Charge state calculation method and device, equipment and storage medium

Abstract

The invention is suitable for the technical field of electrical variable measurement, and provides a state of charge calculation method, device and equipment and a storage medium, and the state of charge calculation method comprises the steps: obtaining the real-time current, real-time voltage, real-time temperature, real-time charge-discharge rate and the state of charge at the beginning of charge-discharge of a target energy storage battery; based on the real-time current, the real-time voltage, the real-time temperature and the real-time charge-discharge rate of the target energy storage battery and the charge state at the beginning of charge-discharge, calculating a first charge state estimation value; and inputting the real-time current, the real-time voltage, the real-time temperature, the real-time charge-discharge rate and the first charge state estimation value of the target energy storage battery into a charge state calculation model, and determining the charge state of the target energy storage battery. According to the invention, the state of charge can be accurately calculated in the charging and discharging process.

Description

Technical Field

[0001] The present application belongs to the technical field of measuring electrical variables, and in particular, relates to a method, device, equipment and storage medium for calculating a state of charge. Background Art

[0002] The application of energy storage technology in multiple fields makes it critical to accurately estimate the battery state of charge (SOC). SOC is the core of the battery management system and affects battery efficiency, life and system reliability. Common estimation methods include the ampere-hour integration method, the open circuit voltage method and the Kalman filter method, but they all have limitations.

[0003] The ampere-hour integration method estimates SOC by integrating the battery charge and discharge current, but the estimation accuracy is limited due to the current measurement error. It does not take into account the change in battery coulomb efficiency and actual capacity, resulting in an increase in error over time, affecting the battery status judgment.

[0004] The open circuit voltage method requires the battery to remain stationary for a long time to obtain accurate data, but this is difficult to meet in practical applications, which limits its use and makes it impossible to accurately estimate SOC in real time.

[0005] Model-based methods such as equivalent circuit models combined with Kalman filtering improve estimation accuracy, but the accuracy of the model depends on the accuracy of battery parameters, which will change due to temperature changes, aging and other factors, affecting the SOC estimation results. Traditional models have limited ability to describe complex nonlinear relationships. Summary of the invention

[0006] The embodiments of the present application provide a state of charge calculation method, device, equipment and storage medium to solve the technical problem of inaccurate state of charge calculation during the charging and discharging process.

[0007] This application is implemented through the following technical solutions: In a first aspect, an embodiment of the present application provides a method for calculating a state of charge, including: Obtain the real-time current, real-time voltage, real-time temperature, real-time charge and discharge rate and state of charge when charging and discharging start of the target energy storage battery.

[0008] A first state of charge estimation value is calculated based on the real-time current, real-time voltage, real-time temperature, real-time charge and discharge rate, and the state of charge when charging and discharging start of the target energy storage battery.

[0009] The real-time current, real-time voltage, real-time temperature, real-time charge and discharge rate and first state of charge estimation value of the target energy storage battery are input into the state of charge calculation model to determine the state of charge of the target energy storage battery.

[0010] In combination with the first aspect, in some possible implementations, calculating a first state of charge estimation value based on the real-time current, real-time voltage, real-time temperature, real-time charge and discharge rate, and state of charge at the start of charging and discharging of the target energy storage battery includes: If the real-time current is less than 0, the target energy storage battery is in a charging state, and the first formula is used to calculate a first state of charge estimation value.

[0011] The first formula is:

[0012] in, represents a first state of charge estimation value, Indicates the rated capacity of the target energy storage battery, Indicates the start time of charging state. Indicates the end time of charging state. is the Coulomb efficiency, Indicates the state of charge when charging begins.

[0013] In combination with the first aspect, in some possible implementations, calculating a first state of charge estimation value based on the real-time current, real-time voltage, real-time temperature, real-time charge and discharge rate, and state of charge at the start of charging and discharging of the target energy storage battery includes: If the real-time current is greater than 0, the target energy storage battery is in a discharging state, and the second formula is used to calculate the first state of charge estimation value.

[0014] The second formula is:

[0015] in, represents a first state of charge estimation value, Indicates the maximum releasable capacity of the target energy storage battery. Indicates the start time of the discharge state, Indicates the end time of the discharge state, Indicates the state of charge when discharge begins.

[0016] In combination with the first aspect, in some possible implementations, obtaining the state of charge of the target energy storage battery when charging and discharging begins includes: The state of charge of the target energy storage battery at the beginning of charging and discharging is obtained by the open circuit voltage method.

[0017] In combination with the first aspect, in some possible implementations, obtaining the state of charge of the target energy storage battery at the beginning of charging and discharging by an open circuit voltage method includes: Get OCV-SOC curve cluster.

[0018] Based on the OCV-SOC curve cluster, the state of charge of the target energy storage battery at the beginning of charging and discharging is determined.

[0019] In combination with the first aspect, in some possible implementations, determining the state of charge of the target energy storage battery at the start of charging and discharging based on the OCV-SOC curve cluster includes: Get the target voltage of the target energy storage battery when charging and discharging starts.

[0020] Based on the target voltage, a corresponding second state of charge estimation value is found on each OCV-SOC curve in the OCV-SOC curve cluster.

[0021] An average of the plurality of second state-of-charge estimation values ​​is used as the state-of-charge of the target energy storage battery when charging and discharging begins.

[0022] In combination with the first aspect, in some possible implementations, the state of charge calculation model is a neural network model; the state of charge calculation model includes an input layer, a convolutional layer, a pooling layer, a fully connected layer and an output layer.

[0023] In a second aspect, an embodiment of the present application provides a charge state calculation device, including: The data acquisition module is used to obtain the real-time current, real-time voltage, real-time temperature, real-time charge and discharge rate and the state of charge when starting to charge and discharge of the target energy storage battery.

[0024] The data calculation module is used to calculate a first state of charge estimation value based on the real-time current, real-time voltage, real-time temperature, real-time charge and discharge rate and the state of charge when charging and discharging start of the target energy storage battery.

[0025] The result output module is used to input the real-time current, real-time voltage, real-time temperature, real-time charge and discharge rate and the first state of charge estimation value of the target energy storage battery into the state of charge calculation model to determine the state of charge of the target energy storage battery.

[0026] In a third aspect, an embodiment of the present application provides a terminal device, comprising: a processor and a memory, the memory being used to store a computer program, and the processor implementing the state of charge calculation method as described in any one of the first aspects when executing the computer program.

[0027] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for calculating the state of charge as described in any one of the first aspects is implemented.

[0028] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0029] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This application first collects the current, voltage, temperature, charge and discharge rate and initial state of charge of the target energy storage battery in real time. Subsequently, based on these data, we calculated a preliminary estimate of the state of charge, namely the first state of charge estimate. Then, this estimate is input into the state of charge calculation model together with the collected data, so as to obtain the accurate value of the state of charge during the charging and discharging process. The method of this scheme uses an improved formula based on the ampere-hour integration method when calculating the first state of charge estimate, and uses the open circuit voltage method when obtaining the initial state of charge. Finally, the preliminary estimate is input into the state of charge calculation model, which improves the accuracy of the estimate. When calculating the state of charge, the influence of factors such as temperature change on the state of charge is considered, and the state of charge during the charging and discharging process can be accurately calculated.

[0030] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0032] Figure 1 is a flow chart of a method for calculating a state of charge provided in an embodiment of the present application; Figure 2 is a structural schematic diagram of a charge state calculation device provided in an embodiment of the present application; Figure 3 It is a structural diagram of a terminal device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0033] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0034] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0035] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0036] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0037] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0038] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0039] The present application provides a method for calculating the state of charge. Figure 1 is a schematic diagram of the process of the charge state calculation method provided in an embodiment of the present application, referring to Figure 1 The detailed description of the charge state calculation method is as follows: Step 101, obtaining the real-time current, real-time voltage, real-time temperature, real-time charge and discharge rate and the state of charge when starting to charge and discharge of the target energy storage battery.

[0040] Exemplarily, obtaining the state of charge of the target energy storage battery when charging and discharging begins may include: The state of charge of the target energy storage battery at the beginning of charging and discharging is obtained by the open circuit voltage method.

[0041] Exemplarily, obtaining the state of charge of the target energy storage battery at the beginning of charging and discharging by the open circuit voltage method may include: Get OCV-SOC curve cluster.

[0042] Based on the OCV-SOC curve cluster, the state of charge of the target energy storage battery at the beginning of charging and discharging is determined.

[0043] Exemplarily, determining the state of charge of the target energy storage battery at the start of charging and discharging based on the OCV-SOC curve cluster may include: Get the target voltage of the target energy storage battery when charging and discharging starts.

[0044] Based on the target voltage, a corresponding second state of charge estimation value is found on each OCV-SOC curve in the OCV-SOC curve cluster.

[0045] An average of the plurality of second state-of-charge estimation values ​​is used as the state-of-charge of the target energy storage battery when charging and discharging begins.

[0046] For example, determining the state of charge of the target energy storage battery through the OCV-SOC curve cluster provides a non-invasive and highly accurate method to accurately assess its charge level without interfering with the battery operation. In addition, this method can effectively avoid cumulative errors.

[0047] Exemplarily, when the target energy storage battery is not being charged or discharged, the state of charge is calibrated once every two hours.

[0048] Step 102 , calculating a first state of charge estimation value based on the real-time current, real-time voltage, real-time temperature, real-time charge and discharge rate, and the state of charge at the start of charge and discharge of the target energy storage battery.

[0049] Exemplarily, step 102 may include: If the real-time current is less than 0, the target energy storage battery is in a charging state, and the first formula is used to calculate a first state of charge estimation value.

[0050] The first formula can be:

[0051] in, represents a first state of charge estimation value, Indicates the rated capacity of the target energy storage battery, Indicates the start time of charging state. Indicates the end time of charging state. Represents the Coulomb efficiency, and the value of Coulomb efficiency is generally less than 1. Indicates the state of charge when charging begins.

[0052] Taking an example, the process of determining the relationship between charging coulombic efficiency and temperature, current and current SOC value is as follows: Charge rate (current) correlation experiment: Select multiple different charge rates and conduct experiments within a specific temperature and SOC range. Each experiment uses a series of charge and discharge cycles to calculate the coulombic efficiency at different charge rates according to a specific formula. Repeat this process for different SOC ranges, collect coulombic efficiency data at different charge rates, and then define relative coulombic efficiency, and determine its correlation with charge rate through data fitting.

[0053] SOC correlation experiment: The SOC range is subdivided into multiple sub-ranges for study. Under the conditions of a specific temperature and a given charge rate, a series of charge and discharge operations are performed on each sub-range to measure the coulombic efficiency. A polynomial fit is performed on the obtained data to determine the best fit order, thereby revealing the relationship between coulombic efficiency and SOC.

[0054] Temperature correlation experiments: Experiments are conducted at multiple temperature points and different SOC ranges, operating at specific charge and discharge rates, defining the relative coulombic efficiency and determining its correlation with temperature through data fitting.

[0055] After determining the correlation between current (charging rate), temperature, SOC value and coulomb efficiency, a three-dimensional relationship table is constructed to quickly determine the coulomb efficiency. The three-dimensional table uses current (charging rate), temperature and SOC value as the three coordinate axes, and discretely stores the coulomb efficiency data under different working conditions obtained from the experiment in the table. In practical applications, for the battery management system (BMS), once the current current (charging rate), temperature and SOC value are obtained in real time, the corresponding coulomb efficiency can be quickly found directly in the table, which effectively avoids the complex real-time calculation process and improves the operating efficiency of the system.

[0056] In view of the impact of battery aging on coulombic efficiency, a strategy of real-time updating of the three-dimensional relationship table of SOC, current rate and temperature is adopted in practical applications. As the number of battery cycles increases, the coulombic efficiency under different working conditions is re-measured regularly (such as every 100 cycles or at a suitable cycle set according to actual application requirements) and the data in the three-dimensional relationship table is updated. At the same time, according to the relationship model between the number of battery cycles and coulombic efficiency, the data in the three-dimensional relationship table is dynamically adjusted and compensated to ensure that the coulombic efficiency that matches the current battery status can always be accurately obtained throughout the battery life cycle, thereby effectively improving the SOC estimation accuracy of the charging ampere-hour integration method at different aging stages.

[0057] Exemplarily, step 102 may include: If the real-time current is greater than 0, the target energy storage battery is in a discharging state, and the second formula is used to calculate the first state of charge estimation value.

[0058] The second formula can be:

[0059] in, represents a first state of charge estimation value, Indicates the maximum releasable capacity of the target energy storage battery. Indicates the start time of the discharge state, Indicates the end time of the discharge state, Indicates the state of charge when discharge begins.

[0060] For example, during the discharge process, since the influence of coulomb efficiency is relatively small, the optimization is mainly carried out from the perspective of the actual releasable capacity of the battery. We mainly consider the three aspects of discharge rate, temperature and number of cycles to achieve the optimization and improvement of the "denominator" of the ampere-hour integration method.

[0061] The relevant parameter settings that affect the maximum capacity that the battery can release are as follows: Discharge rate setting: The actual capacity of the battery is determined by conducting a charge and discharge experiment on the lithium battery, and the discharge information at different discharge rates is recorded. The study found that at a discharge rate of 0.30C, the battery can discharge the maximum capacity and the discharge process is relatively stable. Based on the capacity at this rate, we compared the influencing factors of other discharge rates and found that the maximum amount of electricity actually discharged by the battery will decrease when the discharge rate is high.

[0062] Temperature setting: By comparing the actual capacity discharged by the battery at different temperatures, we obtain an expression for the relationship between the actual battery capacity and temperature, as well as the influence factor of temperature on the actual amount of electricity that can be discharged by the battery.

[0063] Setting of cycle number: Conduct discharge experiments on lithium batteries with different aging degrees, calibrate the actual capacity of the battery every 100 cycles and record the maximum capacity that can be released.

[0064] For example, estimating SOC through the improved ampere-hour integration method can make the obtained SOC calculation value more accurate, ensure the accuracy of subsequent model training, and thus make the calculation of the state of charge during the charging and discharging process more accurate.

[0065] Step 103 , input the real-time current, real-time voltage, real-time temperature, real-time charge and discharge rate and first state of charge estimation value of the target energy storage battery into a state of charge calculation model to determine the state of charge of the target energy storage battery.

[0066] Exemplarily, the state of charge calculation model is a neural network model; the state of charge calculation model includes an input layer, a convolutional layer, a pooling layer, a fully connected layer and an output layer.

[0067] Exemplarily, the input layer is used to receive input data.

[0068] Convolution layer: Use smaller convolution kernels (such as 3x3) to reduce the number of parameters while maintaining the ability to effectively extract input data features. By setting the appropriate number of convolution kernels, a balance is achieved between capturing features and controlling model complexity.

[0069] The convolution layer also includes a depthwise separable convolution layer: the depthwise separable convolution layer is introduced into the network, and the traditional convolution operation is decomposed into two steps: depthwise convolution and pointwise convolution. The depthwise convolution performs convolution operations on each input channel separately, and the pointwise convolution is used to fuse the output of the depthwise convolution. This structure significantly reduces the number of parameters and reduces the computational complexity while maintaining good feature extraction capabilities.

[0070] Pooling layer: Use maximum pooling or average pooling operations to reduce data dimensions, reduce the amount of calculation, and maintain the scale invariance of features. The pooling window size and step size are reasonably selected according to the characteristics of the input data to avoid excessive pooling and information loss.

[0071] Fully connected layer: After multiple convolution and pooling operations, the fully connected layer is used to map the extracted features to the final SOC estimation output. The number of neurons in the fully connected layer is adjusted according to the accuracy requirements of the SOC estimation and the complexity of the model.

[0072] The output layer is used to output the results of the operation.

[0073] Exemplarily, when the state of charge calculation model is trained, mean square error is used as a loss function to measure the accuracy of the model output result, wherein the method for obtaining the accurate value of SOC may include the following steps: Using standard charge and discharge tests, the battery is charged and discharged under strictly controlled conditions. The true SOC value is calculated according to a specific formula by accurately measuring the battery discharge current and time, combined with the nominal capacity of the battery. These true values ​​are used as verification data to compare with the SOC value estimated by the state of charge calculation model to evaluate the accuracy and reliability of the model. Based on the verification results, the hyperparameters of the model are further adjusted and optimized to ensure that the state of charge calculation model can provide high-precision SOC estimation under various actual working conditions.

[0074] The above-mentioned state of charge calculation method first collects the current, voltage, temperature, charge and discharge rate and initial state of charge of the target energy storage battery in real time. Subsequently, based on these data, we calculate a preliminary estimate of the state of charge, that is, the first state of charge estimate. Then, this estimate is input into the state of charge calculation model together with the collected data, so as to obtain the accurate value of the state of charge during the charging and discharging process. The method of this scheme uses an improved formula based on the ampere-hour integration method when calculating the first state of charge estimate, and uses the open circuit voltage method when obtaining the initial state of charge. Finally, the preliminary estimate is input into the state of charge calculation model, which improves the accuracy of the estimation. When calculating the state of charge, the influence of factors such as temperature change on the state of charge is considered, and the state of charge during the charging and discharging process can be accurately calculated.

[0075] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0076] Corresponding to the state of charge calculation method described in the above embodiment, Figure 2 A structural block diagram of a state of charge calculation device provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0077] See also Figure 2 , the state of charge calculation device in the embodiment of the present application may include: The data acquisition module 201 is used to acquire the real-time current, real-time voltage, real-time temperature, real-time charge and discharge rate and the state of charge when charging and discharging start of the target energy storage battery.

[0078] The data calculation module 202 is used to calculate a first state of charge estimation value based on the real-time current, real-time voltage, real-time temperature, real-time charge and discharge rate and the state of charge when charging and discharging start of the target energy storage battery.

[0079] The result output module 203 is used to input the real-time current, real-time voltage, real-time temperature, real-time charge and discharge rate and the first state of charge estimation value of the target energy storage battery into the state of charge calculation model to determine the state of charge of the target energy storage battery.

[0080] Exemplarily, the data calculation module 202 may be used to: If the real-time current is less than 0, the target energy storage battery is in a charging state, and the first formula is used to calculate a first state of charge estimation value.

[0081] The first formula can be:

[0082] in, represents a first state of charge estimation value, Indicates the rated capacity of the target energy storage battery, Indicates the start time of charging state. Indicates the end time of charging state. is the Coulomb efficiency, Indicates the state of charge when charging begins.

[0083] Exemplarily, the data calculation module 202 may be used to: If the real-time current is greater than 0, the target energy storage battery is in a discharging state, and the second formula is used to calculate the first state of charge estimation value.

[0084] The second formula can be:

[0085] in, represents a first state of charge estimation value, Indicates the maximum releasable capacity of the target energy storage battery. Indicates the start time of the discharge state, Indicates the end time of the discharge state, Indicates the state of charge when discharge begins.

[0086] Exemplarily, the data acquisition module 201 may be used for: The state of charge of the target energy storage battery at the beginning of charging and discharging is obtained by the open circuit voltage method.

[0087] Exemplarily, the data acquisition module 201 may be used for: Get OCV-SOC curve cluster.

[0088] Based on the OCV-SOC curve cluster, the state of charge of the target energy storage battery at the beginning of charging and discharging is determined.

[0089] Exemplarily, the data acquisition module 201 may be used for: Get the target voltage of the target energy storage battery when charging and discharging starts.

[0090] Based on the target voltage, a corresponding second state of charge estimation value is found on each OCV-SOC curve in the OCV-SOC curve cluster.

[0091] An average of the plurality of second state-of-charge estimation values ​​is used as the state-of-charge of the target energy storage battery when charging and discharging begins.

[0092] Exemplarily, the state of charge calculation model may be a neural network model; the state of charge calculation model may include an input layer, a convolutional layer, a pooling layer, a fully connected layer, and an output layer.

[0093] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0094] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0095] The present application also provides a terminal device, see Figure 3 The terminal device 300 may include: at least one processor 310 and a memory 320, wherein the memory 320 is used to store a computer program 321, and the processor 310 is used to call and run the computer program 321 stored in the memory 320 to implement the steps in any of the above-mentioned method embodiments, for example Figure 1 Steps 101 to 103 in the illustrated embodiment. Alternatively, when the processor 310 executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented, for example Figure 2 The functions of each module are shown.

[0096] Exemplarily, the computer program 321 may be divided into one or more modules / units, one or more modules / units are stored in the memory 320, and are executed by the processor 310 to complete the present application. The one or more modules / units may be a series of computer program segments that can complete specific functions, and the program segments are used to describe the execution process of the computer program in the terminal device 300.

[0097] Those skilled in the art will understand that Figure 3 It is only an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, such as input and output devices, network access devices, buses, etc.

[0098] The processor 310 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0099] The memory 320 may be an internal storage unit of the terminal device, or an external storage device of the terminal device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. The memory 320 is used to store the computer program and other programs and data required by the terminal device. The memory 320 may also be used to temporarily store data that has been output or is to be output.

[0100] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of this application is not limited to only one bus or one type of bus.

[0101] The state of charge calculation method provided in the embodiment of the present application can be applied to terminal devices such as computers, wearable devices, vehicle-mounted devices, tablet computers, laptops, netbooks, etc. The embodiment of the present application does not impose any restrictions on the specific type of terminal devices.

[0102] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in each embodiment of the above-mentioned state of charge calculation method can be implemented.

[0103] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in each embodiment of the above-mentioned state of charge calculation method when executing the computer program product.

[0104] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera device / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electrical carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0105] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0106] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0107] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0108] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0109] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for calculating state of charge, characterized in that: include: Obtain the real-time current, real-time voltage, real-time temperature, real-time charge and discharge rate and state of charge when charging and discharging start of the target energy storage battery; Calculate a first state of charge estimation value based on the real-time current, real-time voltage, real-time temperature, real-time charge and discharge rate, and state of charge at the start of charge and discharge of the target energy storage battery; The real-time current, real-time voltage, real-time temperature, real-time charge and discharge rate and the first state of charge estimation value of the target energy storage battery are input into the state of charge calculation model to determine the state of charge of the target energy storage battery.

2. The method for calculating the state of charge according to claim 1, wherein: The calculating of the first state of charge estimation value based on the real-time current, real-time voltage, real-time temperature, real-time charge and discharge rate and the state of charge when charging and discharging start of the target energy storage battery includes: If the real-time current is less than 0, the target energy storage battery is in a charging state, and the first state of charge estimation value is calculated using a first formula; The first formula is: in, represents the first state of charge estimation value, Indicates the rated capacity of the target energy storage battery, Indicates the start time of charging state. Indicates the end time of charging state. is the Coulomb efficiency, Indicates the state of charge when charging begins.

3. The method for calculating the state of charge according to claim 1, wherein: The calculating of the first state of charge estimation value based on the real-time current, real-time voltage, real-time temperature, real-time charge and discharge rate and the state of charge when charging and discharging start of the target energy storage battery includes: If the real-time current is greater than 0, the target energy storage battery is in a discharging state, and the first state of charge estimation value is calculated using a second formula; The second formula is: in, represents the first state of charge estimation value, Indicates the maximum releasable capacity of the target energy storage battery. Indicates the start time of the discharge state, Indicates the end time of the discharge state, Indicates the state of charge when discharge begins.

4. The method for calculating the state of charge according to claim 1, wherein: The step of obtaining the charge state of the target energy storage battery when charging and discharging begins includes: The state of charge of the target energy storage battery at the beginning of charging and discharging is obtained by the open circuit voltage method.

5. The method for calculating the state of charge according to claim 4, wherein: The method of obtaining the state of charge of the target energy storage battery at the beginning of charging and discharging by the open circuit voltage method includes: Obtain OCV-SOC curve cluster; Based on the OCV-SOC curve cluster, the state of charge of the target energy storage battery at the beginning of charging and discharging is determined.

6. The method for calculating the state of charge according to claim 5, wherein: The determining, based on the OCV-SOC curve cluster, the state of charge of the target energy storage battery when charging and discharging begins includes: Obtaining a target voltage of the target energy storage battery when charging and discharging begins; Based on the target voltage, searching for a corresponding second state of charge estimation value on each OCV-SOC curve in the OCV-SOC curve cluster; An average of the plurality of second state-of-charge estimation values ​​is used as the state-of-charge of the target energy storage battery when charging and discharging begins.

7. The method for calculating the state of charge according to claim 1, wherein: The state of charge calculation model is a neural network model; the state of charge calculation model includes an input layer, a convolution layer, a pooling layer, a fully connected layer and an output layer.

8. A state of charge calculation device, characterized in that: include: A data acquisition module is used to obtain the real-time current, real-time voltage, real-time temperature, real-time charge and discharge rate and the state of charge when charging and discharging start of the target energy storage battery; A data calculation module, used to calculate a first state of charge estimation value based on the real-time current, real-time voltage, real-time temperature, real-time charge and discharge rate, and state of charge at the start of charging and discharging of the target energy storage battery; The result output module is used to input the real-time current, real-time voltage, real-time temperature, real-time charge and discharge rate and the first state of charge estimation value of the target energy storage battery into the state of charge calculation model to determine the state of charge of the target energy storage battery.

9. A terminal device, comprising: A processor and a memory, wherein the memory stores a computer program that can be run on the processor, and wherein the processor implements the state of charge calculation method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for calculating the state of charge according to any one of claims 1 to 7 is implemented.

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