Method and device for calculating SOC (State of Charge) of energy storage battery

Abstract

The invention provides an energy storage battery SOC calculation method and device. The method comprises the steps of obtaining dynamic performance parameters of an energy storage battery; performing kinetic parameter identification on the dynamic performance parameters to obtain a plurality of state evaluation parameters; and on the basis of executing nonlinear activation on the state evaluation parameters, determining target SOC data of the energy storage battery, and according to the deviation between the target SOC data and the ampere-hour integral result corresponding to the state evaluation parameters, updating the target SOC data. The method comprises the following steps: performing kinetic parameter identification on dynamic performance data of an energy storage battery to obtain a state evaluation parameter, needing to be identified, of the energy storage battery; obtaining a target SOC parameter of the energy storage battery by performing a nonlinear activation operation on the state evaluation parameter; and then comparing the target SOC parameter with the ampere-hour integral result to determine an adjustment strategy for updating the target SOC parameter, so that the accuracy and robustness of SOC calculation of the energy storage battery are greatly improved, and the method is simple and convenient to operate and has wide application value.

Description

Technical Field

[0001] The present application relates to the technical field of energy storage systems, and in particular to a method and device for calculating the SOC of an energy storage battery. Background Art

[0002] SOC (State of charge), the state of charge of the energy storage battery, is used to characterize the remaining capacity of the energy storage battery. Since the total capacity of the battery is easily affected by uncertain factors such as battery aging and ambient temperature changes, it is difficult to calculate the SOC of the energy storage battery. Therefore, how to accurately and conveniently obtain the SOC parameters of the energy storage battery has become a technical problem that needs to be solved in the application of energy storage systems.

[0003] It should be noted that the above introduction to the technical background is only for the convenience of providing a clear and complete description of the technical solutions of the present application and for the convenience of understanding by those skilled in the art. It cannot be considered that the above technical solutions are well known to those skilled in the art simply because they are described in the background technology section of the present application. Summary of the invention

[0004] The embodiments of the present application provide a method and device for calculating the SOC of an energy storage battery.

[0005] The first embodiment of the present application proposes a method for calculating the SOC of an energy storage battery, including:

[0006] Obtain dynamic performance parameters of energy storage batteries;

[0007] Based on the dynamic parameter identification of the dynamic performance parameters, a plurality of state evaluation parameters are obtained;

[0008] Based on performing nonlinear activation on the state evaluation parameter, target SOC data of the energy storage battery is determined, and according to the deviation between the target SOC data and the ampere-hour integration result corresponding to the state evaluation parameter, the target SOC data is updated.

[0009] The second aspect of the present application provides a storage battery SOC calculation device, which is used to implement the storage battery SOC calculation method provided in the first aspect of the present application, including:

[0010] A first acquisition module, wherein the first acquisition module is used to acquire dynamic performance parameters of the energy storage battery;

[0011] A second acquisition module, the second acquisition module is used to acquire a plurality of state evaluation parameters based on dynamic parameter identification of the dynamic performance parameters;

[0012] a third acquisition module, the third acquisition module being used to determine target SOC data of the energy storage battery based on performing nonlinear activation on the state evaluation parameter;

[0013] The regulating module is used to update the target SOC data according to the deviation between the target SOC data and the ampere-hour integration result corresponding to the state evaluation parameter.

[0014] The third aspect embodiment of the present application proposes an electronic device, comprising: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the energy storage battery SOC calculation method proposed in the first aspect embodiment of the present application.

[0015] The fourth aspect embodiment of the present application proposes a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by the processor of an electronic device, the electronic device can execute the method proposed in the first aspect embodiment of the present application.

[0016] The fifth aspect embodiment of the present application proposes a computer program product, including a computer program, which implements the method proposed in the first aspect embodiment of the present application when executed by a processor in a communication device.

[0017] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

[0018] By identifying the kinetic parameters of the dynamic performance data of the energy storage battery, the state evaluation parameters that need to be identified for the energy storage battery are obtained; by performing nonlinear activation operations on the state evaluation parameters, the target SOC parameters of the energy storage battery are obtained; then, by comparing the target SOC parameters with the ampere-hour integration results, the adjustment strategy for updating the target SOC parameters is determined, which greatly improves the accuracy and robustness of the SOC calculation of the energy storage battery. The operation is simple and has wide application value.

[0019] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0021] Figure 1 A schematic diagram of a flow chart of a method for calculating the SOC of an energy storage battery provided in an embodiment of the present application;

[0022] Figure 2 A flow chart of another method for calculating the SOC of an energy storage battery provided in an embodiment of the present application;

[0023] Figure 3 A schematic diagram of the structure of a SOC calculation device for an energy storage battery provided in an embodiment of the present application;

[0024] Figure 4 A schematic diagram of the structure of an electronic device provided according to an embodiment of the present application;

[0025] Figure 5 It is a schematic diagram of the structure of another electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the appended claims.

[0027] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the embodiments of the present application. The singular forms of "a" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0028] It should be understood that, although the terms first, second, third, etc. may be used to describe various information in the embodiments of the present application, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the embodiments of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at..." or "when..." or "in response to determination".

[0029] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0030] The following describes a method and device for calculating the SOC of an energy storage battery in an embodiment of the present application with reference to the accompanying drawings.

[0031] Figure 1A schematic diagram of a method for calculating the SOC of an energy storage battery provided in an embodiment of the present application. Figure 1 As shown, the method includes but is not limited to the following steps:

[0032] S101, obtaining dynamic performance parameters of the energy storage battery.

[0033] In a feasible implementation, the dynamic performance parameters of the energy storage battery involve multiple aspects, which are crucial to evaluating the performance and applicability of the energy storage battery. First, identify whether the energy storage battery needs to be activated, because unactivated energy storage batteries may have phenomena such as capacity decay and increased internal resistance. Through activation treatment, the active substances inside the energy storage battery can be stimulated, the internal structure of the energy storage battery can be improved, and thus the performance of the energy storage battery can be optimized.

[0034] For the activated energy storage battery, the process of obtaining its dynamic performance parameters usually includes: identifying the working environment of the energy storage battery; according to its working environment, extracting the charge and discharge efficiency of the energy storage battery (the charge and discharge efficiency is the energy conversion efficiency during the charging and discharging process, which reflects the energy loss of the battery during the charging and discharging process), the charge and discharge rate (the charge and discharge rate refers to the current value required for the battery to discharge its rated capacity within a specified time, usually represented by the letter C), the discharge platform time (the discharge platform time refers to the discharge time from the battery being fully charged to a certain voltage. This time length reflects the stability of the battery under specific discharge conditions), the self-discharge rate (the self-discharge rate is also called the charge retention capacity, which refers to the ability of the battery to retain the amount of electricity stored in the battery under certain conditions when the battery is in an open circuit state. It reflects the loss of electricity when the battery is not used for a long time), the cycle life (the cycle life refers to the number of charge and discharge times that the battery undergoes under a certain charge and discharge system when the battery capacity drops to a certain specified value. It reflects the durability of the battery and the economy of long-term use), and the battery health state (i.e., the State of Health). Health, referred to as SOH, refers to the ratio of the capacity released by the battery from a fully charged state at a certain rate to the cut-off voltage to its corresponding nominal capacity. It reflects the current performance and remaining life of the battery), depth of discharge (Depth of Discharge, referred to as DOD, which represents the percentage of battery discharge to the rated capacity of the battery), etc. These parameters together reflect the performance and applicability of energy storage batteries, which are of great significance to the design and operation of energy storage systems. In practical applications, it is necessary to select appropriate energy storage batteries according to specific needs and scenarios and pay attention to the changes in their dynamic performance parameters.

[0035] S102, obtaining a plurality of state evaluation parameters based on dynamic parameter identification of the dynamic performance parameters.

[0036] In a feasible implementation, kinetic parameter identification is an important part of calculating the SOC of energy storage batteries. It aims to obtain key state evaluation parameters for calculating and evaluating SOC by measuring and analyzing the dynamic performance parameters of energy storage batteries under different working conditions. These state evaluation parameters include the ohmic internal resistance R1 of the energy storage battery (ohmic internal resistance is the resistance to current inside the battery, mainly composed of the resistance of components such as the electrolyte, electrode materials and current collector inside the battery, reflecting the sudden change characteristics of the battery terminal voltage, especially the voltage jump at the moment of charging and discharging), the electrochemical polarization internal resistance R2 of the energy storage battery (electrochemical polarization is the polarization caused by the electrochemical reaction rate of the positive and negative active substances being less than the electron movement rate. The electrochemical polarization internal resistance represents the resistance of the electrochemical polarization process), and the electrochemical polarization capacitance C1 of the energy storage battery (electrochemical polarization capacitance represents the dynamic response used to simulate the electrochemical polarization process. Its response The response time is usually in microseconds), the concentration polarization internal resistance R3 of the energy storage battery (concentration polarization is caused by the consumption of reactants, which causes the electrode surface to not be replenished in time, or the accumulation of certain products on the electrode surface cannot be evacuated in time, and the concentration polarization internal resistance represents the resistance of the concentration polarization process), the concentration polarization capacitor C2 of the energy storage battery (the concentration polarization capacitor represents the dynamic response used to simulate concentration polarization, and its response time is usually in seconds), the self-discharge internal resistance R4 of the energy storage battery (the self-discharge internal resistance is the resistance of the battery to slowly discharge due to internal chemical reactions when it is stationary, which affects the voltage maintenance ability of the battery after a long period of stationary state), and the open circuit voltage U OCV (Open circuit voltage refers to the potential difference between the two poles of the battery when the external circuit current is zero. In the process of kinetic parameter identification, open circuit voltage is usually used to represent the electromotive force or theoretical voltage of the battery, reflecting the voltage characteristics of the battery in a stable state, and is one of the important indicators for battery performance evaluation), time constant (in the process of kinetic parameter identification, the time constant is usually related to the product of polarization resistance and polarization capacitance, and is used to describe the rate of change of the internal state of the battery. After obtaining the specific time constant value, it is used to evaluate the response speed and stability of the battery under different working conditions), etc. After obtaining multiple state evaluation parameters, the SOC parameters of the energy storage battery can be further determined by using data fitting.

[0037] S103, determining the target SOC data of the energy storage battery based on performing nonlinear activation on the state evaluation parameter, and updating the target SOC data according to the deviation between the target SOC data and the ampere-hour integration result corresponding to the state evaluation parameter.

[0038] In a feasible implementation, after obtaining the state evaluation parameters, a nonlinear activation function can be used to predict or estimate the current state of the energy storage battery. This function can be based on a machine learning model (such as a neural network model, a random forest model, etc.) or a simplified expression based on a physical model. The state evaluation parameters are then implanted into the input layer, and the hidden layer is processed through a nonlinear activation function (a nonlinear activation function is used to introduce nonlinear characteristics between the layers of the function model, thereby enhancing the expressive power of the model. If it is a deep learning model, the nonlinear number of lines can enable the neural network model to better fit the state evaluation parameters, solve the problem that the linear model is difficult to process the state evaluation parameters, and enable the model to map the state evaluation parameters to the prediction of the target SOC parameters of the energy storage battery) to generate an SOC value (i.e., the target SOC parameter of the energy storage battery) at the output layer.

[0039] Furthermore, the target SOC parameter obtained based on the nonlinear activation operation is compared with the reference SOC parameter obtained based on the ampere-hour integration, and the deviation between the two is calculated; based on the calculated deviation, the target SOC parameter can be updated. The update strategy can be based on a variety of factors, such as the size of the deviation, the trend of the deviation (whether it is continuously increasing or decreasing), the battery usage mode (charging, discharging, idle), etc. For example, if the deviation is persistent and significant, it may be necessary to adjust the parameters of the nonlinear activation function or calibrate the calculation of the ampere-hour integration. Furthermore, in practical applications, the state and performance of the energy storage battery will change over time, so the update of the target SOC parameter should be a dynamic process. A feedback mechanism can be set to re-evaluate and adjust the calculation method of the target SOC parameter periodically (such as after each charge / discharge cycle) or according to specific conditions (such as detecting a significant change in battery performance).

[0040] In summary, a method for calculating the SOC of an energy storage battery of the present application obtains the state evaluation parameters that need to be identified for the energy storage battery by performing kinetic parameter identification on the dynamic performance data of the energy storage battery; obtains the target SOC parameters of the energy storage battery by performing a nonlinear activation operation on the state evaluation parameters; and then determines an adjustment strategy for updating the target SOC parameters by comparing the target SOC parameters with the ampere-hour integration result, thereby greatly improving the accuracy and robustness of the SOC calculation of the energy storage battery, and the method is easy to operate and has wide application value.

[0041] Figure 2 This is a flow chart of another method for calculating the SOC of an energy storage battery provided in an embodiment of the present application. Figure 2 As shown, the method includes but is not limited to the following steps:

[0042] S201, identifying the electrochemical properties and physical properties of the energy storage battery.

[0043] In a feasible implementation, the electrochemical properties and physical properties of energy storage batteries are important indicators for evaluating their performance and application scenarios, among which the electrochemical properties mainly involve the electrochemical reactions occurring inside the battery and their related performance parameters, which directly affect the battery's energy storage, conversion efficiency and safety. As an example, if there is no current passing through the circuit, the potential difference between the positive and negative electrodes of the energy storage battery is characterized by the open circuit voltage; if the energy storage battery is in a working state, that is, when there is current flowing through the circuit, the potential difference between the positive and negative electrodes of the energy storage battery is characterized by the working voltage or terminal voltage. Energy storage batteries also have capacity characteristics. Capacity is an important indicator for measuring the ability of energy storage batteries to store electrical energy, including actual capacity and rated capacity. Actual capacity refers to the amount of electricity released by the energy storage battery under certain discharge conditions; rated capacity is the battery capacity calibrated by the energy storage battery manufacturer. Energy storage batteries also have an internal resistance effect. Internal resistance is a key parameter for measuring the performance of energy storage batteries. It consists of two parts: ohmic internal resistance and polarization resistance. Excessive internal resistance will generate a lot of heat during the charging and discharging process, affecting the electrochemical performance of the battery. Energy storage batteries also have charging and discharging characteristics, and will exhibit specific voltage, current and time relationships during the charging and discharging process, such as constant current charging, constant voltage charging, constant power discharging and other characteristics.

[0044] The physical properties of energy storage batteries mainly involve the structure, size, weight, adaptability to the external environment, safety and other aspects of the battery. As an example, the size and weight of energy storage batteries directly affect their installation and ease of use in different application scenarios. Among them, portable electronic devices require small and lightweight batteries, while energy storage power stations may use larger and heavier battery packs. The structural design of energy storage batteries includes the selection of electrode materials, the formulation of electrolytes, the material and shape of the battery casing, etc. These factors will affect the performance and safety of the battery. Energy storage batteries need to work under different environmental conditions, including temperature, humidity, vibration, etc. Therefore, the physical properties of batteries also include their adaptability and stability to the environment. The safety of energy storage batteries is an important aspect of physical properties, which includes thermal stability, short circuit protection, overcharge and over-discharge protection mechanisms of energy storage batteries to ensure that energy storage batteries will not cause dangerous events such as fire or explosion under abnormal conditions.

[0045] For further details on step S201, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0046] S202, performing mixed pulse power characteristic detection on the operating state of the energy storage battery according to the electrochemical properties and physical properties to obtain dynamic performance data of the energy storage battery.

[0047] In a feasible implementation, hybrid pulse power characteristic detection, full name Hybrid Pulse Power Characterization, referred to as HPPC, is used to evaluate the pulse charge and discharge performance of energy storage batteries at different discharge depths, including discharge pulse and regenerative charging pulse power capabilities. Through testing, the power output and energy recovery capabilities of energy storage batteries under different states can be understood, providing a basis for the calculation of the target SOC parameters of the energy storage battery. Specifically, the HPPC test uses a DC pulse signal as the input of the energy storage battery, and observes the response of the battery through short-term charge and discharge pulses. During the test, the energy storage battery will undergo a series of discharge, standby and charging pulses. After each pulse cycle, the battery voltage and current data will be recorded for subsequent analysis. It should be noted that dynamic stress test (full name Dynamic Stress Test, abbreviated as DST, used to evaluate the comprehensive performance of the battery under actual use conditions, including the durability, cycle life and thermal management performance of the energy storage battery, and explore the performance stability of the energy storage battery under different loads and ambient temperatures), dynamic operating condition simulation test (by simulating various operating conditions in the actual use of the energy storage battery, such as urban circulation, high-speed cruising, etc., to analyze the power output, energy recovery, thermal management and other conditions of the energy storage battery), etc., can also be used. As long as the dynamic performance data of the energy storage battery can be obtained, any detection method is applicable, and is not limited to this embodiment.

[0048] For further details on step S202, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0049] S203, obtaining a plurality of state evaluation parameters based on dynamic parameter identification of the dynamic performance parameters.

[0050] In a feasible implementation, the target parameters that need to be identified are obtained according to the application scenarios corresponding to the dynamic performance data. Then, the target parameters are modeled based on the second-order RC network to obtain the kinetic model of the energy storage battery operation, wherein the kinetic model of the energy storage battery operation involves the electrochemical process inside the energy storage battery, thermal behavior, and the interaction between the energy storage battery and the external system (such as the power grid). In the kinetic model, the parameters that need to be identified are clearly defined, such as charge and discharge efficiency, charge and discharge rate, discharge platform time, self-discharge rate, energy storage battery cycle life, battery health status, discharge depth, etc.

[0051] While obtaining the kinetic model of the energy storage battery operation, the first intermediate data is obtained based on the preprocessing of the dynamic performance data, wherein the preprocessing includes data cleaning, data conversion, feature selection and extraction, and data partitioning. Then, the parameters of the first intermediate data are identified based on the kinetic model to obtain multiple state evaluation parameters. Optionally, the parameter identification method can be determined according to the mathematical expression corresponding to the kinetic model and the identification accuracy requirements. The parameter representation method can adopt the least squares method, maximum likelihood estimation, Bayesian statistical method, etc.; deep learning methods can also be used, such as using deep learning models such as neural networks to identify parameters of the first intermediate data; genetic algorithms and particle swarm optimization algorithms can also be used to find the optimal target from the first intermediate data.

[0052] After determining the parameter identification method, the first intermediate data is subjected to parameter identification based on the parameter identification method to obtain multiple state evaluation parameters, such as ohmic internal resistance R1, electrochemical polarization internal resistance R2, electrochemical polarization capacitance C1, concentration polarization internal resistance R3, concentration polarization capacitance C2, self-discharge internal resistance R4, open circuit voltage U OCV , time constant, etc.

[0053] For further details on step S203, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0054] S204, determining the target SOC data of the energy storage battery based on performing nonlinear activation on the state evaluation parameter, and updating the target SOC data according to the deviation between the target SOC data and the ampere-hour integration result corresponding to the state evaluation parameter.

[0055] In a feasible implementation, based on feature extraction of state evaluation parameters, key elements reflecting the state of charge of the energy storage battery are obtained, wherein the terminal voltage of the energy storage battery is one of the direct parameters reflecting the SOC. Under static conditions, there is a relatively stable proportional relationship between the open circuit voltage (OCV) and the SOC. By real-time monitoring of the voltage change of the battery, the SOC state of the battery can be preliminarily determined; current is an important parameter in the charging and discharging process of the energy storage battery. By monitoring the current, the charge and discharge amount of the battery over a period of time can be estimated, and then the SOC of the battery can be calculated; temperature has a significant impact on the performance of the energy storage battery, including the internal resistance and open circuit voltage of the energy storage battery. At different temperatures, the SOC estimation model of the energy storage battery may need to be adjusted. Therefore, temperature is a parameter that cannot be ignored when extracting key elements reflecting the SOC.

[0056] While acquiring the key elements reflecting the state of charge of the energy storage battery, the function type for performing the nonlinear activation operation is determined according to the data type of the state evaluation parameter, for example, a Sigmoid function, a hyperbolic tangent function, etc. Then, according to the function type and the key elements, nonlinear activation is performed on the state evaluation parameter to determine the target SOC parameter of the energy storage battery. For the acquisition process of the target SOC parameter of the energy storage battery, reference can be made to the relevant contents in the above embodiment, which will not be repeated here.

[0057] Then, the deviation between the target SOC parameter and the corresponding ampere-hour integration result of the state evaluation parameter is calculated, wherein the second intermediate data is obtained based on the smoothing process of the target SOC parameter (used to remove the noise of the target SOC parameter, improve its signal-to-noise ratio, and optimize the quality of the target SOC parameter). At the same time, based on the ampere-hour integration of the state evaluation parameter, the reference SOC parameter is obtained; according to the deviation between the second intermediate data and the reference SOC parameter, the corresponding adjustment strategy is determined. Specifically, if the deviation between the second intermediate data and the reference SOC parameter is caused by the uncertainty of the initial SOC parameter of the energy storage battery, the adjustment strategy is determined to be recalibrating the initial SOC parameter of the energy storage battery; if the deviation between the second intermediate data and the reference SOC parameter is caused by the dynamic performance data of the energy storage battery, the adjustment strategy is determined to be calibrating the dynamic performance data; if the deviation between the second intermediate data and the reference SOC parameter is caused by the ambient temperature, the adjustment strategy is determined to introduce a temperature compensation mechanism.

[0058] After determining the corresponding adjustment strategy, the target SOC parameter is updated based on the adjustment strategy, wherein the update strategy can be based on a variety of factors, such as the size of the deviation, the trend of the deviation (whether it is continuously increasing or decreasing), the battery usage mode (charging, discharging, idle), etc. For example, if the deviation is persistent and significant, it may be necessary to adjust the parameters of the nonlinear activation function or calibrate the calculation of the ampere-hour integral. Furthermore, in practical applications, the state and performance of the energy storage battery will change over time, so the update of the target SOC parameter should be a dynamic process. A feedback mechanism can be set to re-evaluate and adjust the calculation method of the target SOC parameter periodically (such as after each charge / discharge cycle) or according to specific conditions (such as detecting a significant change in battery performance).

[0059] For further details on step S204, please refer to the relevant contents in the above embodiments, which will not be repeated here.

[0060] In summary, a method for calculating the SOC of an energy storage battery of the present application obtains the state evaluation parameters that need to be identified for the energy storage battery by performing kinetic parameter identification on the dynamic performance data of the energy storage battery; obtains the target SOC parameters of the energy storage battery by performing a nonlinear activation operation on the state evaluation parameters; and then determines an adjustment strategy for updating the target SOC parameters by comparing the target SOC parameters with the ampere-hour integration result, thereby greatly improving the accuracy and robustness of the SOC calculation of the energy storage battery, and the method is easy to operate and has wide application value.

[0061] Figure 3 This is a schematic diagram of the structure of a storage battery SOC calculation device provided in an embodiment of the present application. Figure 3 As shown, the energy storage battery SOC calculation device 300 is used to implement the energy storage battery SOC calculation method provided in the embodiment of the present application, including:

[0062] A first acquisition module 301, the first acquisition module 301 is used to acquire dynamic performance parameters of the energy storage battery;

[0063] A second acquisition module 302, the second acquisition module 302 is used to obtain multiple state evaluation parameters based on dynamic parameter identification of dynamic performance parameters;

[0064] A third acquisition module 303, the third acquisition module 303 is used to determine the target SOC data of the energy storage battery based on performing nonlinear activation on the state evaluation parameter;

[0065] The adjustment module 304 is used to update the target SOC data according to the deviation between the target SOC data and the ampere-hour integration result corresponding to the state evaluation parameter.

[0066] Figure 4 The present invention is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. Figure 4 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0067] like Figure 4As shown, the electronic device 400 includes a processor 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a memory 406 to a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 are also stored. The processor 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0068] The following components are connected to the I / O interface 405: a memory 406 including a hard disk, etc.; and a communication part 407 including a network interface card such as a LAN (Local Area Network) card, a modem, etc., and the communication part 407 performs communication processing via a network such as the Internet; a drive 408 is also connected to the I / O interface 405 as needed.

[0069] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 407. When the computer program is executed by the processor 401, the above-mentioned functions defined in the method of the present application are executed.

[0070] In an exemplary embodiment, a storage medium including instructions is also provided, such as a memory including instructions, and the instructions can be executed by the processor 401 of the electronic device 400 to complete the above method. Optionally, the storage medium can be a non-transitory computer-readable storage medium, for example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0071] In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0072] Figure 5 It is a schematic diagram of the structure of another electronic device provided according to an embodiment of the present application. Figure 5 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present application. Figure 5 As shown, the electronic device 500 includes a processor 501 and a memory 502. The memory 502 is used to store program codes, and the processor 501 is connected to the memory 502 to read the program codes from the memory 502 to implement the energy storage battery SOC calculation method in the above embodiment.

[0073] Optionally, the number of processors 501 may be one or more.

[0074] Optionally, the electronic device may further include an interface 503, and the number of the interface 503 may be multiple. The interface 503 may be connected to an application program and may receive data from an external device such as a sensor.

[0075] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed in this application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0076] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for calculating the SOC of an energy storage battery, characterized in that: include: Obtain dynamic performance data of energy storage batteries; Based on the dynamic parameter identification of the dynamic performance data, a plurality of state evaluation parameters are obtained; Based on performing nonlinear activation on the state evaluation parameter, a target SOC parameter of the energy storage battery is determined, and according to a deviation between the target SOC parameter and an ampere-hour integration result corresponding to the state evaluation parameter, the target SOC parameter is updated.

2. The method according to claim 1, characterized in that The obtaining of dynamic performance data of the energy storage battery includes: Identify the electrochemical and physical properties of energy storage batteries; According to the electrochemical properties and the physical properties, a mixed pulse power characteristic test is performed on the operating state of the energy storage battery to obtain dynamic performance data of the energy storage battery.

3. The method according to claim 1 or 2, characterized in that: The obtaining of a plurality of state evaluation parameters based on dynamic parameter identification of the dynamic performance data includes: According to the application scenario corresponding to the dynamic performance data, the target parameters to be identified are obtained; Modeling the target parameters based on a second-order RC network to obtain a dynamic model of the energy storage battery operation; Based on preprocessing the dynamic performance data, obtaining first intermediate data; Parameter identification is performed on the first intermediate data based on the dynamic model to obtain a plurality of state assessment parameters.

4. The method according to claim 3, characterized in that The performing parameter identification on the first intermediate data based on the dynamic model to obtain a plurality of state evaluation parameters includes: Determine a parameter identification method according to the mathematical expression corresponding to the dynamic model and the identification accuracy requirement; Parameter identification is performed on the first intermediate data based on the parameter identification method to obtain a plurality of state evaluation parameters.

5. The method according to any one of claims 1 to 4, characterized in that The determining the target SOC parameter of the energy storage battery based on performing nonlinear activation on the state evaluation parameter includes: Based on feature extraction of the state evaluation parameters, key elements reflecting the state of charge of the energy storage battery are obtained; Determining the type of function for performing a nonlinear activation operation according to the data type of the state evaluation parameter; According to the function type and the key elements, nonlinear activation is performed on the state assessment parameters to determine the target SOC parameters of the energy storage battery.

6. The method according to claim 1, characterized in that The updating of the target SOC parameter according to the deviation between the target SOC parameter and the ampere-hour integration result corresponding to the state evaluation parameter comprises: Obtaining second intermediate data based on smoothing the target SOC parameter; Obtaining a reference SOC parameter based on ampere-hour integration of the state assessment parameter; According to the deviation between the second intermediate data and the reference SOC parameter, a corresponding adjustment strategy is determined, and based on the adjustment strategy, the target SOC parameter is updated.

7. The method according to claim 6, characterized in that The determining a corresponding adjustment strategy according to the deviation between the second intermediate data and the reference SOC parameter includes: If the deviation between the second intermediate data and the reference SOC parameter is caused by the uncertainty of the initial SOC parameter of the energy storage battery, determining the adjustment strategy is to recalibrate the initial SOC parameter of the energy storage battery; If the deviation between the second intermediate data and the reference SOC parameter is caused by dynamic performance data of the energy storage battery, determining the adjustment strategy is to calibrate the dynamic performance data; If the deviation between the second intermediate data and the reference SOC parameter is caused by the ambient temperature, the adjustment strategy is determined to be introducing a temperature compensation mechanism.

8. A SOC calculation device for an energy storage battery, used to implement the SOC calculation method for an energy storage battery according to any one of claims 1 to 7, characterized in that: include: A first acquisition module, wherein the first acquisition module is used to acquire dynamic performance parameters of the energy storage battery; A second acquisition module, the second acquisition module is used to acquire a plurality of state evaluation parameters based on dynamic parameter identification of the dynamic performance parameters; a third acquisition module, the third acquisition module being used to determine target SOC data of the energy storage battery based on performing nonlinear activation on the state evaluation parameter; The regulating module is used to update the target SOC data according to the deviation between the target SOC data and the ampere-hour integration result corresponding to the state evaluation parameter.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that: When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method as claimed in any one of claims 1 to 7.

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