includes: The determination module is used to determine the test pouch cell corresponding to the target large battery based on the battery type system. The test module is used to determine the entropy thermal coefficient of the test pouch battery under each state of charge, based on the open-circuit voltage test method. The calculation module is used to determine the entropy-thermal coefficient of the target large battery under each battery state of charge based on the battery current ratio, battery volume ratio, and the entropy-thermal coefficient of each of the pouch cells. The battery current ratio is determined by the target large battery current parameters and the test pouch cell current parameters, and the battery volume ratio is determined by the target large battery volume parameters and the test pouch cell volume parameters.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, the computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform the steps of the method as claimed in any one of claims 1-7.

Images