Battery state-of-health estimation method, electronic device and computer-readable storage medium

Abstract

Provided in the embodiments of the present application are a battery state-of-health estimation method, an electronic device and a computer-readable storage medium. The battery state-of-health estimation method comprises: acquiring historical operation condition data of a battery module under test; according to the historical operation condition data, performing parameter identification on battery parameters of the battery module, so as to obtain an identification result; and according to the identification result, predicting the state of health of a battery in the battery module. By means of the method, the estimation precision of the state of health of a battery can be effectively improved.

Description

Battery health state estimation method, electronic device and computer-readable storage medium

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on July 3, 2023, with application number 202310808644.2 and invention name “Battery Health State Estimation Method, Electronic Device and Computer-Readable Storage Medium”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of batteries, and in particular to a battery health status estimation method, an electronic device, and a computer-readable storage medium. Background Art

[0003] With the development of electric vehicles, battery life is gaining increasing attention. Battery state of health (SOH), also known as the battery attenuation coefficient, reflects the degree of battery aging and health. A higher SOH value indicates less aging and better health; a lower SOH value indicates more aging and worse health. However, related SOH estimation methods often have low accuracy, which affects users' understanding of battery status. Technical issues

[0004] One of the purposes of the embodiments of the present application is to provide a battery health status estimation method, an electronic device, and a computer-readable storage medium, which solve the problem of low battery health status estimation accuracy. Technical Solutions

[0005] To solve the above technical problems, the technical solutions adopted in the embodiments of the present application are:

[0006] In a first aspect, a battery health state estimation method is provided, comprising:

[0007] Obtain historical operating condition data of the battery module to be tested;

[0008] Performing parameter identification on battery parameters of the battery module according to the historical operating condition data to obtain an identification result;

[0009] The health status of the batteries in the battery module is predicted according to the identification result.

[0010] In the embodiments of the present application, battery parameter identification is performed based on the battery module's historical operating data, which is equivalent to an offline identification method. Compared with real-time identification, the offline identification method can avoid the lack of stability in the identification results caused by recursive solution. In addition, because the parameter identification is performed based on the battery module's historical operating data, the battery parameters of the battery module can be fitted more accurately, thereby effectively improving the accuracy of battery health estimation.

[0011] In an implementation of the first aspect, performing parameter identification on the battery parameters of the battery module according to the historical operating condition data to obtain an identification result includes:

[0012] Obtaining a mathematical model of the battery module, wherein the mathematical model is constructed according to battery parameters of the battery module;

[0013] Parameter identification is performed on battery parameters of the battery module according to the historical operating condition data and the mathematical model to obtain an identification result.

[0014] In the embodiments of the present application, parameter identification is performed in conjunction with a mathematical model to identify the parameters of the battery module, and then the battery health status is predicted based on the identified battery module parameters. Because the mathematical model is constructed based on the battery parameters of the battery module, it can accurately reflect the battery module status and provide a reliable model foundation for subsequent parameter identification. In addition, the parameter identification method can accurately fit the battery module battery parameters. Combining the mathematical model and parameter identification method can effectively improve the accuracy of battery health estimation.

[0015] In an implementation of the first aspect, performing parameter identification on the battery parameters of the battery module according to the historical operating condition data and the mathematical model to obtain an identification result includes:

[0016] Discretize the mathematical model according to the historical operating condition data to obtain the discretized mathematical model;

[0017] Parameter identification is performed on battery parameters of the battery module according to the historical operating condition data and the discretized mathematical model to obtain an identification result.

[0018] In practical applications, the sampled data of the observable parameters of the battery module are usually discrete. In the embodiment of the present application, the mathematical model is discretized so that the mathematical model can more accurately reflect the actual state of the battery module, which is conducive to improving the estimation accuracy of the health status of the battery.

[0019] In an implementation of the first aspect, discretizing the mathematical model according to the historical operating condition data to obtain the discretized mathematical model includes:

[0020] Obtaining the sampling frequency of the historical operating condition data;

[0021] The mathematical model is discretized according to the sampling frequency of the historical operating condition data to obtain the discretized mathematical model.

[0022] In this embodiment, the mathematical model is discretized based on the sampling frequency of the battery module to ensure that the time intervals in the mathematical model are consistent with the actual sampling frequency. This allows the mathematical model to more accurately reflect the actual state of the battery module, thereby improving the accuracy of the battery health state estimation.

[0023] In an implementation of the first aspect, performing parameter identification on battery parameters of the battery module based on the historical operating condition data and the discretized mathematical model to obtain an identification result includes:

[0024] Establishing an identification model based on the discretized mathematical model;

[0025] Parameter identification is performed according to the historical operating condition data and the identification model to obtain the identification result.

[0026] In the embodiment of the present application, converting the mathematical model of the battery module into an identification model is equivalent to converting a practical problem into a mathematical problem, which facilitates subsequent calculations.

[0027] In an implementation of the first aspect, establishing an identification model based on the discretized mathematical model includes:

[0028] Constructing an objective function based on the discretized mathematical model;

[0029] Obtaining constraints on independent variables in the mathematical model;

[0030] The identification model is established according to the objective function and the constraint conditions.

[0031] In practical applications, the battery parameters of a battery module cannot take on unlimited values. In the embodiments of the present application, adding constraints to the independent variables of the mathematical model is equivalent to limiting the range of values ​​of the battery parameters in the battery module, that is, identifying the battery parameters within a certain range of values. In this way, the optimal solution can be quickly identified, and the obtained optimal solution is more consistent with the actual situation, thereby improving the estimation accuracy of the battery health status.

[0032] In an implementation of the first aspect, the independent variable includes an initial state of charge of the battery module;

[0033] The obtaining of the constraint conditions of the independent variables in the mathematical model includes:

[0034] Acquiring historical data of the initial state of charge from the historical operating condition data;

[0035] The constraint condition of the initial state of charge is determined according to the first frame data in the historical data of the initial state of charge.

[0036] In an implementation of the first aspect, the independent variable includes a battery capacity of the battery module;

[0037] The obtaining of the constraint conditions of the independent variables in the mathematical model includes:

[0038] Acquiring historical charging data of the battery module from the historical operating condition data;

[0039] estimating the battery capacity of the battery module according to the historical charging data to obtain an estimated value of the battery capacity;

[0040] The constraint condition of the battery capacity is determined according to the estimated value of the battery capacity.

[0041] In the above embodiment, the constraints of the independent variables are set according to the historical data of the independent variables. Since the historical data is the actual operating condition data, the constraints set according to the historical data can more accurately reflect the actual state of the battery module, thereby improving the reliability of the identification results.

[0042] In an implementation of the first aspect, the constraint conditions include linear constraints and nonlinear constraints;

[0043] The step of establishing the identification model according to the objective function and the constraint conditions includes:

[0044] Adding a multiplier to the nonlinear constraint in the constraint condition to obtain a nonlinear constraint term;

[0045] Adding the nonlinear constraint term to the objective function to obtain an updated objective function;

[0046] The identification model is established according to the updated objective function and the linear constraints in the constraint conditions.

[0047] In the embodiment of the present application, by adding a multiplier to the nonlinear constraint, the nonlinear constraint term is added to the objective function, which is equivalent to converting the problem with nonlinear constraints into a problem without nonlinear constraints, thereby reducing the algorithm complexity of parameter identification.

[0048] In an implementation of the first aspect, the independent variables of the mathematical model include observed quantities and unobserved quantities; the historical operating condition data include initial values ​​of the unobserved quantities, historical observation data of the observed quantities, and historical observation data of the dependent variables of the mathematical model;

[0049] The performing parameter identification according to the historical operating condition data and the identification model to obtain the identification result includes:

[0050] Inputting the initial value of the non-observed quantity, the historical observation data of the observed quantity, and the historical observation data of the dependent variable into the identification model to obtain a first value of the objective function;

[0051] Adjusting the value of the non-observed quantity according to the first value to obtain a first adjusted value of the non-observed quantity;

[0052] Inputting the first adjustment value, the historical observation data of the observation quantity, and the historical observation data of the dependent variable into the identification model to obtain a second value of the objective function;

[0053] If the second value does not satisfy the preset condition, continue adjusting the value of the non-observed quantity according to the second value until the value of the objective function satisfies the preset condition;

[0054] If the second value satisfies a preset condition, the current value of the non-observed quantity is determined as the identification result.

[0055] In the embodiment of the present application, during the parameter identification process, the actual operating condition data of the battery module is used for iterative calculation rather than recursively solving it in advance, so that the identification result is more stable and reliable.

[0056] In an implementation of the first aspect, adjusting the value of the non-observed quantity according to the first value to obtain a first adjusted value of the non-observed quantity includes:

[0057] determining a feasible range of the non-observed quantity according to the first value;

[0058] The first adjustment value is determined from the feasible range, wherein a value of the objective function corresponding to the first adjustment value is smaller than a value of the objective function corresponding to any adjustment value within the feasible range.

[0059] In this embodiment, the feasible range of the unobservable variable is continuously adjusted based on the value of the objective function, and then the point with the minimum value of the objective function is sought within the feasible range. This is equivalent to converting the point search problem into a local range search problem. This method can quickly converge to the optimal solution, effectively improving the efficiency of parameter identification and thus improving the efficiency of battery health estimation.

[0060] In an implementation of the first aspect, the method further includes:

[0061] The mathematical model of the battery module is optimized according to a preset period.

[0062] By regularly optimizing the mathematical model of the battery module, the mathematical model changes with the use of the battery module and becomes closer to the actual working conditions of the battery module, thereby effectively avoiding poor identification results caused by inaccurate mathematical models and improving the reliability of the identification results.

[0063] In an implementation of the first aspect, the step of updating the mathematical model of the battery module each time includes:

[0064] determining dependent variables of the mathematical model according to the battery parameters;

[0065] determining an independent variable related to the dependent variable based on the battery parameter;

[0066] The mathematical model is constructed according to the independent variables and the dependent variables.

[0067] In the embodiment of the present application, the dependent variables and independent variables of the mathematical model of the battery module are determined according to the battery parameters of the battery module, so that the established mathematical model can more accurately reflect the state of the battery module and provide a reliable model basis for subsequent parameter identification.

[0068] In an implementation of the first aspect, constructing the mathematical model according to the independent variable and the dependent variable includes:

[0069] Obtaining an equivalent circuit model of the battery module;

[0070] The mathematical model between the independent variable and the dependent variable is constructed according to the equivalent circuit model.

[0071] In the embodiments of the present application, a mathematical model of the battery module is constructed based on the equivalent circuit model of the battery module. Because the equivalent circuit model typically replaces a relatively complex structure with a relatively simple structure, and the replaced circuit maintains the same effect as the original circuit on the untransformed portion, this approach can simplify the model while ensuring that the mathematical model can accurately reflect the state of the battery module, thereby simplifying subsequent calculations.

[0072] In an implementation of the first aspect, the equivalent circuit model includes a first resistor, a second resistor, a first capacitor, and a battery, wherein the second resistor and the first capacitor are connected in parallel to form a parallel circuit, and the battery, the first internal resistor, and the parallel circuit are connected in series;

[0073] In a case where the dependent variable is the output voltage of the battery module, constructing the mathematical model between the independent variable and the dependent variable according to the equivalent circuit model includes:

[0074] constructing a first expression for the open circuit voltage of the battery based on the independent variable, the open circuit voltage representing the potential difference between the positive electrode and the negative electrode of the battery;

[0075] constructing a second expression for the internal resistance voltage of the first internal resistor based on the independent variable, wherein the internal resistance voltage represents the potential difference between two connection terminals of the first internal resistor;

[0076] constructing a third expression for a parallel voltage of the parallel circuit based on the independent variable, the parallel voltage representing a potential difference between two connection terminals of the parallel circuit;

[0077] The mathematical model is constructed according to the first expression, the second expression and the third expression.

[0078] In the embodiment of the present application, the equivalent circuit model adopts a simple connection mode of resistors, capacitors and batteries in series and parallel, and the equivalent circuit model includes only one dynamic element (capacitor), that is, the equivalent circuit model is equivalent to a first-order circuit, and its corresponding state equation is a first-order linear ordinary differential equation. Such an equivalent circuit model structure is relatively simple, and the state equation is linear. Therefore, the mathematical model constructed based on the equivalent circuit model is relatively simple and convenient for subsequent calculations.

[0079] Furthermore, in the embodiments of the present application, the first, second, and third expressions are all expressions representing voltages, which is equivalent to establishing a mathematical model based on the relationship between the voltages of each branch in the battery module. In this way, the established mathematical model can reflect the state of the battery module's output voltage. Since the battery module's output voltage is observable, this approach provides a model foundation for subsequent parameter identification.

[0080] In an implementation of the first aspect, the independent variables include the output current, battery capacity, and initial state of charge of the battery module;

[0081] The step of constructing a first expression for the open circuit voltage of the battery according to the independent variable comprises:

[0082] constructing a fourth expression for the real-time state of charge according to the output current, the battery capacity, and the initial state of charge among the independent variables;

[0083] The first expression is constructed according to the fourth expression and the relationship between the real-time state of charge and the open circuit voltage.

[0084] Independent variables such as battery capacity and initial state of charge are closely related to the battery's state of health. In the embodiments of the present application, expressions are constructed between these independent variables and the branch voltages of the battery module. During the subsequent parameter identification process, parameter identification can be performed on these independent variables related to the battery's state of health, thereby facilitating the calculation of the battery's state of health based on the identified parameters (independent variables). In this way, after obtaining the parameter identification results, there is no need to convert the identified parameters into parameters related to the battery's state of health. Instead, the identified parameters are directly used to calculate the battery's state of health, thereby simplifying the steps of the estimation method.

[0085] In an implementation of the first aspect, the independent variable includes the first resistance; and constructing a second expression for the internal resistance voltage of the first internal resistance based on the independent variable includes:

[0086] The second expression is constructed based on the output current, the first resistance, and the fourth expression among the independent variables.

[0087] The independent variables such as the first resistance, battery capacity and initial state of charge are unobservable parameters in the battery module, and the independent variables such as battery capacity and initial state of charge are closely related to the health state of the battery. In an embodiment of the present application, constructing an expression between the above-mentioned independent variables and the branch voltage of the battery module is equivalent to establishing a relationship between the unobservable parameters in the battery module and the observable parameters (output voltage) of the battery module. In the subsequent identification process, the data of the unobservable parameters and the parameters related to the health state of the battery can be identified through the data of the observable parameters. In the above manner, a model basis is provided for subsequent parameter identification, which facilitates obtaining a more accurate state of the battery module, thereby providing an estimation accuracy of the health state of the battery.

[0088] In an implementation of the first aspect, the independent variables include the output current of the battery module, the first capacitor, and the second resistor;

[0089] The constructing of a third expression for the parallel voltage of the parallel circuit according to the independent variable comprises:

[0090] The third expression is constructed based on the output current, the first capacitance, and the second resistance among the independent variables.

[0091] The first capacitor and the first resistor are unobservable parameters in the battery module. In the embodiment of the present application, constructing an expression between the above-mentioned independent variables and the branch voltage of the battery module is equivalent to establishing a relationship between the unobservable parameters in the battery module and the observable parameters (output voltage) of the battery module. In the subsequent identification process, the data of the unobservable parameters can be identified by the data of the observable parameters. In the above manner, a model basis is provided for subsequent parameter identification, which facilitates obtaining a more accurate state of the battery module, thereby providing an estimation accuracy of the battery's health status.

[0092] In an implementation of the first aspect, the identification result includes a battery capacity of the battery module;

[0093] The predicting the health status of the battery in the battery module according to the identification result includes:

[0094] The health status of the batteries in the battery module is predicted according to the value of the battery capacity in the identification result.

[0095] In the embodiment of the present application, since the identified battery parameters of the battery module are relatively accurate, the health status of the battery estimated based on the identification result is also more accurate.

[0096] In a second aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the battery health status estimation method as described in any one of the first aspects is implemented.

[0097] In a third aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the battery health status estimation method as described in any one of the first aspects is implemented.

[0098] In a fourth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on a terminal device, the terminal device executes the battery health status estimation method described in any one of the first aspects above.

[0099] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0100] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present application. The same reference numerals are used throughout the drawings to represent the same components. In the drawings:

[0102] FIG1 is a flow chart of a method for estimating a battery health state according to an embodiment of the present application;

[0103] FIG2 is a flow chart of a mathematical model construction method provided in an embodiment of the present application;

[0104] FIG3 is a schematic diagram of a second-order equivalent circuit provided in an embodiment of the present application;

[0105] FIG4 is a schematic diagram of an equivalent circuit provided in an embodiment of the present application;

[0106] FIG5 is a flow chart of a parameter identification method provided in an embodiment of the present application;

[0107] FIG6 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Modes for Carrying Out the Invention

[0108] It is only used to more clearly illustrate the technical solution of the present application and is therefore only an example and cannot be used to limit the scope of protection of the present application.

[0109] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art 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" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0110] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0111] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0112] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0113] With the development of electric vehicles, battery life is gaining increasing attention. Battery state of health (SOH), also known as the battery attenuation coefficient, reflects the degree of battery aging and health. A higher SOH value indicates less aging and better health; a lower SOH value indicates more aging and worse health. However, related SOH estimation methods often have low accuracy, which affects users' understanding of battery status.

[0114] To address the above issues, embodiments of the present application provide a method for estimating the battery state of health. In this embodiment, the battery module to be tested is converted into a mathematical model, and parameter identification is performed in conjunction with the mathematical model to identify the battery module parameters. The battery state of health is then predicted based on the identified battery module parameters. This method effectively improves the accuracy of battery state of health estimation.

[0115] The following describes a method for estimating the battery health status provided by an embodiment of the present application with reference to a specific embodiment.

[0116] 1 is a flow chart of a method for estimating the state of health of a battery provided in an embodiment of the present application. As an example and not a limitation, as shown in FIG1 , the method for estimating the state of health of a battery may include the following steps: S101, obtaining historical operating condition data of a battery module to be tested.

[0117] In the embodiment of the present application, the historical operating condition data is actual data corresponding to the battery parameters of the battery module during the historical operation of the battery module.

[0118] S102 , performing parameter identification on battery parameters of the battery module according to the historical operating condition data to obtain an identification result.

[0119] In the embodiments of the present application, a battery module includes batteries (or cells) and circuit components for regulating the output current or output voltage of the battery module. Accordingly, the battery parameters of the battery module may include the output current, output voltage, open-circuit voltage of the battery, and parameters of the circuit components in the battery module.

[0120] Parameter identification technology is a technique that combines theoretical models with experimental data for prediction. Parameter identification determines a set of model parameters based on experimental data and the established model, so that the numerical results calculated by the model can best fit the test data (considered as a curve fitting problem), thereby enabling predictions of unknown processes and providing certain theoretical guidance. In specific studies, a rough model is first established, and this rough model is used to predict the experimental measurement results. When the error between the calculated numerical results and the test values ​​is large, it is considered that the mathematical model does not match the actual process or the gap is large, and then the model is modified and reselected for modification. When the predicted results are consistent with the measured results, the model is considered to have a high degree of credibility.

[0121] In an embodiment of the present application, the battery parameters of the battery module include observable quantities (parameter values ​​can be obtained through observation) and unobservable quantities (parameter values ​​cannot be obtained through observation). Through parameter identification, the data of unobservable quantities in the battery module can be fitted to obtain the working status of the battery module, and then the health status of the batteries in the battery module can be predicted.

[0122] S103: Predicting the health status of batteries in the battery module according to the identification result.

[0123] In the embodiment shown in Figure 1, battery parameters are identified based on the battery module's historical operating data, which is equivalent to an offline identification method. Compared with real-time identification, the offline identification method can avoid the lack of stability in the identification results caused by recursive solution. In addition, because the parameter identification is performed based on the battery module's historical operating data, the battery parameters of the battery module can be fitted more accurately, thereby effectively improving the accuracy of battery health estimation.

[0124] In some embodiments, S102 may include:

[0125] Obtaining a mathematical model of the battery module, wherein the mathematical model is constructed according to battery parameters of the battery module;

[0126] Parameter identification is performed on battery parameters of the battery module according to the historical operating condition data and the mathematical model to obtain an identification result.

[0127] The mathematical model constructed in the embodiment of the present application can reflect the circuit operation status of the battery module. The construction process of the mathematical model is described in detail in the following mathematical model optimization embodiment, which will not be repeated here.

[0128] In the embodiments of the present application, parameter identification is performed in conjunction with a mathematical model to identify the parameters of the battery module, and then the battery health status is predicted based on the identified battery module parameters. Because the mathematical model is constructed based on the battery parameters of the battery module, it can accurately reflect the battery module status and provide a reliable model foundation for subsequent parameter identification. In addition, the parameter identification method can accurately fit the battery module battery parameters. Combining the mathematical model and parameter identification method can effectively improve the accuracy of battery health estimation.

[0129] In some embodiments, the method further comprises:

[0130] The mathematical model of the battery module is optimized according to a preset period.

[0131] By regularly optimizing the mathematical model of the battery module, the mathematical model changes with the use of the battery module and becomes closer to the actual working conditions of the battery module, thereby effectively avoiding poor identification results caused by inaccurate mathematical models and improving the reliability of the identification results.

[0132] In some embodiments, referring to FIG2 , which is a flow chart of a mathematical model construction method provided in an embodiment of the present application, as shown in FIG2 , each step of optimizing the mathematical model of a battery module may include:

[0133] S201: Determine dependent variables of the mathematical model according to the battery parameters.

[0134] S202: Determine an independent variable related to the dependent variable according to the battery parameter.

[0135] S203: Construct the mathematical model according to the independent variables and the dependent variables.

[0136] In some examples, the dependent variable may be the output voltage of the battery module, and the independent variables may include the output current of the battery module, the resistance and capacitance in the battery module, the battery capacity and state of charge of the battery, etc.

[0137] In other examples, the dependent variable may be the output current of the battery module, and the independent variables may include the output voltage of the battery module, the resistance and capacitance in the battery module, the battery capacity and state of charge of the battery, etc.

[0138] In practical applications, the calculations involved in output voltage are simpler than those of output current. Therefore, the output voltage can be used as the dependent variable.

[0139] In the embodiment of the present application, the dependent variables and independent variables of the mathematical model of the battery module are determined according to the battery parameters of the battery module, so that the established mathematical model can more accurately reflect the state of the battery module and provide a reliable model basis for subsequent parameter identification.

[0140] In some embodiments, an implementation of S201 may include:

[0141] Acquire an equivalent circuit model of the battery module; and construct the mathematical model between the independent variable and the dependent variable according to the equivalent circuit model.

[0142] Among them, the equivalent circuit refers to replacing a relatively complex structure of a part of the circuit with a relatively simple structure, and the circuit after replacement maintains the same effect on the untransformed part (or external circuit) as the original circuit.

[0143] In the embodiment shown in FIG2 , a mathematical model of the battery module is constructed based on the equivalent circuit model of the battery module. Because the equivalent circuit model typically replaces a relatively complex structure with a simpler one, the replaced circuit maintains the same effect as the original circuit on the untransformed portion. Therefore, this approach simplifies the model while ensuring that the mathematical model accurately reflects the state of the battery module, thereby simplifying subsequent calculations.

[0144] In some implementations, the equivalent circuit model can adopt a second-order RC equivalent circuit. Referring to Figure 3, it is a schematic diagram of a second-order equivalent circuit provided in an embodiment of the present application. A second-order equivalent circuit generally includes two dynamic elements. For example, in the second-order RC equivalent circuit shown in Figure 3, there are three resistors R1, R2, and R3, two capacitors C1 and C2, and a battery B1. Among them, capacitors C1 and C2 are dynamic elements. Accordingly, the corresponding state equation is a second-order nonlinear equation, and the calculation is relatively cumbersome. In the embodiment of the present application, in order to simplify the calculation, the following method can be adopted.

[0145] In one implementation, the equivalent circuit model includes a first resistor, a second resistor, a first capacitor, and a battery, wherein the second resistor and the first capacitor are connected in parallel to form a parallel circuit, and the battery, the first internal resistor, and the parallel circuit are connected in series.

[0146] Referring to Figure 4, it is a schematic diagram of the equivalent circuit provided in an embodiment of the present application. As an example and not a limitation, as shown in Figure 4, the equivalent circuit includes a first resistor R0, a second resistor Rp, a first capacitor Cp and a battery B0. Among them, the first resistor R0 is equivalent to the ohmic internal resistance of the battery module, that is, it is composed of the electrode material, electrolyte, diaphragm internal resistance and the contact resistance of each part. The second resistor Rp is equivalent to the polarization internal resistance of the battery module, that is, the internal resistance caused by polarization during the electrochemical reaction, usually including the resistance caused by electrochemical polarization and concentration polarization. The first capacitor Cp is equivalent to the polarization capacitance of the battery module, that is, a capacitor with positive and negative poles. The polarization capacitance and the polarization internal resistance are connected in parallel to form a capacitance-resistance loop, which is used to simulate the dynamic characteristics exhibited during the generation and elimination of battery polarization.

[0147] In the embodiment of the present application, the equivalent circuit model adopts a simple connection mode of resistors, capacitors and batteries in series and parallel, and the equivalent circuit model includes only one dynamic element (capacitor), that is, the equivalent circuit model is equivalent to a first-order circuit, and its corresponding state equation is a first-order linear ordinary differential equation. Such an equivalent circuit model structure is relatively simple, and the state equation is linear. Therefore, the mathematical model constructed based on the equivalent circuit model is relatively simple and convenient for subsequent calculations.

[0148] For ease of description, in the following embodiments, the dependent variable is taken as the output voltage of the battery module as an example to illustrate the process of constructing the mathematical model.

[0149] The constructing the mathematical model between the independent variable and the dependent variable according to the equivalent circuit model includes:

[0150] 1. Construct a first expression for the open circuit voltage of the battery based on the independent variable, wherein the open circuit voltage represents the potential difference between the positive electrode and the negative electrode of the battery.

[0151] The open circuit voltage of the battery is shown in Figure 4. ocv .

[0152] Optionally, the independent variables include the output current, battery capacity and initial state of charge of the battery module. The first expression in step 1 is constructed as follows:

[0153] constructing a fourth expression for the real-time state of charge according to the output current, the battery capacity, and the initial state of charge among the independent variables;

[0154] The first expression is constructed according to the fourth expression and the relationship between the real-time state of charge and the open circuit voltage.

[0155] For example, the fourth expression is Where SOC(t) represents the real-time state of charge at time t, SOC(0) represents the initial state of charge, I(x) represents the output current at time x, and Q0 represents the battery capacity. It should be noted that battery charging current is generally defined as positive, and discharging current as negative. The output current I(x) in the expressions shown in the embodiments of this application is the charging current. If discharging current is used, the sign of the output current in the expression should be modified accordingly.

[0156] The first expression is Among them, U OCV (t) represents the open circuit voltage at time t.

[0157] Independent variables such as battery capacity and initial state of charge are closely related to the battery's state of health. In the embodiment of the present application, an expression is constructed between the above-mentioned independent variables and the branch voltage of the battery module. In the subsequent parameter identification process, the independent variables related to the battery's state of health can be parameter identified, thereby facilitating the calculation of the battery's state of health based on the identified parameters (independent variables). In this way, after obtaining the parameter identification results, there is no need to convert the identified parameters into parameters related to the battery's state of health. Instead, the battery's state of health can be directly calculated using the identified parameters, thereby simplifying the steps of the estimation method.

[0158] 2. Construct a second expression for the internal resistance voltage of the first internal resistor based on the independent variable, where the internal resistance voltage represents the potential difference between two connection terminals of the first internal resistor.

[0159] The internal resistance voltage of the first internal resistance is shown in FIG4 as U R0 .

[0160] Optionally, the independent variable includes the first resistor. The second expression in step 2 is constructed in the following manner:

[0161] The second expression is constructed based on the output current, the first resistance, and the fourth expression among the independent variables.

[0162] For example, the second expression is Among them, U R0 (t) represents the internal resistance voltage of the first internal resistor at time t.

[0163] The independent variables such as the first resistance, battery capacity and initial state of charge are unobservable parameters in the battery module, and the independent variables such as battery capacity and initial state of charge are closely related to the health state of the battery. In an embodiment of the present application, constructing an expression between the above-mentioned independent variables and the branch voltage of the battery module is equivalent to establishing a relationship between the unobservable parameters in the battery module and the observable parameters (output voltage) of the battery module. In the subsequent identification process, the data of the unobservable parameters and the parameters related to the health state of the battery can be identified through the data of the observable parameters. In the above manner, a model basis is provided for subsequent parameter identification, which facilitates obtaining a more accurate state of the battery module, thereby providing an estimation accuracy of the health state of the battery.

[0164] 3. Construct a third expression for the parallel voltage of the parallel circuit based on the independent variable, wherein the parallel voltage represents the potential difference between two connection terminals of the parallel circuit.

[0165] The parallel voltage of the parallel circuit is shown as U1 in Figure 4.

[0166] Optionally, the independent variables include the output current of the battery module, the first capacitor, and the second resistor. The third expression in step 3 is constructed as follows:

[0167] The third expression is constructed based on the output current, the first capacitance, and the second resistance among the independent variables.

[0168] For example, the third expression is Among them, U1(t) represents the parallel voltage at time t, C p Represents the first capacitor, R p represents the second resistor.

[0169] The first capacitor and the first resistor are unobservable parameters in the battery module. In the embodiment of the present application, constructing an expression between the above-mentioned independent variables and the branch voltage of the battery module is equivalent to establishing a relationship between the unobservable parameters in the battery module and the observable parameters (output voltage) of the battery module. In the subsequent identification process, the data of the unobservable parameters can be identified by the data of the observable parameters. In the above manner, a model basis is provided for subsequent parameter identification, which facilitates obtaining a more accurate state of the battery module, thereby providing an estimation accuracy of the battery's health status.

[0170] 4. Construct the mathematical model based on the first expression, the second expression and the third expression.

[0171] According to Kirchhoff's voltage law, the output voltage of the battery module is the sum of the branch voltages, that is, the sum of the open circuit voltage of the battery, the internal resistance voltage of the first internal resistor, and the parallel voltage of the parallel circuit. For example, the mathematical model can be obtained as follows:

[0172] Wherein, U(t) represents the output voltage of the battery module at time t.

[0173] In the embodiments of the present application, the first, second, and third expressions are all expressions representing voltages, which is equivalent to establishing a mathematical model based on the relationship between the voltages of each branch in the battery module. In this way, the established mathematical model can reflect the state of the output voltage of the battery module. Since the output voltage of the battery module is observable, this method provides a model foundation for subsequent parameter identification.

[0174] It is understandable that if the dependent variable is the output current of the battery module, the principle of constructing the mathematical model is similar to that of the above embodiment, and reference may be made to the description in the above embodiment, which will not be repeated here.

[0175] In the above embodiment, it is equivalent to constructing a state equation (mathematical model) of the output voltage of the battery module based on Kirchhoff's voltage law. In practical applications, a mathematical model can also be constructed based on Kirchhoff's current law. It is understandable that in practical applications, there can be other ways of expression. For example, in some simplified models, the internal resistance voltage of the first resistor is ignored. For another example, in some models, the open circuit voltage of the battery does not take into account the battery capacity of the battery itself. In other words, if the selected independent variables are different, and / or the selected equivalent circuit models are different, the generated mathematical models are all different. The above mathematical model is only an example, and the form of the mathematical model is not specifically limited in the embodiments of the present application.

[0176] The mathematical model obtained in the above embodiment is continuous, but in actual applications, the sampled data of the observable parameters of the battery module are usually discrete. If a continuous mathematical model is used to identify the discrete state of the battery module, the accuracy of the identification result may be affected.

[0177] To solve this problem, in some embodiments, see FIG5 , which is a flow chart of a parameter identification method provided in an embodiment of the present application. As an example and not a limitation, as shown in FIG5 , one implementation of step S102 may include:

[0178] S501 , discretizing the mathematical model according to the historical operating condition data to obtain the discretized mathematical model.

[0179] In one implementation, a sampling interval can be preset and the mathematical model discretized based on the preset sampling interval. However, in this approach, the preset sampling interval may differ from the actual sampling interval of the battery module, resulting in the discretized mathematical model being unable to reflect the actual operating status of the battery module.

[0180] To solve this problem, in one implementation, S501 may include:

[0181] Obtaining the sampling frequency of the historical operating condition data;

[0182] The mathematical model is discretized according to the sampling frequency of the historical operating condition data to obtain the discretized mathematical model.

[0183] For example, taking the mathematical model in the above embodiment as an example, the discretized mathematical model is:

[0184] Where Δt represents the sampling interval, which is determined by the sampling frequency, U k Indicates the output voltage sampled at the kth sampling moment, I k Indicates the output current sampled at the kth sampling moment, SOC 0 represents the initial charge state.

[0185] In this embodiment, the mathematical model is discretized based on the sampling frequency of the battery module to ensure that the time intervals in the mathematical model are consistent with the actual sampling frequency. This allows the mathematical model to more accurately reflect the actual state of the battery module, thereby improving the accuracy of the battery health state estimation.

[0186] S502 , performing parameter identification on battery parameters of the battery module according to the historical operating condition data and the discretized mathematical model to obtain an identification result.

[0187] In practical applications, the sampled data of the observable parameters of the battery module are usually discrete. In the embodiment of FIG5 , the mathematical model is discretized so that the mathematical model can more accurately reflect the actual state of the battery module, which helps to improve the estimation accuracy of the health status of the battery.

[0188] Specifically, S502 includes:

[0189] I. Establish an identification model based on the discretized mathematical model.

[0190] II. Perform parameter identification based on the historical operating condition data and the identification model to obtain the identification result.

[0191] In the embodiment of the present application, converting the mathematical model of the battery module into an identification model is equivalent to converting a practical problem into a mathematical problem, which facilitates subsequent calculations.

[0192] In one implementation of step I, a least squares model can be established based on the discretized mathematical model to evaluate the error between the calculated output voltage and the actual observed output voltage. Exemplarily, the identification model is:

[0193] Where, represents the total number of sampling moments, represents the actual value of the output voltage of the battery module collected at the kth sampling moment, and x is the parameter to be identified. For example, x = [R0, R1, C1, SOC 0 ,Q0].

[0194] As can be seen from the above mathematical model example, the expression for the output voltage of the battery module is highly nonlinear and non-convex, which may result in multiple stationary points in the identification result. In addition, in practical applications, the battery parameters of the battery module cannot take on unlimited values. To ensure that the identification model is more consistent with the actual operating conditions of the battery module and to quickly search for the optimal solution, in some embodiments, step I may include:

[0195] I-1 constructs an objective function based on the discretized mathematical model.

[0196] I-2 obtains the constraint conditions of the independent variables in the mathematical model.

[0197] I-3 establishes the identification model according to the objective function and the constraint conditions.

[0198] In this embodiment, adding constraints to the independent variables of the mathematical model is equivalent to limiting the range of values ​​of the battery parameters in the battery module, that is, identifying the battery parameters within a certain range of values. In this way, the optimal solution can be quickly identified, and the obtained optimal solution is more consistent with the actual situation, thereby improving the accuracy of battery health estimation.

[0199] Optionally, the independent variables include a first resistor, a second resistor, and a first capacitor. For these independent variables, their constraints can be limited to be greater than 0.

[0200] Optionally, the independent variable includes a real-time state of charge. The real-time state of charge may be constrained to fall within the interval [0, 1].

[0201] Optionally, the independent variable includes the open circuit voltage of the battery. For the open circuit voltage, its constraint condition can be limited to being less than or equal to the output voltage of the battery module. Optionally, the independent variable includes the initial state of charge of the battery module. Accordingly, step I-2 includes:

[0202] Acquiring historical data of the initial state of charge from the historical operating condition data;

[0203] The constraint condition of the initial state of charge is determined according to the first frame data in the historical data of the initial state of charge.

[0204] In practical applications, a battery management system (BMS) can report the battery state of charge. The historical data of the initial state of charge refers to the battery state of charge reported historically by the BMS.

[0205] In one implementation, the maximum and minimum values ​​of the historical data of the initial state of charge can be determined as the upper and lower limits of the constraint conditions, respectively. There may be a certain error between the data reported by the BMS and the actual data, and the numerical range between the maximum and minimum values ​​in the historical data is large, which is not conducive to subsequent identification calculations. In another implementation, a numerical range can be determined based on the first frame of historical data of the initial state of charge. For example, the first frame of historical data plus a preset value is used as the upper limit of the constraint condition, and the first frame of historical data minus a preset value is used as the lower limit of the constraint condition, and the numerical range between the upper and lower limits is determined as the constraint condition.

[0206] The first frame of historical data refers to the first frame of data collected in the historical data. For example, if the historical data includes K frames of data (data obtained by K sampling times), where the first frame collected is the first frame of data and the last frame collected is the Kth frame of data, then the first frame of historical data is the first frame of data among the K frames of data.

[0207] Optionally, the independent variable includes the battery capacity of the battery module. Accordingly, step I-2 includes:

[0208] Acquiring historical charging data of the battery module from the historical operating condition data;

[0209] estimating the battery capacity of the battery module according to the historical charging data to obtain an estimated value of the battery capacity;

[0210] The constraint condition of the battery capacity is determined according to the estimated value of the battery capacity.

[0211] Historical charging data includes the percentage and charge capacity of each charge. Based on the percentage and charge capacity of any charge, the battery module's capacity can be estimated. For example, if a battery is charged 40% during a charge, and the corresponding charge capacity is 40mA, the corresponding battery capacity is 40 / 40% = 100mA.

[0212] In one implementation, the estimated battery capacity can be used as the upper limit of the constraint. However, in practice, the estimated battery capacity may have a certain degree of error. In another implementation, the estimated battery capacity can be adjusted up or down by a preset value, with the adjusted estimated value serving as the upper limit of the constraint.

[0213] The constraints on the independent variables listed in the above embodiments are merely examples and are not intended to specifically limit the selection of independent variables and the constraints on the independent variables.

[0214] In the above embodiment, the constraints of the independent variables are set according to the historical data of the independent variables. Since the historical data is the actual operating condition data, the constraints set according to the historical data can more accurately reflect the actual state of the battery module, thereby improving the reliability of the identification results.

[0215] As can be seen from the above embodiment, the constraints include linear constraints (such as the constraints on the first resistor, the second resistor, and the first capacitor) and nonlinear constraints (such as the constraints on the initial state of charge, the battery capacity, the battery open circuit voltage, and the real-time state of charge). Nonlinear constraints have a higher computational complexity.

[0216] In order to reduce the computational complexity of the identification parameter model, in some embodiments, step I-3 accordingly includes:

[0217] Adding a multiplier to the nonlinear constraint in the constraint condition to obtain a nonlinear constraint term;

[0218] Adding the nonlinear constraint term to the objective function to obtain an updated objective function;

[0219] The identification model is established according to the updated objective function and the linear constraints in the constraint conditions.

[0220] In one implementation, nonlinear constraints can be added to the objective function through Lagrange multipliers. For example, the identification model with nonlinear constraints for real-time state of charge is:

[0221] The nonlinear constraints are added to the objective function using Lagrange multipliers, and the updated model is:

[0222] Where λ1 and λ2 are Lagrange multipliers.

[0223] The above identification model is only an example and only shows the nonlinear constraint part. In this implementation, only the nonlinear constraint can be added to the objective function, and the linear constraint is still retained outside the objective function as a constraint condition.

[0224] In the embodiment of the present application, by adding a multiplier to the nonlinear constraint, the nonlinear constraint term is added to the objective function, which is equivalent to converting the problem with nonlinear constraints into a problem without nonlinear constraints, thereby reducing the algorithm complexity of parameter identification.

[0225] In some other implementations, linear constraints can also be added to the objective function, that is, the constrained problem is converted into an unconstrained problem. In the embodiment of the present application, the independent variables of the mathematical model include observed quantities and non-observed quantities. Among them, the observed quantity refers to a parameter whose actual value can be detected by a device or apparatus, and the non-observed quantity refers to a parameter whose actual value cannot be detected by a device or apparatus. The historical operating condition data includes the initial value of the non-observed quantity, the historical observation data of the observed quantity, and the historical observation data of the dependent variable of the mathematical model.

[0226] For example, the non-observable quantity may include the state of charge, the first resistor, the second resistor, the first capacitor, and the battery capacity. The observable quantity may include the output current of the battery module. The initial value of the state of charge is recorded as the initial state of charge.

[0227] As described in the above embodiment, for the initial state of charge, the first data in the historical data reported by the BMS can be used. If there is a sufficiently long static working condition before charging, the voltage value of the data before charging can be used as the open circuit voltage U of the battery. ocv , look up the initial state of charge value in the SOC-OCV table (a table that maps state of charge to open circuit voltage). For the initial value of the battery capacity, an estimated value of the battery capacity can be used. For the first resistor, second resistor, and first capacitor, initial values ​​can be set based on factory parameters or manually.

[0228] In some embodiments, step II may include:

[0229] Inputting the initial value of the non-observed quantity, the historical observation data of the observed quantity, and the historical observation data of the dependent variable into the identification model to obtain a first value of the objective function;

[0230] Adjusting the value of the non-observed quantity according to the first value to obtain a first adjusted value of the non-observed quantity;

[0231] Inputting the first adjustment value, the historical observation data of the observation quantity, and the historical observation data of the dependent variable into the identification model to obtain a second value of the objective function;

[0232] If the second value satisfies a preset condition, determining the current value of the non-observed quantity as the identification result;

[0233] If the second value does not meet the preset condition, the value of the non-observed quantity is continuously adjusted according to the second value until the value of the objective function meets the preset condition.

[0234] The preset condition may be that the value of the objective function is less than a preset value. Of course, other iteration termination conditions may also be set, for example, determining the number of iterations, and stopping the iteration when the number of iterations reaches a preset number.

[0235] The objective function is For example, the value of the objective function can represent the error between the identification result and the actual observation value of the output voltage of the battery module.

[0236] For example, assume that the historical observation data includes sampled data corresponding to K sampling moments. During the first iteration, the initial values ​​of the first resistor, the second resistor, the first capacitor, and the battery capacity, the initial state of charge, the first sampled data in the historical observation data of the output current, and the first sampled data in the historical observation data of the output voltage are input into the identification model to obtain a first value of the objective function. The values ​​of the real-time state of charge, the first resistor, the second resistor, the first capacitor, and the battery capacity are adjusted according to the first values ​​to obtain their respective first adjusted values. The first adjusted values, the second sampled data in the historical data of the output current, and the second sampled data in the historical observation data of the output voltage are input into the identification model to obtain a second value of the objective function. If the second value meets a preset condition, the current value of the non-observed quantity is determined as the identification result. If the second value does not meet the preset condition, the values ​​of the real-time state of charge, the first resistor, the second resistor, the first capacitor, and the battery capacity are adjusted according to the second value to obtain their respective second adjusted values; the second adjusted value, the third sampled data in the historical output current data, and the third sampled data in the historical output voltage observation data are input into the identification model to obtain a third value of the objective function, and so on.

[0237] In the embodiment of the present application, during the parameter identification process, the actual operating condition data of the battery module is used for iterative calculation rather than recursively solving it in advance, so that the identification result is more stable and reliable.

[0238] Optionally, adjusting the value of the non-observed quantity according to the first value to obtain a first adjusted value of the non-observed quantity includes:

[0239] determining a feasible range of the non-observed quantity according to the first value;

[0240] The first adjustment value is determined from the feasible range, wherein a value of the objective function corresponding to the first adjustment value is smaller than a value of the objective function corresponding to any adjustment value within the feasible range.

[0241] The feasible range here is equivalent to a neighborhood of the current value of the unobservable quantity, or the trust region. In the above method, if the first value is large, the feasible range can be appropriately narrowed; if the first value is small, the feasible range can be appropriately expanded.

[0242] One implementation method for determining the first adjustment value from the feasible range is to input each sample value within the feasible range into the objective function, calculate the corresponding objective function value, and determine the sample value corresponding to the minimum calculated objective function value as the first adjustment value. Of course, in other implementation methods, a gradient descent method or an approximation method can also be used to search for the first adjustment value that minimizes the objective function value within the feasible range. This embodiment of the present application does not specifically limit this.

[0243] In this embodiment, the feasible range of the unobservable variable is continuously adjusted based on the value of the objective function, and then the point with the minimum value of the objective function is sought within the feasible range. This is equivalent to converting the point search problem into a local range search problem. This method can quickly converge to the optimal solution, effectively improving the efficiency of parameter identification and thus improving the efficiency of battery health estimation.

[0244] In some embodiments, the identification result includes the battery capacity of the battery module. Accordingly, S103 may include:

[0245] The health status of the batteries in the battery module is predicted according to the value of the battery capacity in the identification result.

[0246] For example, according to the formula Calculate the battery's health status. The nominal capacity of a battery refers to the minimum amount of electricity that should be discharged under certain discharge conditions, as specified or guaranteed during the design and manufacture of the battery.

[0247] In the embodiment of the present application, since the identified battery parameters of the battery module are relatively accurate, the health status of the battery estimated based on the identification result is also more accurate.

[0248] 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 this application.

[0249] Figure 6 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. As shown in Figure 6, the electronic device 6 of this embodiment includes: at least one processor 60 (only one is shown in Figure 6), a memory 61, and a computer program 62 stored in the memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, it implements the steps of any of the above-mentioned battery health status estimation method embodiments.

[0250] The electronic device may be a computing device such as a desktop computer, a laptop, a PDA, or a cloud server. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that FIG6 is merely an example of an electronic device 6 and does not limit the electronic device 6. The electronic device may include more or fewer components than shown, or a combination of certain components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0251] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor.

[0252] In some embodiments, the memory 61 may be an internal storage unit of the electronic device 6, such as a hard disk or memory of the electronic device 6. In other embodiments, the memory 61 may also be an external storage device of the electronic device 6, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 6. Furthermore, the memory 61 may also include both an internal storage unit of the electronic device 6 and an external storage device. The memory 61 is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program. The memory 61 may also be used to temporarily store data that has been output or is about to be output.

[0253] An embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the battery health status estimation method described in each of the above embodiments is implemented.

[0254] An embodiment of the present application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the battery health status estimation method in the above-mentioned embodiments is implemented.

[0255] This embodiment also provides a computer program product, wherein the computer-readable storage medium stores program code. When the computer program product is run on a computer, the computer is caused to execute the above-mentioned related steps to implement the battery health status estimation method in the above-mentioned embodiment.

[0256] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electric carrier signal, a telecommunication signal and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0257] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0258] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

Claims

1. A method for estimating a battery health state, characterized in that: include: Obtain historical operating condition data of the battery module to be tested; Performing parameter identification on the battery parameters of the battery module according to the historical operating condition data to obtain an identification result; The health status of the batteries in the battery module is predicted according to the identification result.

2. The method according to claim 1, characterized in that The step of performing parameter identification on the battery parameters of the battery module according to the historical operating condition data to obtain an identification result includes: Acquire a mathematical model of the battery module, wherein the mathematical model is constructed according to battery parameters of the battery module; Parameter identification is performed on battery parameters of the battery module according to the historical operating condition data and the mathematical model to obtain an identification result.

3. The method according to claim 2, characterized in that The step of performing parameter identification on the battery parameters of the battery module according to the historical operating condition data and the mathematical model to obtain an identification result includes: Discretize the mathematical model according to the historical operating condition data to obtain the discretized mathematical model; Parameter identification is performed on battery parameters of the battery module according to the historical operating condition data and the discretized mathematical model to obtain an identification result.

4. The method according to claim 3, characterized in that The step of discretizing the mathematical model according to the historical operating condition data to obtain the discretized mathematical model includes: Obtaining the sampling frequency of the historical operating condition data; The mathematical model is discretized according to the sampling frequency of the historical operating condition data to obtain the discretized mathematical model.

5. The method according to claim 4, characterized in that The step of performing parameter identification on the battery parameters of the battery module according to the historical operating condition data and the discretized mathematical model to obtain an identification result includes: Establishing an identification model according to the discretized mathematical model; Parameter identification is performed according to the historical operating condition data and the identification model to obtain the identification result.

6. The method according to claim 5, characterized in that The step of establishing an identification model according to the discretized mathematical model comprises: Constructing an objective function according to the discretized mathematical model; Obtaining constraints on independent variables in the mathematical model; The identification model is established according to the objective function and the constraint conditions.

7. The method according to claim 6, characterized in that The independent variables include the initial state of charge of the battery module; The obtaining of the constraint conditions of the independent variables in the mathematical model includes: Acquiring historical data of the initial state of charge from the historical operating condition data; The constraint condition of the initial state of charge is determined according to the first frame data in the historical data of the initial state of charge.

8. The method according to claim 6, characterized in that The independent variable includes the battery capacity of the battery module; The obtaining of the constraint conditions of the independent variables in the mathematical model includes: Acquiring historical charging data of the battery module from the historical operating condition data; estimating the battery capacity of the battery module according to the historical charging data to obtain an estimated value of the battery capacity; The constraint condition of the battery capacity is determined according to the estimated value of the battery capacity.

9. The method according to any one of claims 6 to 8, characterized in that The constraint conditions include linear constraints and nonlinear constraints; The step of establishing the identification model according to the objective function and the constraint conditions comprises: Adding a multiplier to the nonlinear constraint in the constraint condition to obtain a nonlinear constraint term; Adding the nonlinear constraint term to the objective function to obtain an updated objective function; The identification model is established according to the updated objective function and the linear constraints in the constraint conditions.

10. The method according to any one of claims 6 to 9, characterized in that: The independent variables of the mathematical model include observed quantities and non-observed quantities; the historical operating condition data include the initial values ​​of the non-observed quantities, the historical observed data of the observed quantities, and the historical observed data of the dependent variables of the mathematical model; The performing parameter identification according to the historical operating condition data and the identification model to obtain the identification result includes: Inputting the initial value of the non-observable quantity, the historical observation data of the observed quantity, and the historical observation data of the dependent variable into the identification model to obtain a first value of the objective function; Adjusting the value of the non-observable quantity according to the first value to obtain a first adjusted value of the non-observable quantity; Inputting the first adjustment value, the historical observation data of the observation quantity, and the historical observation data of the dependent variable into the identification model to obtain a second value of the objective function; If the second value does not meet the preset condition, continue to adjust the value of the non-observation quantity according to the second value until the value of the objective function meets the preset condition; If the second value satisfies a preset condition, the current value of the non-observed quantity is determined as the identification result.

11. The method according to claim 10, characterized in that The step of adjusting the value of the non-observable quantity according to the first value to obtain a first adjusted value of the non-observable quantity includes: Determining a feasible range of the non-observable quantity according to the first value; The first adjustment value is determined from the feasible range, wherein a value of the objective function corresponding to the first adjustment value is smaller than a value of the objective function corresponding to any adjustment value within the feasible range.

12. The method according to any one of claims 2 to 11, characterized in that The method further comprises: The mathematical model of the battery module is optimized according to a preset period.

13. The method according to claim 12, characterized in that The steps of updating the mathematical model of the battery module each time include: Determining dependent variables of the mathematical model according to the battery parameters; Determining an independent variable related to the dependent variable based on the battery parameter; The mathematical model is constructed according to the independent variables and the dependent variables.

14. The method according to claim 13, characterized in that The step of constructing the mathematical model according to the independent variable and the dependent variable comprises: Obtaining an equivalent circuit model of the battery module; The mathematical model between the independent variable and the dependent variable is constructed according to the equivalent circuit model.

15. The method according to claim 14, characterized in that The equivalent circuit model includes a first resistor, a second resistor, a first capacitor and a battery, wherein the second resistor and the first capacitor are connected in parallel to obtain a parallel circuit, and the battery, the first internal resistor and the parallel circuit are connected in series; In the case where the dependent variable is the output voltage of the battery module, constructing the mathematical model between the independent variable and the dependent variable according to the equivalent circuit model includes: constructing a first expression for an open circuit voltage of the battery based on the independent variable, the open circuit voltage representing the potential difference between a positive electrode and a negative electrode of the battery; constructing a second expression of an internal resistance voltage of the first internal resistance according to the independent variable, wherein the internal resistance voltage represents a potential difference between two connection ends of the first internal resistance; constructing a third expression for a parallel voltage of the parallel circuit according to the independent variable, the parallel voltage representing a potential difference between two connection terminals of the parallel circuit; The mathematical model is constructed according to the first expression, the second expression and the third expression.

16. The method according to claim 15, characterized in that The independent variables include the output current, battery capacity and initial state of charge of the battery module; The first expression for the open circuit voltage of the battery is constructed according to the independent variable, comprising: Constructing a fourth expression of the real-time state of charge according to the output current, the battery capacity and the initial state of charge among the independent variables; The first expression is constructed according to the fourth expression and the relationship between the real-time state of charge and the open circuit voltage.

17. The method according to claim 16, characterized in that The independent variable includes the first resistance; the second expression of the internal resistance voltage of the first internal resistance constructed according to the independent variable includes: The second expression is constructed based on the output current, the first resistance and the fourth expression among the independent variables.

18. The method according to claim 15, characterized in that The independent variables include the output current of the battery module, the first capacitor and the second resistor; The third expression for the parallel voltage of the parallel circuit is constructed according to the independent variable, comprising: The third expression is constructed according to the output current, the first capacitance and the second resistance among the independent variables.

19. The method according to any one of claims 1 to 18, characterized in that The identification result includes the battery capacity of the battery module; The predicting the health status of the battery in the battery module according to the identification result includes: The health status of the battery in the battery module is predicted according to the value of the battery capacity in the identification result.

20. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the battery health state estimation method according to any one of claims 1 to 19 is implemented.

21. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the battery health status estimation method according to any one of claims 1 to 19.

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