Battery model building method under high-frequency charging and discharging working conditions

Abstract

The embodiment of the invention provides a method for establishing a battery model under a high-frequency charging and discharging working condition. The method is applied to automobile battery development. The method comprises the following steps: respectively carrying out charging and discharging tests on an automobile battery under an ideal working condition and a high-frequency charging and discharging working condition, and obtaining the maximum temperature rise of the battery under the two working conditions in the test process and the corresponding battery capacity after the test is finished; fitting the relationship between the maximum temperature rise of the battery under the two working conditions, and obtaining the nonlinear relationship of the maximum temperature rise of the battery; fitting the relationship between the battery capacities under the two working conditions to obtain the nonlinear relationship of the battery capacities; fusing the battery maximum temperature rise nonlinear relation and the battery capacity nonlinear relation into the basic battery model to obtain a pre-trained battery model; and training the pre-training battery model by taking the current data under the existing high-frequency charging and discharging working condition as a training sample and taking the output voltage under the existing high-frequency charging and discharging working condition as a label, and taking the trained pre-training battery model as a final battery model. Therefore, the state of the lithium ion battery is accurately estimated.

Description

Technical Field

[0001] The present disclosure relates to the field of automobile battery development, and in particular to a method for establishing a battery model under high-frequency charging and discharging conditions. Background Art

[0002] With the improvement of global environmental awareness and the support of various countries' policies for renewable energy, the electric vehicle industry is in a period of rapid growth. In the core power system of electric vehicles, lithium batteries play a key role. Their advantages include high energy density, light weight, and long cycle life, which can provide electric vehicles with longer driving range and higher energy efficiency. However, lithium batteries also have some shortcomings, such as high cost and unstable performance in extreme environments. As a result, the safety of lithium batteries has also aroused concern, especially under high-frequency charging and discharging conditions, which are prone to overcharging and discharging, increased internal resistance due to aging, or mechanical damage, which may lead to safety accidents such as thermal runaway or fire. Therefore, it is of great practical significance to accurately monitor the status of lithium-ion batteries under high-frequency charging and discharging conditions. Summary of the invention

[0003] The present invention provides a method and device for establishing a battery model under high-frequency charging and discharging conditions, a device and a storage medium.

[0004] According to a first aspect of the present disclosure, a method for establishing a battery model under high-frequency charging and discharging conditions is provided. The method comprises: Carry out charge and discharge tests on the car battery under ideal working conditions and high-frequency charge and discharge working conditions, respectively, and obtain the maximum temperature rise of the battery during the test and the corresponding battery capacity after the test under the two working conditions; Fit the relationship between the maximum temperature rise of the battery under ideal working conditions and high-frequency charging and discharging conditions to obtain the nonlinear relationship of the maximum temperature rise of the battery; Fit the relationship between battery capacity under ideal working conditions and high-frequency charging and discharging conditions to obtain the nonlinear relationship of battery capacity; Integrate the nonlinear relationship of the maximum temperature rise of the battery and the nonlinear relationship of the battery capacity into the basic battery model to obtain a pre-trained battery model; The current data under the existing high-frequency charging and discharging conditions is used as a training sample, and the output voltage under the existing high-frequency charging and discharging conditions is used as a label to train the pre-trained battery model, and the trained pre-trained battery model is used as the final battery model.

[0005] In some implementations of the first aspect, using the trained pre-trained battery model as the final battery model includes: Train the pre-trained battery model and calculate the difference between the voltage output by the pre-trained battery model and the output voltage under the existing high-frequency charging and discharging conditions; adjusting model parameters of the pre-trained battery model according to the gap value, and re-inputting the adjusted model parameters into the pre-trained battery model; Repeat the training of the pre-trained battery model, calculation of the gap value, adjustment of the model parameters, and re-entry of the model parameters until the gap value is less than a preset threshold, and use the pre-trained battery model with a gap value less than the preset threshold as the final battery model.

[0006] In some implementations of the first aspect, the basic battery model is an electrochemical-thermal-aging coupling model under ideal operating conditions; wherein, The electrochemical-thermal-aging coupled model includes the battery thermal model and the battery aging model.

[0007] In some implementations of the first aspect, the battery thermal model is constructed by the following energy conservation equation: in, represents the density of battery materials, is the heat capacity at constant pressure, T is the temperature, k is the thermal conductivity, Q is the total heat production.

[0008] In some implementations of the first aspect, the total heat generation includes irreversible ohmic heat generation, irreversible polarization heat generation and reversible electrochemical heat generation; wherein, Irreversible ohmic heat generation is calculated based on the electrical heat generated in the solid phase and the ionic heat generated in the electrolyte; The irreversible polarization heat generation is calculated based on the specific surface area of ​​active particles, local current density and overpotential; The reversible electrochemical heat generation is calculated based on the specific surface area of ​​the active particles, the local current density, and the equilibrium potential of the porous electrode reaction.

[0009] In some implementations of the first aspect, the battery aging model is constructed based on the current density of the battery lithium plating side reaction, the current density of the battery solid electrolyte membrane generation side reaction, the surface film thickness of the battery side reaction products, and the resistivity of the battery solid electrolyte membrane and the lithium plating layer.

[0010] According to a second aspect of the present disclosure, a device for establishing a battery model under high-frequency charging and discharging conditions is provided. The device comprises: The test data acquisition module is used to perform charge and discharge tests on the vehicle battery under ideal working conditions and high-frequency charge and discharge working conditions, respectively, to obtain the maximum temperature rise of the battery during the test and the corresponding battery capacity after the test under the two working conditions; The data fitting module is used to fit the relationship between the maximum temperature rise of the battery under ideal working conditions and high-frequency charging and discharging conditions, and obtain the nonlinear relationship of the maximum temperature rise of the battery; It is also used to fit the relationship between battery capacity under ideal working conditions and high-frequency charging and discharging conditions, and obtain the nonlinear relationship of battery capacity; A pre-trained battery model acquisition module is used to integrate the nonlinear relationship of the maximum temperature rise of the battery and the nonlinear relationship of the battery capacity into the basic battery model to obtain a pre-trained battery model; The battery model building module is used to train the pre-trained battery model using the current data under the existing high-frequency charging and discharging conditions as training samples and the output voltage under the existing high-frequency charging and discharging conditions as annotations, and use the trained pre-trained battery model as the final battery model.

[0011] According to a third aspect of the present disclosure, an electronic device is provided. The electronic device includes: at least one processor; and a memory connected to the at least one processor in communication; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the method described above.

[0012] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, where the computer instructions are used to cause a computer to execute the method as described above.

[0013] In the present disclosure, the car battery is charged and discharged under ideal working conditions and high-frequency charging and discharging working conditions, respectively, and the maximum temperature rise of the battery during the test and the corresponding battery capacity after the test are obtained under the two working conditions; the relationship between the maximum temperature rise of the battery under the ideal working condition and the high-frequency charging and discharging working condition is fitted to obtain the nonlinear relationship of the maximum temperature rise of the battery; the relationship between the battery capacity under the ideal working condition and the high-frequency charging and discharging working condition is fitted to obtain the nonlinear relationship of the battery capacity; the nonlinear relationship of the maximum temperature rise of the battery and the nonlinear relationship of the battery capacity are integrated into the basic battery model to obtain a pre-trained battery model; the current data under the existing high-frequency charging and discharging working condition is used as the training sample, and the output voltage under the existing high-frequency charging and discharging working condition is used as the annotation to train the pre-trained battery model, and the trained pre-trained battery model is used as the final battery model. In this way, the state of the lithium-ion battery is accurately estimated under the high-frequency charging and discharging working condition.

[0014] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which: Figure 1 A flow chart of a method for establishing a battery model under high-frequency charging and discharging conditions provided by an embodiment of the present disclosure is shown; Figure 2 A nonlinear relationship curve diagram of the maximum temperature rise of a battery provided by an embodiment of the present disclosure is shown; Figure 3 A fitting curve diagram of the relationship between the battery health status under the ideal working condition and the high-frequency charging and discharging working condition provided by the embodiment of the present disclosure is shown; Figure 4 A structural diagram of a battery model establishment device under high-frequency charging and discharging conditions provided by an embodiment of the present disclosure is shown; Figure 5 A block diagram of an exemplary electronic device capable of implementing an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0017] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0018] In response to the problems in the background technology, the embodiments of the present disclosure provide a method for establishing a battery model under high-frequency charging and discharging conditions. Specifically, the automobile battery is charged and discharged under ideal conditions and high-frequency charging and discharging conditions, respectively, and the maximum temperature rise of the battery during the test and the corresponding battery capacity after the test are obtained under the two conditions; the relationship between the maximum temperature rise of the battery under the ideal condition and the high-frequency charging and discharging condition is fitted to obtain the nonlinear relationship of the maximum temperature rise of the battery; the relationship between the battery capacity under the ideal condition and the high-frequency charging and discharging condition is fitted to obtain the nonlinear relationship of the battery capacity; the nonlinear relationship of the maximum temperature rise of the battery and the nonlinear relationship of the battery capacity are integrated into the basic battery model to obtain a pre-trained battery model; the current data under the existing high-frequency charging and discharging condition is used as the training sample, and the output voltage under the existing high-frequency charging and discharging condition is used as the annotation, and the pre-trained battery model is trained, and the trained pre-trained battery model is used as the final battery model. In this way, the state of the lithium-ion battery can be accurately estimated under high-frequency charging and discharging conditions.

[0019] The method for establishing a battery model under high-frequency charging and discharging conditions provided by the embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings through specific embodiments.

[0020] Figure 1 A flow chart of a method for establishing a battery model under high-frequency charging and discharging conditions provided by an embodiment of the present disclosure is shown. The method 100 includes the following steps: S110, performing charge and discharge tests on the vehicle battery under ideal working conditions and high-frequency charge and discharge working conditions, respectively, to obtain the maximum temperature rise of the battery during the test and the corresponding battery capacity after the test under the two working conditions.

[0021] In some embodiments, under ideal operating conditions, the charge and discharge curve of lithium-ion batteries is relatively flat, the temperature change is small, and the battery energy density is high. Selecting appropriate charging and discharging cycles can avoid excessive charging and discharging, effectively extending the battery life.

[0022] In some embodiments, the constant current condition is used as the ideal condition, and the standardized automobile test condition (UDDS) is used as the high-frequency charging and discharging condition. The standardized automobile test condition (UDDS) can simulate typical conditions of urban driving, including frequent acceleration, deceleration and parking, covering a variety of speed changes, and belongs to the high-frequency charging and discharging condition; During the same period of time, the battery is charged and discharged for one day under constant current conditions and UDDS conditions respectively. After the test, the battery is left to stand for a long time to obtain the maximum temperature rise of the battery under the two conditions during the test and the battery capacity corresponding to the two conditions after the test. This test process is repeated multiple times to obtain the maximum temperature rise of the battery under the two conditions during the test and the battery capacity corresponding to the two conditions after the test in one week. Furthermore, when the battery is charged and discharged under constant current conditions and UDDS conditions respectively, the amount of electricity converted by the battery under constant current conditions and UDDS conditions should remain the same within the same time.

[0023] In some embodiments, compared with the constant current condition, the maximum temperature rise and battery capacity of the battery are higher under the UDDS condition.

[0024] S120, fitting the relationship between the maximum temperature rise of the battery under the ideal working condition and the high-frequency charging and discharging working condition to obtain the nonlinear relationship of the maximum temperature rise of the battery; fitting the relationship between the battery capacity under the ideal working condition and the high-frequency charging and discharging working condition to obtain the nonlinear relationship of the battery capacity.

[0025] In some embodiments, the relationship between the maximum temperature rise of the battery during the test under the ideal working condition and the high-frequency charging and discharging working condition in a week is fitted to obtain a nonlinear relationship curve of the maximum temperature rise of the battery, such as Figure 2 As shown, the horizontal axis represents the maximum temperature rise of the battery under ideal working conditions, and the vertical axis represents the maximum temperature rise of the battery under high-frequency charging and discharging conditions. The units areo C.

[0026] In some embodiments, the relationship between the battery capacity under ideal operating conditions and high-frequency charging and discharging operating conditions after the test is completed in one week is fitted to obtain a nonlinear relationship of the battery capacity; Furthermore, according to the battery capacity in the ideal working condition and the high-frequency charging and discharging working condition after the test, the battery health state under the corresponding working condition is calculated, and the battery health state corresponding to the ideal working condition and the high-frequency charging and discharging working condition is fitted to obtain the battery health state relationship fitting curve diagram, such as Figure 3 As shown, the horizontal axis represents the battery health status under ideal working conditions, and the vertical axis represents the battery health status under high-frequency charging and discharging conditions; By fitting the nonlinear relationship of battery capacity or battery health status, the relationship between battery life under ideal operating conditions and high-frequency charging and discharging conditions can be evaluated.

[0027] S130, integrating the nonlinear relationship of the maximum temperature rise of the battery and the nonlinear relationship of the battery capacity into the basic battery model to obtain a pre-trained battery model.

[0028] In some embodiments, the nonlinear relationship between the maximum temperature rise of the battery and the battery health status relationship fitting curve are integrated into the basic battery model to obtain a pre-trained battery model.

[0029] In some embodiments, the basic battery model is an electrochemical-thermal-aging coupling model under ideal operating conditions; wherein, The electrochemical-thermal-aging coupled model includes the battery thermal model and the battery aging model.

[0030] In some embodiments, the battery thermal model is constructed by the following energy conservation equation: in, represents the density of battery materials, is the heat capacity at constant pressure, T is the temperature, k is the thermal conductivity, Q is the total heat production.

[0031] In some embodiments, the total heat generation includes irreversible ohmic heat generation, irreversible polarization heat generation, and reversible electrochemical heat generation; wherein, Irreversible ohmic heat generation is calculated based on the electrical heat generated in the solid phase and the ionic heat generated in the electrolyte; The irreversible polarization heat generation is calculated based on the specific surface area of ​​active particles, local current density and overpotential; Reversible electrochemical heat generation is calculated based on the specific surface area of ​​active particles, local current density and the equilibrium potential of the porous electrode reaction; Furthermore, irreversible ohmic heating The calculation formula is as follows: is the current value in the solid phase, is the voltage difference in the solid phase, is the current value in the electrolyte, is the voltage difference in the electrolyte; Irreversible polarization heating The calculation formula is as follows: represents the specific surface area of ​​active particles, is the local current density, is the overpotential; Reversible electrochemical heat generation The calculation formula is as follows: is the equilibrium potential of the porous electrode reaction.

[0032] In some embodiments, the battery aging model is constructed based on the current density of the battery lithium plating side reaction, the current density of the battery solid electrolyte membrane generation side reaction, the surface film thickness of the battery side reaction product, and the resistivity of the battery solid electrolyte membrane and the lithium plating layer.

[0033] In some embodiments, the current density of the battery lithium plating side reaction is Calculate according to the following formula: in, represents the initial current density of the battery lithium plating side reaction, , are the transfer coefficients during lithium ion migration and adsorption and lithium metal deposition, respectively. is the overpotential of the battery's lithium plating side reaction, R, F are the ideal gas constant and Faraday's constant respectively.

[0034] In some embodiments, the current density of the battery solid electrolyte membrane (SEI membrane) generating side reactions Calculate according to the following formula: in, is the initial current density of the side reaction generated by the battery solid electrolyte membrane, is the transfer coefficient of the side reaction generated by the battery solid electrolyte membrane, , are the solid and liquid phase points, respectively. is the equilibrium potential, , are the current density through the solid electrolyte membrane and the resistivity of the solid electrolyte membrane, respectively.

[0035] In some embodiments, the surface film thickness of the battery side reaction product is calculated from the conductivity of the solid electrolyte membrane; Furthermore, the conductivity of the solid electrolyte membrane Calculate according to the following formula: in, , are the concentrations of by-products generated by the SEI film reaction and the concentrations of by-products generated by the lithium plating reaction, , are the molar mass of the SEI film and the molar mass of the lithium plating layer, , are the density of SEI film and the density of lithium plating layer respectively.

[0036] In some embodiments, the resistivity of the battery solid electrolyte membrane is The calculation formula is as follows: in, is the initial conductivity of the solid electrolyte membrane; Resistivity of lithium plating The calculation formula is as follows: in, is the electrical conductivity of the solid electrolyte.

[0037] S140, using the current data under the existing high-frequency charging and discharging conditions as training samples, and the output voltage under the existing high-frequency charging and discharging conditions as annotations, the pre-trained battery model is trained, and the trained pre-trained battery model is used as the final battery model.

[0038] In some embodiments, the trained pre-trained battery model is used as the final battery model, including: Train the pre-trained battery model and calculate the difference between the voltage output by the pre-trained battery model and the output voltage under the existing high-frequency charging and discharging conditions; adjusting model parameters of the pre-trained battery model according to the gap value, and re-inputting the adjusted model parameters into the pre-trained battery model; Repeat the training of the pre-trained battery model, calculation of the gap value, adjustment of the model parameters, and re-entry of the model parameters until the gap value is less than a preset threshold, and use the pre-trained battery model with a gap value less than the preset threshold as the final battery model.

[0039] In some embodiments, the current data under high-frequency charging and discharging conditions is input into the final battery model, and the state of the lithium-ion battery can be obtained through the battery model, for example, the temperature change of the lithium-ion battery, the battery capacity, etc. According to the obtained battery state, the battery health management, fault diagnosis and other operations are performed to improve the battery working efficiency and life.

[0040] According to the embodiments of the present disclosure, the automobile battery is charged and discharged under the ideal working condition and the high-frequency charging and discharging working condition respectively, and the maximum temperature rise of the battery in the test process and the corresponding battery capacity after the test are obtained under the two working conditions; the relationship between the maximum temperature rise of the battery under the ideal working condition and the high-frequency charging and discharging working condition is fitted to obtain the nonlinear relationship of the maximum temperature rise of the battery; the relationship between the battery capacity under the ideal working condition and the high-frequency charging and discharging working condition is fitted to obtain the nonlinear relationship of the battery capacity; the nonlinear relationship of the maximum temperature rise of the battery and the nonlinear relationship of the battery capacity are integrated into the basic battery model to obtain the pre-trained battery model; the current data under the existing high-frequency charging and discharging working condition is used as the training sample, and the output voltage under the existing high-frequency charging and discharging working condition is used as the annotation, and the pre-trained battery model is trained, and the trained pre-trained battery model is used as the final battery model. In this way, the state of the lithium-ion battery is accurately estimated under the high-frequency charging and discharging working condition.

[0041] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0042] The above is an introduction to the method embodiment. The following is a further explanation of the scheme disclosed in the present invention through an apparatus embodiment.

[0043] Figure 4 The structure diagram of a battery model establishment device under high-frequency charging and discharging conditions provided by an embodiment of the present disclosure is shown. The device 400 includes: The test data acquisition module 410 is used to perform charge and discharge tests on the vehicle battery under ideal working conditions and high-frequency charge and discharge working conditions, respectively, to obtain the maximum temperature rise of the battery during the test and the corresponding battery capacity after the test under the two working conditions.

[0044] The data fitting module 420 is used to fit the relationship between the maximum temperature rise of the battery under ideal working conditions and high-frequency charging and discharging conditions to obtain the nonlinear relationship of the maximum temperature rise of the battery; it is also used to fit the relationship between the battery capacity under ideal working conditions and high-frequency charging and discharging conditions to obtain the nonlinear relationship of the battery capacity.

[0045] The pre-trained battery model acquisition module 430 is used to integrate the nonlinear relationship of the maximum temperature rise of the battery and the nonlinear relationship of the battery capacity into the basic battery model to obtain the pre-trained battery model.

[0046] In some embodiments, module 430 is specifically configured to: The basic battery model is an electrochemical-thermal-aging coupling model under ideal working conditions; The electrochemical-thermal-aging coupled model includes the battery thermal model and the battery aging model.

[0047] In some embodiments, module 430 is further configured to: The battery thermal model is constructed using the following energy conservation equations: in, represents the density of battery materials, is the heat capacity at constant pressure, T is the temperature, k is the thermal conductivity, Q is the total heat production.

[0048] In some embodiments, module 430 is further configured to: The total heat generation includes irreversible ohmic heat generation, irreversible polarization heat generation and reversible electrochemical heat generation; among them, Irreversible ohmic heat generation is calculated based on the electrical heat generated in the solid phase and the ionic heat generated in the electrolyte; The irreversible polarization heat generation is calculated based on the specific surface area of ​​active particles, local current density and overpotential; The reversible electrochemical heat generation is calculated based on the specific surface area of ​​the active particles, the local current density, and the equilibrium potential of the porous electrode reaction.

[0049] In some embodiments, module 430 is further configured to: The battery aging model is constructed based on the current density of the battery lithium plating side reaction, the current density of the battery solid electrolyte membrane generation side reaction, the surface film thickness of the battery side reaction products, and the resistivity of the battery solid electrolyte membrane and lithium plating layer.

[0050] The battery model establishment module 440 is used to train the pre-trained battery model using the current data under the existing high-frequency charging and discharging conditions as training samples and the output voltage under the existing high-frequency charging and discharging conditions as labels, and use the trained pre-trained battery model as the final battery model.

[0051] In some embodiments, module 440 is specifically configured to: The trained pre-trained battery model is used as the final battery model, including: Train the pre-trained battery model and calculate the difference between the voltage output by the pre-trained battery model and the output voltage under the existing high-frequency charging and discharging conditions; adjusting model parameters of the pre-trained battery model according to the gap value, and re-inputting the adjusted model parameters into the pre-trained battery model; Repeat the training of the pre-trained battery model, calculation of the gap value, adjustment of the model parameters, and re-entry of the model parameters until the gap value is less than a preset threshold, and use the pre-trained battery model with a gap value less than the preset threshold as the final battery model.

[0052] Understandably, Figure 4 Each module / unit in the illustrated device 400 has the function of implementing each step in the method 100 provided in the embodiment of the present disclosure and can achieve its corresponding technical effect, which will not be described in detail here for the sake of brevity.

[0053] Figure 5 A block diagram of an exemplary electronic device capable of implementing an embodiment of the present disclosure is shown. Electronic device 500 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 500 may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0054] like Figure 5 As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 to a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An I / O interface 505 is also connected to the bus 504.

[0055] Multiple components in the electronic device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the electronic device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0056] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 501 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the method 100 in any other appropriate manner (e.g., by means of firmware).

[0057] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0058] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0059] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0060] It should be noted that the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute method 100 and achieve the corresponding technical effect achieved by executing the method in the embodiment of the present disclosure. For the sake of concise description, they are not repeated here.

[0061] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0062] The systems and techniques described herein can be implemented in a computing system that includes a backend component (e.g., as a data server), or a computing system that includes a middleware component (e.g., an application server), or a computing system that includes a frontend component (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with the systems and techniques described herein). The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0063] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0064] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0065] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for establishing a battery model under high-frequency charging and discharging conditions, characterized in that: include: Carry out charge and discharge tests on the car battery under ideal working conditions and high-frequency charge and discharge working conditions, respectively, and obtain the maximum temperature rise of the battery during the test and the corresponding battery capacity after the test under the two working conditions; Fit the relationship between the maximum temperature rise of the battery under ideal working conditions and high-frequency charging and discharging conditions to obtain the nonlinear relationship of the maximum temperature rise of the battery; Fit the relationship between battery capacity under ideal working conditions and high-frequency charging and discharging conditions to obtain the nonlinear relationship of battery capacity; Integrate the nonlinear relationship of the maximum temperature rise of the battery and the nonlinear relationship of the battery capacity into a basic battery model to obtain a pre-trained battery model; The pre-trained battery model is trained using the current data under the existing high-frequency charging and discharging conditions as training samples and the output voltage under the existing high-frequency charging and discharging conditions as annotations, and the trained pre-trained battery model is used as the final battery model.

2. The method according to claim 1, characterized in that The method of using the trained pre-trained battery model as the final battery model includes: Training the pre-trained battery model, and calculating the difference between the voltage output by the pre-trained battery model and the output voltage under the existing high-frequency charging and discharging conditions; Adjusting model parameters of the pre-trained battery model according to the gap value, and re-inputting the adjusted model parameters into the pre-trained battery model; Repeat the training of the pre-trained battery model, calculation of the gap value, adjustment of the model parameters, and re-entry of the model parameters until the gap value is less than a preset threshold, and use the pre-trained battery model with a gap value less than the preset threshold as the final battery model.

3. The method according to claim 1, characterized in that The basic battery model is an electrochemical-thermal-aging coupling model under ideal working conditions; The electrochemical-thermal-aging coupling model includes a battery thermal model and a battery aging model.

4. The method according to claim 3, characterized in that The battery thermal model is constructed by the following energy conservation equation: in, represents the density of battery materials, is the heat capacity at constant pressure, T is the temperature, k is the thermal conductivity, Q is the total heat production.

5. The method according to claim 4, characterized in that The total heat generation includes irreversible ohmic heat generation, irreversible polarization heat generation and reversible electrochemical heat generation; wherein, The irreversible ohmic heat generation is calculated based on the electric heat generated in the solid phase and the ionic heat generated in the electrolyte; The irreversible polarization heat generation is calculated based on the specific surface area of ​​the active particles, the local current density and the overpotential; The reversible electrochemical heat generation is calculated based on the specific surface area of ​​the active particles, the local current density and the equilibrium potential of the porous electrode reaction.

6. The method according to claim 3, characterized in that The battery aging model is constructed based on the current density of the battery lithium plating side reaction, the current density of the battery solid electrolyte membrane generation side reaction, the surface film thickness of the battery side reaction product, and the resistivity of the battery solid electrolyte membrane and the lithium plating layer.

7. A battery model establishment device under high-frequency charging and discharging conditions, characterized in that: include: The test data acquisition module is used to perform charge and discharge tests on the vehicle battery under ideal working conditions and high-frequency charge and discharge working conditions, respectively, to obtain the maximum temperature rise of the battery during the test and the corresponding battery capacity after the test under the two working conditions; The data fitting module is used to fit the relationship between the maximum temperature rise of the battery under ideal working conditions and high-frequency charging and discharging conditions, and obtain the nonlinear relationship of the maximum temperature rise of the battery; It is also used to fit the relationship between battery capacity under ideal working conditions and high-frequency charging and discharging conditions, and obtain the nonlinear relationship of battery capacity; A pre-trained battery model acquisition module, used to integrate the nonlinear relationship of the maximum temperature rise of the battery and the nonlinear relationship of the battery capacity into a basic battery model to acquire a pre-trained battery model; The battery model establishment module is used to train the pre-trained battery model using the current data under the existing high-frequency charging and discharging conditions as training samples and the output voltage under the existing high-frequency charging and discharging conditions as annotations, and use the trained pre-trained battery model as the final battery model.

8. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

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