Battery equivalent circuit modeling method and device, battery management chip and electric equipment

Abstract

The invention provides a battery equivalent circuit modeling method and device, a battery management chip, electric equipment and a computer readable storage medium, and relates to the technical field of battery modeling. The battery equivalent circuit modeling method comprises the following steps: acquiring a state-space equation of an initial equivalent circuit model, wherein the state-space equation comprises to-be-identified parameters of each electrical element; constructing a sparse model of the initial equivalent circuit model based on the state-space equation, the to-be-identified parameters and actually measured working condition data of the battery in the target scene, and solving a sparse solution of the sparse model; based on the sparse solution, a target equivalent circuit model of the battery in the target scene is determined, and the target equivalent circuit model comprises all or part of electrical elements in the initial equivalent circuit model. According to the method, the target equivalent circuit model corresponding to the target scene is determined by solving the sparse solution, and model selection adaptive to different application scenes can be efficiently completed.

Description

Technical Field

[0001] The present application relates to the technical field of battery modeling, and in particular to a battery equivalent circuit modeling method, device, battery management chip, electrical equipment and computer-readable storage medium. Background Art

[0002] With the rapid development of lithium-ion battery technology, the research on its state of health (SOH) estimation method is also deepening. SOH represents the degree of battery aging, reflects the remaining life and remaining available capacity of the battery, helps users use the battery reasonably, and is of great significance to reducing battery abuse.

[0003] At present, the SOH estimation method based on the equivalent circuit model mainly determines the various parameters of the equivalent circuit model through the detection of electrochemical impedance spectroscopy; however, this method is a continuous nonlinear measurement process and cannot be implemented in different real vehicle scenarios, resulting in the selection of the battery equivalent circuit model cannot be adapted to the specific application scenario. Summary of the invention

[0004] According to various embodiments of the present application, a battery equivalent circuit modeling method, device, electrical equipment and readable storage medium are provided, which can solve the problem that the selection of battery equivalent circuit model cannot be adapted to specific application scenarios.

[0005] In a first aspect, the present application provides a battery equivalent circuit modeling method, comprising:

[0006] The state space equation of the initial equivalent circuit model is obtained, wherein the state space equation includes parameters to be identified of each electrical component; based on the state space equation, the parameters to be identified and the measured operating condition data of the battery in the target scenario, a sparse model of the initial equivalent circuit model is constructed, and a sparse solution of the sparse model is solved; based on the sparse solution, a target equivalent circuit model of the battery in the target scenario is determined, wherein the target equivalent circuit model includes all or part of the electrical components in the initial equivalent circuit model.

[0007] In the above method, since the sparse solution can reflect the importance of the parameters to be identified of each electrical component in the state-space equation relative to the overall equivalent circuit model, by constructing a sparse model of the initial equivalent circuit model that includes a relatively comprehensive set of electrical components, and solving the sparse solution of the sparse model, the parameter identification of each electrical component in the equivalent circuit model is converted into determining the contribution of each parameter to the equivalent circuit model through the sparse solution, and determining the target equivalent circuit model adapted to the target scenario based on the contribution reflected by the sparse solution; the target equivalent circuit model can be composed of some electrical components in the initial equivalent circuit model, or can be composed of all electrical components in the initial equivalent circuit model, and can be determined based on the requirements of different application scenarios, so that the selection of equivalent circuit models can be efficiently completed for different application scenarios; it has strong ease of use and practicality.

[0008] In a possible implementation manner of the first aspect, after obtaining the state space equation of the initial equivalent circuit model, the method further includes:

[0009] The state space equation is discretized to obtain the discretized state space equation of the initial equivalent circuit model.

[0010] Through the above method, the state space equation of the initial equivalent circuit model is discretized, which can be applicable to the scenario where the battery operating condition data is discrete sequence data. At the same time, it can also reduce the calculation time for parameter identification, improve the efficiency of subsequent solving of sparse solutions, and improve the efficiency of selecting the target equivalent circuit model of the battery.

[0011] In a possible implementation of the first aspect, a sparse model of an initial equivalent circuit model is constructed based on a state space equation, parameters to be identified, and measured operating condition data of a battery in a target scenario, including:

[0012] Based on the state space equation, the degree of deviation between the simulated operating condition data and the measured operating condition data is obtained; and according to the degree of deviation and the sparse penalty term generated based on the parameters to be identified, a sparse model of the initial equivalent circuit model is constructed.

[0013] In the above manner, the simulation operating condition data corresponding to the measured operating condition data are calculated through the state-space equation, and the coefficient model is obtained based on the degree of deviation between the simulation operating condition data and the measured operating condition data, and the sparse penalty term corresponding to the parameter to be identified. The mechanism of identifying parameters based on the degree of deviation is transformed into a mechanism for parameter identification based on sparse model solution, thereby reducing the number of parameters involved in the calculation, reducing the complexity of calculation, reducing the time for parameter identification, and improving the generalization ability of model selection for equivalent circuit models, which is suitable for more application scenarios.

[0014] In a possible implementation of the first aspect, a sparse model of an initial equivalent circuit model is constructed based on a state space equation, parameters to be identified, and measured operating condition data of a battery in a target scenario, including:

[0015] According to the state-space equation and the measured operating data of the battery under the target scenario, a loss function of the initial equivalent circuit model is constructed; based on the parameters to be identified and the loss function, a sparse model of the initial equivalent circuit model is constructed.

[0016] In the above manner, by constructing a loss function and constructing a sparse model based on the loss function and the parameters to be identified, the problem of optimizing parameter values ​​based on the loss function is transformed into the problem of parameter identification based on the sparse model, thereby reducing the complexity of parameter identification and making the target equivalent circuit model selected subsequently more accurate and more suitable for actual application scenarios.

[0017] In a possible implementation of the first aspect, a loss function of an initial equivalent circuit model is constructed according to a state space equation and measured operating condition data of a battery in a target scenario, including:

[0018] Based on the state-space equation, the simulation working condition data corresponding to the measured working condition data are obtained; based on the deviation between the simulation working condition data and the measured working condition data, a loss function is constructed; wherein the loss function is represented based on each parameter to be identified, and the value of each parameter to be identified is within a preset constraint range.

[0019] In the above manner, the loss function is used to measure the degree of deviation between the simulated operating condition data and the measured operating condition data of the equivalent circuit model. The function value of the loss function is used as an optimization reference for the parameters to be identified, thereby improving the accuracy of the selection of the equivalent circuit model. That is, the smaller the function value of the loss function, the closer the simulated operating condition data output by the state-space equation is to the measured operating condition data, and the more consistent the equivalent circuit model is with the actual operating conditions of the battery, so that the subsequent estimation of the battery health status based on the equivalent circuit model is more accurate.

[0020] In a possible implementation of the first aspect, a sparse model of an initial equivalent circuit model is constructed based on the parameters to be identified and the loss function, including:

[0021] Based on the parameters to be identified, a sparsity inducing function is constructed; the sparsity inducing function is used as the sparse penalty term of the loss function to obtain a sparse model.

[0022] Through the above method, the corresponding sparsity inducing function is constructed based on the parameters to be identified. By using the sparsity inducing function as a sparse penalty term, the complexity of the sparse model is controlled, making the parameters to be identified in the sparse model more sparse, thereby improving the generalization ability of the sparse model and being suitable for fast and accurate selection of equivalent circuit models in any scenario.

[0023] In a possible implementation manner of the first aspect, constructing a sparsity inducing function based on the parameter to be identified includes:

[0024] Based on the circuit modules to which each electrical component in the initial equivalent circuit model belongs, the parameters to be identified are divided to obtain parameter groups corresponding to each circuit module; based on the parameter groups of each circuit module, a sparsity inducing function is constructed.

[0025] Through the above method, since each parameter in the equivalent circuit does not exist independently but relies on different circuit modules, each parameter is grouped based on the structural relationship between the circuit modules. This can better reflect the relationship between the parameters to be identified in the equivalent circuit module, and more accurately reflect the importance of each circuit module relative to the equivalent circuit model when solving the sparse solution later.

[0026] In a possible implementation manner of the first aspect, the measured operating condition data includes current data and voltage data of the battery; and solving the sparse solution of the sparse model includes:

[0027] The current data and voltage data are input into the sparse model, and the sparse solution corresponding to each parameter group is calculated; wherein the initial equivalent circuit model includes the circuit modules to which each electrical component belongs, and the parameter group is a set of parameters to be identified included in the circuit module.

[0028] Through the above method, a sparse solution of the sparse model is solved based on the measured operating condition data, and the contribution of each circuit module to the equivalent circuit model is reflected based on the sparse solution, so as to realize the identification of the parameters to be identified in the equivalent circuit model, and the sparsity between each circuit module in the equivalent circuit model is reflected based on the sparse solution, so that the circuit modules with a higher contribution to the equivalent circuit model can be quickly and effectively determined, and the efficient selection of the target equivalent circuit model can be realized; the process of rapid model selection is made more generalized and can be applied to different application scenarios (including parameter identification of electrochemical modules), such as different operating conditions, different battery types, different vehicle models and other application scenarios.

[0029] In a possible implementation of the first aspect, determining a target equivalent circuit model of a battery in a target scenario based on a sparse solution includes:

[0030] The circuit modules included in the target equivalent circuit model are determined according to the size of the sparse solution corresponding to each parameter group and the operating condition constraints; wherein the size of the sparse solution is used to indicate the contribution of the circuit module to the target equivalent circuit model, and the operating condition constraints are the quantity restrictions on the circuit modules included in the target scenario.

[0031] Through the above method, in actual application scenarios, it is also necessary to determine the various circuit modules used to constitute the target equivalent circuit model in combination with the actual operating condition constraints. That is, not all application scenarios require an equivalent circuit model with uniform performance. While limiting the number of circuit modules, based on the sparse solution, several circuit modules with relatively high contribution levels are determined to constitute the target equivalent circuit model. The accuracy of the target equivalent circuit model is guaranteed without increasing the complexity of the model.

[0032] In a possible implementation manner of the first aspect, after determining a target equivalent circuit model of the battery in a target scenario based on a sparse solution, the method further includes:

[0033] Based on the measured operating condition data of the battery under the target scenario, the parameter values ​​corresponding to the various electrical components contained in the target equivalent circuit model are determined by calculating the state space equation corresponding to the target equivalent circuit model; wherein, the target equivalent circuit model after determining the parameter values ​​is used to estimate the health status of the battery.

[0034] Through the above method, polynomial calculations are further performed on the parameter values ​​of each electrical component in the target equivalent circuit model based on the measured operating condition data to obtain parameter values ​​that are more in line with the target scenario, making the equivalent circuit model more accurate. No other additional specific test conditions are required, and the model can be determined efficiently, thereby further improving the accuracy of subsequent battery health status estimation.

[0035] In a second aspect, the present application provides a battery equivalent circuit modeling device, comprising:

[0036] An acquisition unit, used for acquiring a state space equation of an initial equivalent circuit model, wherein the state space equation includes parameters to be identified of each electrical component;

[0037] A processing unit, configured to construct a sparse model of the initial equivalent circuit model based on the state-space equation, the parameters to be identified, and the measured operating condition data of the battery in the target scenario, and to solve a sparse solution of the sparse model;

[0038] An output unit is used to determine a target equivalent circuit model of the battery in the target scenario based on the sparse solution, wherein the target equivalent circuit model includes all or part of the electrical elements in the initial equivalent circuit model.

[0039] In a third aspect, the present application provides a battery management chip, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements any one of the methods described in the first aspect when executing the computer program.

[0040] In a fourth aspect, the present application provides an electrical device, comprising a battery and the battery management chip described in the third aspect; the battery management chip is electrically connected to the battery, and monitors and manages the battery.

[0041] In a fifth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any one of the methods described in the first aspect.

[0042] In a sixth aspect, the present application provides a computer program product, which, when executed on a computer, enables the computer to execute any of the methods described in the first aspect.

[0043] It can be understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, 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 work.

[0045] Figure 1 A schematic diagram of the architecture of a battery equivalent circuit model provided in an embodiment of the present application;

[0046] Figure 2 A schematic diagram of a flow chart of a battery equivalent circuit modeling method provided in an embodiment of the present application;

[0047] Figure 3 A schematic diagram of a process for establishing a sparse model provided in an embodiment of the present application;

[0048] Figure 4 A schematic diagram of the structure of a battery equivalent circuit modeling device provided in an embodiment of the present application;

[0049] Figure 5 A schematic diagram of the structure of a battery management chip is provided for an embodiment of the present application;

[0050] Figure 6 A structural schematic diagram of an electrical device is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0051] The following embodiments of the technical solution of the present application are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0053] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0054] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0055] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0056] With the rapid development of lithium-ion battery technology, the research on the estimation method of its state of charge (SOH) is also deepening. SOH represents the degree of battery aging, reflects the remaining life and remaining available capacity of the battery, and helps users use the battery reasonably.

[0057] However, due to the lack of support from electrical characteristic sensors, accurately estimating SOH is a very challenging task. Therefore, in different application scenarios (such as different working conditions in real vehicle scenarios, different battery types, different vehicle models, etc.), selecting an equivalent circuit model (ECM) with high accuracy and appropriate complexity is an urgent problem to be solved.

[0058] The SOH estimation method based on the equivalent circuit model (hereinafter referred to as the ECM model) simulates the polarization reaction and self-discharge reaction of the battery based on the electrical characteristics of the battery through modules such as resistance, capacitance, and electrochemical ion diffusion, so that the model can reflect the physical and chemical characteristics of the battery. The current selection of the ECM model is mainly based on electrochemical impedance spectroscopy (EIS) detection.

[0059] However, the method of determining model selection based on EIS detection is usually difficult to implement in real vehicle scenarios; and in the equivalent circuit model (ECM), the fewer circuit simulation devices introduced (such as resistor-capacitor RC modules of different orders), the simpler the model is and the lower the accuracy is. The more complex the model is, the higher the accuracy is, but the corresponding amount of calculation and the difficulty of parameter identification will also increase; this in turn increases the difficulty of model selection, making it difficult to select suitable models for different application scenarios.

[0060] On the other hand, in order to make up for the shortcomings of the ECM model in the internal characterization of the battery, an extended equivalent circuit model (EECM) (hereinafter referred to as the EECM model) of the electrochemical model (EM) and the equivalent circuit model (ECM) is proposed to integrate the two models to give the ECM model the ability to characterize electrochemical properties.

[0061] Correspondingly, the EECM model also faces a specific selection problem, that is, which electrochemical modules should be introduced into the EECM model to be the optimal model. Therefore, a more practical method without additional input requirements and specific test conditions is needed to efficiently complete the selection of ECM models or EECM models.

[0062] In view of the above problems, the present application provides a battery equivalent circuit modeling method, through which efficient model selection can be achieved and it is applicable to application scenarios under various working conditions. The specific implementation process of the battery equivalent circuit modeling method is further described below through embodiments.

[0063] First, the model architecture applicable to the embodiments of the present application is introduced. Figure 1 As shown, an embodiment of the present application provides an equivalent circuit model (or equivalent circuit mechanism model) of a battery, and the equivalent circuit model is illustrated by taking an electrochemical solid phase diffusion module combined with an extended equivalent circuit model including a second-order RC as an example.

[0064] like Figure 1 As shown, E EEMThe electrochemical solid phase diffusion module is used to simulate the difference in surface potential of lithium ions between the cathode and anode of lithium-ion batteries, which can well simulate the nonlinear characteristics of lithium batteries; C S R represents the concentration of lithium ions on the electrode surface after solid phase diffusion, and r represents the position of lithium ions. 0 Represents ohmic resistance, R 1 and R 2 Represent the electrochemical polarization resistance and concentration polarization resistance, C 1 and C 2 Represent the electrochemical polarization capacitance and concentration polarization capacitance respectively. 1 and U 2 Respectively represent the polarization voltage at both ends of the two RC networks, U L Indicates terminal voltage ( Figure 1 In the figure, U is U), I represents the discharge current (positive) and the charging current (negative).

[0065] It should be noted that the composition of each module in the overall architecture of the above-mentioned equivalent circuit model is only illustrative, including but not limited to the various modules in the above-mentioned model, and may also be an equivalent circuit model including other module combinations, such as a third-order RC or higher-order RC module.

[0066] Based on the overall architecture of the above equivalent circuit model, the specific implementation process of the battery equivalent circuit modeling method is introduced below. Through this battery equivalent circuit modeling method, it is possible to determine which modules to retain in the model so that the model effect in the corresponding scenario is better. This method can be applied to the selection of any EMC model or EEMC model, and can be adapted to a variety of different real vehicle application scenarios.

[0067] See also Figure 2 , Figure 2 A flowchart of a battery equivalent circuit modeling method provided in an embodiment of the present application. The execution subject of the battery equivalent circuit modeling method can be a battery management chip or a battery management system (BMS), or an electrical device including the former; the electrical device can be a terminal device, an electric vehicle, or an energy storage device, etc. Figure 2 As shown, the battery equivalent circuit modeling method may include the following steps:

[0068] S201, obtaining a state space equation of an initial equivalent circuit model, where the state space equation includes parameters to be identified of each electrical component.

[0069] In the embodiment of the present application, the initial equivalent circuit model can be any EMC model or EEMC model, for example Figure 1 The initial equivalent circuit model can be a model with a wide coverage and a comprehensive circuit module, such as Figure 1 the first-order module R shown in 1 C 1 , the second-order module R 2 C 2 , and the solid-phase diffusion module EEM; thus, subsequently, for different application scenario conditions, each circuit module therein can be selected to determine an equivalent circuit model adapted to the application scenario conditions.

[0070] Exemplarily, based on this initial equivalent circuit model, a state-space equation of this initial equivalent circuit model can be established, that is, a state-space equation represented by the parameters of each electrical component; since this initial equivalent circuit model is a reference model for model selection, the parameters of each corresponding electrical component are the parameters to be identified. As Figure 1 the parameters to be identified corresponding to each circuit module shown in 1 R 1 and C 2 in the first-order module, R 2 and C sd in the second-order module, the time constant τ sd and the correlation coefficient k 0 in the solid-phase diffusion module EEM, as well as the battery capacity Q 0 , the initial SOC of charging, etc.

[0071] In a possible implementation manner, after obtaining the state-space equation of the initial equivalent circuit model, the method further includes:

[0072] Discretize the state-space equation to obtain a discretized state-space equation of the initial equivalent circuit model.

[0073] Exemplarily, the equivalent circuit model is a continuous system, and the corresponding state-space equation can be an equation established for the continuously changing working condition data of the battery. And the working condition data collected in the real vehicle application scenario is time series data, and the time interval between every two frames of data is the sampling interval, that is, the real vehicle application scenario corresponds to a discretized system. Therefore, the embodiments of the present application can also discretize the continuous circuit model system to obtain a discretized state-space equation corresponding to the equivalent circuit model; correspond the discrete equivalent circuit model to the time series data under the real vehicle working conditions, so that the state-space equation of the equivalent circuit model can be applied to the real vehicle data; that is, convert the system described by continuous input and output signals into an equivalent discrete system described by discrete input and output signals.

[0074] Taking Figure 1 the equivalent circuit model shown in

[0075]

[0076] Among them, SOC ave Indicates the actual SOC (State of Charge) of the battery; SOC S represents the surface SOC of lithium ions; dSOC represents the difference between the actual SOC of the battery and the surface SOC; t represents the time; Q 0 Indicates battery capacity; U OCV It represents the open circuit voltage (OCV) of the battery, that is, the difference between the electrode potential of the positive electrode and the electrode potential of the negative electrode of the battery when no current passes through the two poles of the battery. It is a function of SOC. The OCV-SOC curve of the battery has basically the same trend as the battery charging voltage curve.

[0077] For the solid phase diffusion module, the surface concentration of active materials is used in the electrochemical model of lithium-ion batteries to represent the effect of open circuit voltage (OCV) on battery output voltage. When the battery has no output current, the surface concentration of active materials approaches the average concentration, and the battery terminal voltage U L ( Figure 1 U in the figure approaches the open circuit voltage; when there is output current, the solid phase diffusion process of lithium ions causes the difference between the electrode surface concentration and the average concentration, which leads to the difference between the surface SOC and the average SOC. The solid phase diffusion module adds the effect of lithium ion diffusion on the battery terminal voltage, so the second-order RC circuit model of lithium ion diffusion can be applied to the state estimation of lithium-ion batteries.

[0078] Accordingly, in order to apply the equivalent circuit model in discrete real vehicle operating condition data, the above equation group (1) is discretized to obtain a discretized state space equation. The discretized state space equation can be a differential equation. The formula of the equation group is expressed as follows:

[0079]

[0080] Where k represents the kth moment in the time series (or the kth frame of the time series data), and the SOC at the next moment k is the SOC at the previous moment k-1 plus the amount of electricity charged at the kth moment; k sd represents the correlation coefficient of the solid phase diffusion module, τ sd represents the time constant of the solid phase diffusion module, sd represents the solid phase diffusion module, △t represents the time difference between two adjacent frames of data (in seconds, 3600 represents converting seconds into hours).

[0081] It should be noted that the above equivalent circuit model and the corresponding state space equations (including those before and after discrete processing) are only for illustrative purposes. It can also be an equivalent circuit model including other electrical components and the corresponding state space equations. For example, it can also include third-order or higher-order RC modules, and other types of electrical components such as resistors or capacitors.

[0082] In addition, the embodiments of the present application can support application scenarios with continuously changing operating condition data based on the undiscretized equivalent circuit model, and can also support application scenarios of discrete time series data based on the discretized equivalent circuit model. Since the more modules the equivalent circuit model contains, the greater the computational complexity of SOH estimation. First, start with the ECM model (or EEMC model) with the widest coverage and the most comprehensive modules as the equivalent circuit model selected later. Under the condition of ensuring the accuracy of SOH estimation, the circuit modules it contains can be minimized as much as possible, thereby improving the calculation efficiency.

[0083] S202, based on the state space equation, the parameters to be identified, and the measured operating condition data of the battery in the target scenario, construct a sparse model of the initial equivalent circuit model, and solve the sparse solution of the sparse model.

[0084] In the embodiments of the present application, the target scenario can be the application scenario of a real vehicle. The variable factors of this application scenario can include vehicle type, operating condition, battery type, etc. Different factors correspond to different application scenarios and are adapted to different equivalent circuit models. The measured operating condition data includes the measured current and measured voltage of the battery during the operation of the real vehicle; the measured operating condition data can be discrete time series data of current and voltage, or continuously changing data.

[0085] Exemplarily, in the process of selecting and modeling the equivalent circuit model, combined with the parameter identification problem of sparse optimization, the selection of the equivalent circuit model is realized through the sparsity of the parameter identification solution. The sparse model can be a group sparse optimization model. By grouping the parameters to be identified according to the circuit modules, multiple parameter groups are obtained. For example, the solid-phase diffusion module parameter group (τ sd , k sd ), the first-order RC parameter group (R 1 , C 1 ), and the second-order RC parameter group (R 2 , C 2 ); based on multiple parameter groups, a sparsity-inducing function is constructed.

[0086] Among them, the function of the circuit equivalent model is to simulate the terminal voltage of the battery and output it. In the process of constructing the sparse model, the loss function can be first constructed based on the state space equation and the measured working condition data; that is, the measured current in the measured working condition data is used as the input of the state space equation to derive the representation of the simulated voltage (battery terminal voltage). For example, the last four formulas in the equation group (1) (or equation group (2)) are substituted into the first formula to obtain the representation of the simulated voltage; thus, the loss function is constructed by the simulated voltage and the measured voltage. The simulated voltage is derived through the state space equation and represented based on each parameter to be identified, so the loss function can also be a function corresponding to each parameter to be identified. Based on the constraint range of each parameter to be identified of the introduced equivalent circuit model, the loss function is used to continuously optimize each parameter to be identified within the constraint range.

[0087] Exemplarily, the sparsity induction function is applied to the aforementioned divided parameter groups to obtain sparsity induction functions corresponding to multiple parameter groups. The sparsity induction function is used as a regular term penalty on the loss function, or as a constraint condition of the loss function, to obtain a sparse model.

[0088] Exemplarily, the measured working condition data is input into the sparse model, and combined with the constraint range of each parameter to be identified in the equivalent circuit model, a set of sparse solutions is obtained. Among them, the sparse solution obtained by the sparse model is not the specific value of the parameter in each parameter group, but the importance weight corresponding to each parameter group; at this time, the specific value of each parameter is not used as the value of each parameter in the target equivalent circuit model.

[0089] In the embodiment of the present application, in the process of constructing a sparse model, a loss function is constructed based on the state equation and the measured working condition data, and the problem of parameter identification based on the loss function is combined with the sparsity of the induced parameters to be identified to transform it into a group sparse optimization problem. The measured working condition data of the target scene is input into the sparse model, and a sparse parameter identification result (sparse solution) is calculated.

[0090] S203, determining a target equivalent circuit model of the battery in a target scenario based on the sparse solution, where the target equivalent circuit model includes all or part of the electrical components in the initial equivalent circuit model.

[0091] In some embodiments, the importance of each parameter group to the entire equivalent circuit model (or extended equivalent circuit model) is determined based on the value of the sparse solution (the importance weight corresponding to each parameter group), and the target equivalent circuit model most suitable for the target scenario is selected based on the importance. Since the sparse solution is calculated based on the measured operating data of the target scenario and the corresponding operating data, the importance of each circuit module determined also corresponds to the importance of the target scenario.

[0092] Exemplarily, the target equivalent circuit model can be a part of the circuit modules selected in the initial equivalent circuit model based on the importance of each module, that is, including some electrical components, or it can be all the circuit modules in the initial equivalent circuit model, that is, including all electrical components; for example, the initial equivalent circuit model includes 5 circuit modules, and after the sparse solution is confirmed, the target equivalent circuit can select the first three circuit modules in order of sparse solution size. The larger the sparse solution value, the greater the contribution of the circuit module, and the better the simulation result obtained by using the circuit module as a submodule of the target equivalent circuit model.

[0093] It should be noted that the wider the coverage of the equivalent circuit model and the more modules it contains, the higher the estimation accuracy of SOH will be, but the complexity of the calculation will also increase significantly, and the difficulty of identifying each parameter will also increase. Through the ECM model finally selected based on the sparse solution in the embodiment of the present application, the number of circuit modules contained in the model is minimized while ensuring the accuracy of SOH estimation, thereby improving calculation efficiency. The embodiment of the present application starts with the initial equivalent circuit model with the widest coverage and the most comprehensive modules. The contribution of each circuit module in the initial equivalent circuit model in the target scenario is solved by the sparse model, and a part of the modules with high contribution is selected to determine the target equivalent circuit model.

[0094] Through the embodiment of the present application, based on the state space equation of the equivalent circuit model and the measured working condition data of the target scene, a loss function is constructed to optimize and identify each parameter to be identified of the equivalent circuit model; by dividing each parameter to be identified into parameter groups according to the circuit module, the sparsity of the parameters to be identified is induced based on group sparsification, and a sparse solution characterizing the importance of each parameter group is solved, and finally the target equivalent circuit model of the target scene is constructed based on the sparse solution; thereby, the selection of the ECM (or EECM) model can be completed more efficiently, and there is no additional input requirement, no requirement for specific test conditions, and the practicality and generalization ability are strong. It can be applied to battery modeling in different application scenarios, reducing the difficulty of parameter identification. Since the characteristics of the working condition data generated by different types of batteries under different working conditions are different, the most matching (i.e., the most suitable for the working condition data) ECM (or EECM) model can be automatically selected according to the characteristics of the working condition data, and the accuracy of the SOH estimation value obtained by the corresponding solution is also high.

[0095] The specific implementation process of constructing a sparse model is further introduced below through an embodiment. Among them, the sparse model can be composed of a loss function term and a sparse penalty term. The loss function is used to measure the degree of deviation between the simulated voltage and the measured voltage. The sparse penalty term is to select the circuit module and sparse the parameter group by applying the penalty function of the sparse model to the input parameter group to be identified, thereby outputting the contribution degree of each circuit module.

[0096] For example, the general form of the sparse model can be expressed as: argmin f(x)+Ψ λ (x), f(x) is the loss function, Ψ λ (x) is a penalty function with a sparsity effect, x is a set of parameters to be identified (i.e., a parameter group), and λ is a regularization parameter.

[0097] Exemplarily, the loss function may be any one of a root mean square error (RMSE) function, a mean square error (MSE) function, a mean absolute error (MAE) function, and a modified square loss function (Huber loss function).

[0098] Exemplarily, the sparse penalty term can be any one of a group lasso model, a group bridge model, a group smoothly clipped absolute deviation (SCAD) model, a group Monte Carlo (MC) model, and a smoothly clipped absolute deviation estimation (SLOPE) model.

[0099] The following describes the process of constructing a sparse model by taking the root mean square error RMSE function as the loss function and the group lasso model as the sparse penalty term. 0 The norm is used as a regularization term to characterize the sparsity at the parameter group level and realize the selection of parameter groups; for example, the sparse penalty term can be expressed as Ψ λ (x) = λ||x|| p .

[0100] like Figure 3 As shown, the implementation process may include the following steps:

[0101] S2021. Obtain a loss function of the initial equivalent circuit model based on the state-space equation and the measured operating data of the battery under the target scenario.

[0102] Exemplarily, the measured operating data in the target scenario can be parameters for comparison with the analog quantity output by the equivalent circuit model, and can include measured current and measured voltage. The measured operating data can be a known set of time series data, such as time series data of current or voltage. The function of the equivalent circuit model is to simulate and output the terminal voltage of the battery, that is, the expression of the corresponding simulated voltage can be derived through the state space equation, so that the loss function can be constructed based on the measured voltage in the measured operating data and the derived simulated voltage.

[0103] In some embodiments, the measured operating condition data includes the measured current and measured voltage of the battery; and according to the state space equation and the measured operating condition data of the battery in the target scenario, a loss function of the initial equivalent circuit model is obtained, including:

[0104] Based on the state space equation, the simulated voltage is derived with the measured current as an input parameter; and the loss function is constructed based on the simulated voltage and the measured voltage.

[0105] Among them, the values ​​of each parameter to be identified corresponding to the loss function are within the preset constraint range.

[0106] Exemplarily, the root mean square error function between the simulated voltage and the measured voltage is used as the loss function. The terminal voltage value obtained by simulating the equivalent circuit model based on solid phase diffusion is (Simulation voltage) and the measured terminal voltage U k The error of (measured voltage) is used to construct the loss function (objective function), and the formula is as follows:

[0107]

[0108] Among them, the objective function argmin represents the solution that minimizes the root mean square error RMSE. st represents the constraint range of the parameters to be identified, that is, the parameter group to be identified Each parameter in has upper and lower bound constraints (LB is the lower bound constraint and UB is the upper bound constraint).

[0109] by Figure 1 For example, the parameter group Including charging initial SOC of equivalent circuit module 0 、Battery capacity Q 0 、Ohmic resistance R 0 , Polarization internal resistance R 1 R 2 , polarization capacitance C 1 C 2 , including the time constant and correlation coefficient τ of the solid phase diffusion module sd , k sd ,Right now

[0110] S2022: construct a sparse model corresponding to the initial equivalent circuit model based on the parameters to be identified and the loss function.

[0111] Exemplarily, the parameters to be identified are grouped according to the circuit modules to which they belong to obtain parameter groups, and a sparsity inducing function is constructed based on the parameter groups. The sparsity inducing function is used as a regular term to penalize the objective function, or it can be used as a constraint condition of the loss function (the specific setting can depend on the actual scenario) to obtain a sparse model.

[0112] In some embodiments, based on the parameters to be identified and the loss function, a sparse model of the initial equivalent circuit model is constructed, including:

[0113] Based on the parameters to be identified, a sparsity inducing function is constructed; the sparsity inducing function is used as the sparse penalty term of the loss function to obtain a sparse model.

[0114] Among them, the sparse penalty term can be a regularization term or constraint condition of the loss function, and the constraint conditions of the sparse model also include the state space equation and the constraint range of each parameter to be identified. Since the loss function is derived based on the state space equation, the constraint conditions of the constructed sparse model also include the relationship between the state space equation, the constraint range satisfied by each parameter, and the number of circuit modules included in the target equivalent circuit model; for example, the target scenario has a clear limit on the number of circuit modules used (that is, the target ECM or EECM model requires several circuit modules).

[0115] For example, the sparsity induction function is used as a regularization term to penalize the objective function (as shown in Model 1), or as a constraint condition of the loss function (as shown in Model 2). The following are two model forms of adding regularization terms (Model 1) and constraints (Model 2).

[0116] First model (Model 1):

[0117]

[0118] Second model (Model 2):

[0119]

[0120] Among them, in model 1 is a sparsity induction function, which can be expressed based on a norm expression; λ represents the regularization term coefficient, which is an adjustable parameter. The larger λ is, the higher the importance of the regularization term is. It can be set based on actual conditions. S in model 2 is the number restriction condition for the circuit modules included in the target scenario. Different numbers and types of modules constitute different types of ECM (or EECM) models.

[0121] In some embodiments, based on the parameters to be identified, constructing a sparsity inducing function includes:

[0122] Based on the circuit modules to which each electrical component in the initial equivalent circuit model belongs, the parameters to be identified are divided to obtain parameter groups corresponding to each circuit module; based on the parameter groups of each circuit module, a sparsity inducing function is constructed.

[0123] For example, Figure 1 For example, the parameter group Initial SOC of charging including equivalent circuit module 0 、Battery capacity Q 0 、Ohmic resistance R 0 、Polarization internal resistance R 1 and R 2 、Polarization capacitance C 1 and C 2 ,It also includes the time constant and correlation coefficient τ of the solid-phase diffusion module sd 、k sd ,That is where SOC 0 、Q 0 can be used to represent the open-circuit voltage OCV module of the model, R 0 is the ohmic internal resistance module, R 1 、C 1 and R 2 、C 2 respectively represent the first-order and second-order RC modules, τ sd 、k sd can be used to represent the solid-phase diffusion module. The 9 parameters to be identified are divided into 5 groups according to the above circuit module classification method (i.e., open-circuit voltage module, ohmic internal resistance module, first-order RC module, second-order RC module, solid-phase diffusion module).

[0124] Correspondingly, denote to represent 5 parameter groups. Define the group sparsity-inducing function to act on the above 5 parameter groups Common sparsity-inducing functions include l 0 ,l 1 ,l p (0 < p < 1) norm, and the norm expression of the sparsity-inducing function is where, x i corresponds to each parameter group, and p is a real number.

[0125] The fitting degree between the simulation working condition data and the measured working condition data is measured by the loss function, ||x|| p represents the l p norm of the parameter group x, and the sparsity between each circuit module is reflected by the l p norm of the parameter group. λ (greater than 0) in Model 1 represents the penalty sparsity of the sparsity degree, and S (taking a positive integer) in Model 2 represents the upper bound of the sparsity degree.

[0126] In some embodiments, the measured working condition data further includes current data and voltage data; the method further includes: inputting the working condition data into the sparse model for calculation to obtain a sparse solution. That is, inputting the current data and voltage data into the sparse model, and calculating the sparse solution corresponding to each parameter group.

[0127] The initial equivalent circuit model includes the circuit modules to which each electrical component belongs, and the parameter group is a set of parameters to be identified included in the circuit module.

[0128] Exemplarily, the measured operating condition data may correspond to a set of specific operating condition data, such as the current data and voltage data of the battery. Since each parameter to be identified in the equivalent circuit model corresponds to a specific physical meaning, each parameter corresponds to a constraint range of values, and the constraint range is known. Accordingly, the process of identifying each parameter can be to solve a set of optimal parameter values ​​within the constraint range of the parameter so that the loss function is minimized, that is, the simulated voltage output by the state space equation is closest to the value of the measured voltage.

[0129] In the calculation process based on the sparse model, the current data and voltage data are input into the loss function, and the optimal value of each parameter to be identified is calculated within the constraint range of each parameter to be identified. That is, within the constraint range of each parameter to be identified, the value of each parameter to be identified is continuously adjusted, combined with the input operating condition data, when the value of the loss function is minimum, combined with the calculation of the sparsity inducing function, based on the combination of the two, the sparse solutions corresponding to each parameter group are determined when each parameter takes the corresponding optimal value.

[0130] For example, the time series data of the current and voltage of the target scene are used as the input of the sparse model. After solving the above sparse model (model 1 or model 2), a set of sparse solutions can be obtained. The contribution of the group of circuit modules to the entire ECM or EECM model is judged according to the values ​​of each sparse solution, so as to select the most appropriate ECM model.

[0131] In another possible implementation, a sparse model of the initial equivalent circuit model is constructed based on the state space equation, the parameters to be identified, and the measured operating condition data of the battery in the target scenario, and further includes:

[0132] Based on the state space equation, the degree of deviation between the simulated working condition data and the measured working condition data is obtained; according to the degree of deviation and the sparse penalty term generated based on the parameters to be identified, a sparse model of the initial equivalent circuit model is constructed.

[0133] Exemplarily, the degree of deviation between the simulated operating condition data output by the state-space equation and the measured operating condition data is used as the target of parameter identification based on the sparse model. That is, the smaller the degree of deviation, the higher the accuracy of the target equivalent circuit model constructed by the parameter identification results determined by the sparse model, and the closer the corresponding output simulated operating condition data is to the actual operating condition data.

[0134] Exemplarily, based on the implementation principle of the above embodiment, the parameters to be identified are grouped, a sparse penalty term is constructed, and a sparse model is obtained.

[0135] For example, based on the group SCAD model, the representation of the sparse penalty term can be in the form of the following system of equations:

[0136]

[0137] Among them, a is a non-negative number, and γ is an amplification factor of a; by adjusting the parameters a and γ, the shape and characteristics of the SCAD penalty function can be controlled.

[0138] For another example, a sparse penalty term of a sparse model can be formed based on the bridge group model. The bridge group model can be expressed as: λ (x) = (|x| - λ) γ , where γ is the penalty exponent for bridge regression.

[0139] Different sparse models can be realized through the above different sparse penalty term construction methods; the contribution of each parameter group can be obtained through parameter identification, thereby realizing the selection of circuit modules included in the equivalent circuit.

[0140] In some embodiments, determining a target equivalent circuit model of a battery in a target scenario based on a sparse solution includes:

[0141] According to the size relationship of the sparse solutions corresponding to each parameter group and the operating condition constraints, the circuit modules included in the target equivalent circuit model are determined.

[0142] The size of the sparse solution is used to indicate the contribution of the circuit module to the target equivalent circuit model, and the operating condition constraint is the quantity restriction condition of the circuit modules included in the target scenario.

[0143] For example, based on the obtained set of sparse solutions If there is the following size relationship between sparse solutions: This means that the contribution of each circuit module in the equivalent circuit model is as follows from large to small: If the target equivalent circuit model of the target scenario is limited to only three circuit modules, the equivalent circuit model that best matches the input operating condition data includes the following modules: Solid phase diffusion module First-order RC module Open Circuit Voltage OCV Module

[0144] In some embodiments, after determining a target equivalent circuit model of the battery in a target scenario based on a sparse solution, the method further includes:

[0145] Based on the measured operating data of the battery under the target scenario, the parameter values ​​corresponding to the various electrical components included in the target equivalent circuit model are determined by calculating the state space equation corresponding to the target equivalent circuit model.

[0146] The target equivalent circuit model after determining the parameter values ​​is used to estimate the health status of the battery. The operating data of the target scenario is substituted into the state space equation, and when the loss function is minimized, the specific values ​​of the corresponding parameters, that is, the parameter values ​​corresponding to each electrical component, are determined.

[0147] Exemplarily, the sparse solution obtained by solving the sparse model based on the initial equivalent circuit model and the parameter values ​​corresponding to each sparse solution are not necessarily the optimal parameter values ​​corresponding to the target equivalent circuit model. Therefore, after determining the circuit modules included in the target equivalent circuit model based on the sparse solution, the state space equation and loss function of the target equivalent circuit model are established; based on the input operating condition data and the constraint range of each parameter, the parameter value that minimizes the loss function is calculated.

[0148] During the calculation process, an initial value can be defined for each parameter based on the constraint range of each parameter, the size of the loss function can be calculated based on the input operating condition data, and based on the size of the loss function, the values ​​of each parameter can be adjusted within the constraint range. The calculation based on the loss function is continued until the result of the loss function is minimized, and the optimal value of each parameter in the target equivalent circuit is determined.

[0149] Through the embodiments of the present application, the loss function is determined based on the state-space equation and a sparse model is constructed, which can reduce the difficulty of parameter identification. Based on different application scenarios, the model that is compatible with the target scenario can be determined more flexibly and reliably based on the sparse solution of the sparse model. While ensuring the accuracy of the model, the number of modules in the model is controlled, the computational complexity is reduced, and the model response efficiency is improved. It can be applied to any scenario where an equivalent circuit model is established for a battery.

[0150] 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.

[0151] Corresponding to the battery equivalent circuit modeling method provided in the above embodiment, Figure 4 A schematic diagram of the structure of a battery equivalent circuit modeling 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.

[0152] Reference Figure 4 , the battery equivalent circuit modeling device comprises:

[0153] An acquisition unit 41 is used to acquire a state space equation of an initial equivalent circuit model, wherein the state space equation includes parameters to be identified of each electrical component;

[0154] A processing unit 42 is used to construct a sparse model of the initial equivalent circuit model based on the state space equation, the parameters to be identified and the measured operating condition data of the battery in the target scenario, and solve the sparse solution of the sparse model;

[0155] The output unit 43 is used to determine a target equivalent circuit model of the battery in the target scenario based on the sparse solution, wherein the target equivalent circuit model includes all or part of the electrical components in the initial equivalent circuit model.

[0156] In a possible implementation, the acquisition unit 41 is further configured to discretize the state-space equation to obtain a discretized state-space equation of the initial equivalent circuit model.

[0157] In one possible implementation, the processing unit 42 is used to obtain the degree of deviation between the simulated operating condition data and the measured operating condition data based on the state-space equation; and construct a sparse model of the initial equivalent circuit model according to the degree of deviation and the sparse penalty term generated based on the parameters to be identified.

[0158] In one possible implementation, the processing unit 42 is used to obtain the loss function of the initial equivalent circuit model based on the state-space equation and the measured operating data of the battery in the target scenario; and construct the sparse model of the initial equivalent circuit model based on the parameters to be identified and the loss function.

[0159] In a possible implementation, the processing unit 42 is also used to obtain the simulation operating condition data corresponding to the measured operating condition data based on the state-space equation; and construct the loss function based on the deviation between the simulation operating condition data and the measured operating condition data; wherein the loss function is represented based on each of the parameters to be identified, and the value of each of the parameters to be identified is within a preset constraint range.

[0160] In a possible implementation, the processing unit 42 is further configured to construct a sparsity inducing function based on the parameters to be identified; and use the sparsity inducing function as a sparse penalty term of the loss function to obtain the sparse model.

[0161] In a possible implementation, the processing unit 42 is also used to divide the parameters to be identified based on the circuit modules to which the various electrical components in the initial equivalent circuit model belong, so as to obtain parameter groups corresponding to the various circuit modules; and construct the sparsity inducing function based on the parameter groups of the various circuit modules.

[0162] In one possible implementation, the measured operating condition data of the battery includes current data and voltage data of the battery; the processing unit 42 is also used to input the current data and the voltage data into the sparse model to calculate the sparse solution corresponding to each parameter group; wherein the initial equivalent circuit model includes the circuit modules to which each of the electrical components belongs, and the parameter group is a set of the parameters to be identified included in the circuit module.

[0163] In one possible implementation, the output unit 43 is also used to determine the circuit modules included in the target equivalent circuit model based on the size relationship of the sparse solutions corresponding to each of the parameter groups and the operating condition constraints; wherein the size of the sparse solution is used to indicate the degree of contribution of the circuit module to the target equivalent circuit model, and the operating condition constraints are the quantity restrictions on the circuit modules included in the target scenario.

[0164] In one possible implementation, the processing unit 42 is also used to determine the parameter values ​​corresponding to each electrical component included in the target equivalent circuit model based on the measured operating condition data of the battery in the target scenario by calculating the state space equation corresponding to the target equivalent circuit model; wherein the target equivalent circuit model after determining the parameter values ​​is used to estimate the health status of the battery.

[0165] Through the embodiments of the present application, a sparse solution is obtained based on a sparse model, and a target equivalent circuit model corresponding to a target scenario is determined, so that model selection suitable for different real vehicle scenarios can be efficiently completed.

[0166] Figure 5 A schematic diagram of the hardware structure of the battery management chip 5 is shown.

[0167] like Figure 5 As shown, the battery management chip 5 of this embodiment includes: at least one processor 50 ( Figure 5 Only one is shown in the figure), a memory 51, in which a computer program 52 that can be run on the processor 50 is stored. When the processor 50 executes the computer program 52, the steps in the above method embodiment are implemented, such as Figure 2 Alternatively, when the processor 50 executes the computer program 52, the functions of the modules / units in the above-mentioned device embodiments are realized.

[0168] It is understood that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the battery management chip 5. In other embodiments of the present application, the battery management chip 5 may include more or fewer components than shown in the figure, or combine certain components, or separate certain components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0169] The battery management chip 5 may include, but is not limited to, a processor 50 and a memory 51. Those skilled in the art can understand that Figure 5 These are merely examples of the battery management chip 5 and do not constitute a limitation on the battery management chip 5. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the server may also include an input sending device, a network access device, a bus, etc.

[0170] The above-mentioned processor 50 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0171] A memory may also be provided in the processor 50 for storing instructions and data. In some embodiments, the memory in the processor 50 is a cache memory. This memory can save the instructions or data that the processor 50 has just used or recycled. If the processor 50 needs to use the instruction or data again, it can directly call it from the memory. This avoids repeated accesses, reduces the waiting time of the processor 50, and thus improves the efficiency of the system.

[0172] In some embodiments, the above-mentioned memory 51 may be an internal storage unit of the battery management chip 5, such as the hard disk or memory of the battery management chip 5. The memory 51 may also be an external storage device of the battery management chip 5, such as a plug-in hard disk equipped on the battery management chip 5, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 51 may also include both the internal storage unit and the external storage device of the battery management chip 5. The memory 51 is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of a computer program, etc. The memory 51 may also be used to temporarily store data that has been sent or will be sent.

[0173] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0174] It should be noted that the above structure is only an example, and based on different application scenarios, it may also include other physical structures, and the physical structure of the battery management chip is not limited here.

[0175] Figure 6 The schematic diagram of the structure of the electric device provided in the embodiment of the present application is shown. For the convenience of explanation, only the part related to the embodiment of the present application is shown. The electric device 6 can be a terminal device, an electric vehicle or an energy storage device. Figure 6 As shown, the electric device 6 may include a battery management chip 5 and a battery 60. The battery management chip 5 is electrically connected to the battery 60 and monitors and manages the battery 60. The battery management chip 5 is used to accurately model the battery 60 under different working conditions, accurately estimate the health status, monitor the battery parameters in real time, and perform balanced management and thermal management, so as to improve the utilization rate of the battery, reduce the risk of overcharging and over-discharging of the battery, and thus extend the service life of the battery.

[0176] 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.

[0177] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0178] The embodiment of the present application provides a computer program product. When the computer program product runs on a computer, the computer can implement the steps in the above-mentioned method embodiments when executing the computer.

[0179] If the integrated module / 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 process in the above-mentioned embodiment method, and can also 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. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0180] The battery management chip, electrical equipment, computer storage medium, and computer program product provided in the above-mentioned embodiments of the present application are all used to execute the method provided above. Therefore, the beneficial effects that can be achieved can refer to the corresponding beneficial effects of the method provided above, and will not be repeated here.

[0181] It should be understood that the above is only to help those skilled in the art better understand the embodiments of the present application, rather than to limit the scope of the embodiments of the present application. According to the above examples given, those skilled in the art can obviously make various equivalent modifications or changes. For example, some steps in each embodiment of the above method may be unnecessary, or some new steps may be added. Or a combination of any two or any multiple embodiments of the above. Such modifications, changes or combined solutions also fall within the scope of the embodiments of the present application.

[0182] It should also be understood that the division of the methods, situations, categories and embodiments in the embodiments of the present application is only for the convenience of description and should not constitute a special limitation. The features of various methods, categories, situations and embodiments can be combined without contradiction.

[0183] It should also be understood that in the various embodiments of the present application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships.

[0184] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0185] The above-described embodiments are only used to illustrate the technical solutions of this application, rather than to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included in the protection scope of this application.

[0186] Finally, it should be noted that: the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any changes or replacements within the technical scope disclosed in this application should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claimed rights.

Claims

1. A battery equivalent circuit modeling method, It is characterized in that include: Obtaining a state space equation of an initial equivalent circuit model, wherein the state space equation includes parameters to be identified of each electrical component; Based on the state-space equation, the parameters to be identified and the measured operating condition data of the battery in the target scenario, a sparse model of the initial equivalent circuit model is constructed, and a sparse solution of the sparse model is obtained; Based on the sparse solution, a target equivalent circuit model of the battery in the target scenario is determined, wherein the target equivalent circuit model includes all or part of the electrical elements in the initial equivalent circuit model.

2. The method according to claim 1, It is characterized in that After obtaining the state space equation of the initial equivalent circuit model, the method further includes: The state space equation is discretized to obtain the discretized state space equation of the initial equivalent circuit model.

3. The method according to claim 1 or 2, It is characterized in that The sparse model of the initial equivalent circuit model is constructed based on the state space equation, the parameters to be identified and the measured operating condition data of the battery in the target scenario, including: Based on the state-space equation, obtaining the degree of deviation between the simulated operating condition data and the measured operating condition data; The sparse model of the initial equivalent circuit model is constructed according to the degree of deviation and a sparse penalty term generated based on the parameters to be identified.

4. The method according to claim 1 or 2, It is characterized in that The sparse model of the initial equivalent circuit model is constructed based on the state space equation, the parameters to be identified and the measured operating condition data of the battery in the target scenario, including: Constructing a loss function of the initial equivalent circuit model according to the state space equation and the measured operating condition data of the battery in the target scenario; Based on the parameters to be identified and the loss function, the sparse model of the initial equivalent circuit model is constructed.

5. The method according to claim 4, It is characterized in that The step of constructing the loss function of the initial equivalent circuit model according to the state space equation and the measured operating condition data of the battery in the target scenario includes: Based on the state-space equation, obtaining simulation operating condition data corresponding to the measured operating condition data; Constructing the loss function based on the deviation between the simulated working condition data and the measured working condition data; The loss function is represented based on each of the parameters to be identified, and the value of each of the parameters to be identified is within a preset constraint range.

6. The method according to claim 4, It is characterized in that The step of constructing the sparse model of the initial equivalent circuit model based on the parameters to be identified and the loss function includes: Based on the parameters to be identified, construct a sparsity inducing function; The sparsity inducing function is used as a sparse penalty term of the loss function to obtain the sparse model.

7. The method according to claim 6, It is characterized in that The step of constructing a sparsity inducing function based on the parameter to be identified includes: Based on the circuit modules to which the electrical components in the initial equivalent circuit model belong, the parameters to be identified are divided to obtain parameter groups corresponding to the circuit modules; The sparsity inducing function is constructed based on the parameter groups of each of the circuit modules.

8. The method according to any one of claims 1 to 7, It is characterized in that The measured operating condition data includes current data and voltage data of the battery; and the sparse solution of the sparse model includes: Inputting the current data and the voltage data into the sparse model, and calculating the sparse solution corresponding to each parameter group; The initial equivalent circuit model includes the circuit module to which each of the electrical components belongs, and the parameter group is a set of the parameters to be identified included in the circuit module.

9. The method according to claim 8, It is characterized in that The determining, based on the sparse solution, a target equivalent circuit model of the battery in the target scenario includes: Determining the circuit modules included in the target equivalent circuit model according to the size of the sparse solution corresponding to each of the parameter groups and the operating condition constraints; The size of the sparse solution is used to indicate the contribution of the circuit module to the target equivalent circuit model, and the operating condition constraint is a quantity restriction condition for the circuit modules included in the target scenario.

10. The method according to any one of claims 1 to 9, It is characterized in that After determining the target equivalent circuit model of the battery in the target scenario based on the sparse solution, the method further includes: Based on the measured operating condition data of the battery in the target scenario, determining parameter values ​​corresponding to each electrical component included in the target equivalent circuit model by calculating the state space equation corresponding to the target equivalent circuit model; The target equivalent circuit model after determining the parameter value is used to estimate the health state of the battery.

11. A battery equivalent circuit modeling device, It is characterized in that include: An acquisition unit, used for acquiring a state space equation of an initial equivalent circuit model, wherein the state space equation includes parameters to be identified of each electrical component; A processing unit, configured to construct a sparse model of the initial equivalent circuit model based on the state-space equation, the parameters to be identified, and the measured operating condition data of the battery in the target scenario, and to solve a sparse solution of the sparse model; An output unit is used to determine a target equivalent circuit model corresponding to the battery in the target scenario based on the sparse solution; the target equivalent circuit model includes all or part of the electrical elements in the initial equivalent circuit model.

12. A battery management chip, It is characterized in that The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method according to any one of claims 1 to 10 when executing the computer program.

13. An electrical device, It is characterized in that It comprises a battery and a battery management chip as claimed in claim 12; the battery management chip is electrically connected to the battery and monitors and manages the battery.

14. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

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