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

Abstract

The embodiment of the invention provides a battery health state estimation method, electronic equipment and a computer readable storage medium. The battery health state estimation method comprises the following steps: acquiring historical working condition data of a to-be-tested battery module; performing parameter identification on battery parameters of the battery module according to the historical working condition data to obtain an identification result; and predicting the health state of the battery in the battery module according to the identification result. Through the method, the estimation precision of the battery health state can be effectively improved.

Description

Technical Field

[0001] 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

[0002] With the development of electric vehicles, the service life of batteries has received more and more attention. The battery state of health (SOH), also known as the battery attenuation coefficient, is used to reflect the aging and health of the battery. The larger the SOH value, the lower the battery aging and the higher the health; the smaller the SOH value, the higher the battery aging and the lower the health. However, the relevant SOH value estimation methods usually have low estimation accuracy, which affects the user's understanding of the battery status. Summary of the invention

[0003] The present application provides a battery health state estimation method, an electronic device and a computer-readable storage medium, which solve the problem of low estimation accuracy of the battery health state.

[0004] In order to achieve the above objectives, this application adopts the following technical solutions:

[0005] In a first aspect, a method for estimating a battery health state is provided, comprising:

[0006] Obtain historical operating condition data of the battery module to be tested;

[0007] Performing parameter identification on the battery parameters of the battery module according to the historical operating condition data to obtain an identification result;

[0008] The health status of the batteries in the battery module is predicted according to the identification result.

[0009] In the embodiment of the present application, the battery parameters are identified based on the historical operating data of the battery module, which is equivalent to using an offline identification method. Compared with the real-time identification method, the offline identification method can avoid the situation where the identification result is not stable enough due to recursive solution. In addition, since the parameter identification is performed based on the historical operating data of the battery module, the battery parameters of the battery module can be fitted more accurately, thereby effectively improving the estimation accuracy of the battery health status.

[0010] In an implementation of the first aspect, the performing parameter identification on the battery parameters of the battery module according to the historical operating condition data to obtain an identification result includes:

[0011] Acquire a mathematical model of the battery module, wherein the mathematical model is constructed according to battery parameters of the battery module;

[0012] 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.

[0013] In the embodiment of the present application, parameter identification is performed in combination with a mathematical model to identify the parameters of the battery module, and then the health status of the battery is predicted based on the identified parameters of the battery module. Since the mathematical model is constructed based on the battery parameters of the battery module, the mathematical model can more accurately reflect the state of the battery module and provide a reliable model basis for subsequent parameter identification; in addition, through the parameter identification method, the battery parameters of the battery module can be more accurately fitted. Combining the mathematical model and the parameter identification method can effectively improve the estimation accuracy of the battery health status.

[0014] In an implementation of the first aspect, the 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:

[0015] Discretize the mathematical model according to the historical operating condition data to obtain the discretized mathematical model;

[0016] 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.

[0017] 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 state of the battery.

[0018] In an implementation of the first aspect, the discretizing the mathematical model according to the historical operating condition data to obtain the discretized mathematical model includes:

[0019] Obtaining the sampling frequency of the historical operating condition data;

[0020] The mathematical model is discretized according to the sampling frequency of the historical operating condition data to obtain the discretized mathematical model.

[0021] In the embodiment of the present application, the mathematical model is discretized according to the sampling frequency of the battery module so that the time interval in the mathematical model is consistent with the actual sampling frequency. In this way, the mathematical model can more accurately reflect the actual state of the battery module, which is conducive to improving the estimation accuracy of the battery health state.

[0022] In an implementation of the first aspect, the 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:

[0023] Establishing an identification model according to the discretized mathematical model;

[0024] Parameter identification is performed according to the historical operating condition data and the identification model to obtain the identification result.

[0025] 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.

[0026] In an implementation of the first aspect, establishing an identification model according to the discretized mathematical model includes:

[0027] Constructing an objective function according to the discretized mathematical model;

[0028] Obtaining constraints on independent variables in the mathematical model;

[0029] The identification model is established according to the objective function and the constraint conditions.

[0030] In practical applications, the battery parameters of the battery module cannot take unlimited values. In the embodiment of the present application, adding constraints to the independent variables of the mathematical model is equivalent to limiting the value range of the battery parameters in the battery module, that is, identifying the battery parameters within a certain value range. In this way, the optimal solution can be quickly identified, and the optimal solution obtained is more in line with the actual situation, which is conducive to improving the estimation accuracy of the battery health status.

[0031] In an implementation of the first aspect, the independent variable includes an initial state of charge of the battery module;

[0032] The obtaining of the constraint conditions of the independent variables in the mathematical model includes:

[0033] Acquiring historical data of the initial state of charge from the historical operating condition data;

[0034] 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.

[0035] In an implementation of the first aspect, the independent variable includes a battery capacity of the battery module;

[0036] The obtaining of the constraint conditions of the independent variables in the mathematical model includes:

[0037] Acquiring historical charging data of the battery module from the historical operating condition data;

[0038] estimating the battery capacity of the battery module according to the historical charging data to obtain an estimated value of the battery capacity;

[0039] The constraint condition of the battery capacity is determined according to the estimated value of the battery capacity.

[0040] 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 result.

[0041] In an implementation of the first aspect, the constraint condition includes a linear constraint and a nonlinear constraint;

[0042] The step of establishing the identification model according to the objective function and the constraint conditions comprises:

[0043] Adding a multiplier to the nonlinear constraint in the constraint condition to obtain a nonlinear constraint term;

[0044] Adding the nonlinear constraint term to the objective function to obtain an updated objective function;

[0045] The identification model is established according to the updated objective function and the linear constraints in the constraint conditions.

[0046] 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 a problem with nonlinear constraints into a problem without nonlinear constraints, thereby reducing the algorithm complexity of parameter identification.

[0047] In an implementation of the first aspect, the independent variables of the mathematical model include observed quantities and non-observed quantities; the historical operating condition data include initial values ​​of the non-observed quantities, historical observed data of the observed quantities, and historical observed data of the dependent variables of the mathematical model;

[0048] The performing parameter identification according to the historical operating condition data and the identification model to obtain the identification result includes:

[0049] 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;

[0050] Adjusting the value of the non-observable quantity according to the first value to obtain a first adjusted value of the non-observable quantity;

[0051] 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;

[0052] 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;

[0053] If the second value satisfies a preset condition, the current value of the non-observed quantity is determined as the identification result.

[0054] 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.

[0055] In an implementation manner of the first aspect, 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:

[0056] Determining a feasible range of the non-observable quantity according to the first value;

[0057] 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.

[0058] In the embodiment of the present application, the feasible range of the non-observable quantity is continuously adjusted according to the value of the objective function, and then the point with the minimum value of the objective function is sought within the feasible range, which is equivalent to converting the point search problem into a local range search problem. In this way, it is possible to quickly converge to the optimal solution, effectively improving the efficiency of parameter identification, thereby improving the efficiency of battery health state estimation.

[0059] In an implementation of the first aspect, the method further includes:

[0060] The mathematical model of the battery module is optimized according to a preset period.

[0061] 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 the situation where poor identification results are caused by inaccurate mathematical models, and helping to improve the reliability of the identification results.

[0062] In an implementation of the first aspect, the step of updating the mathematical model of the battery module each time includes:

[0063] Determining dependent variables of the mathematical model according to the battery parameters;

[0064] Determining an independent variable related to the dependent variable based on the battery parameter;

[0065] The mathematical model is constructed according to the independent variables and the dependent variables.

[0066] 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.

[0067] In an implementation of the first aspect, constructing the mathematical model according to the independent variable and the dependent variable includes:

[0068] Obtaining an equivalent circuit model of the battery module;

[0069] The mathematical model between the independent variable and the dependent variable is constructed according to the equivalent circuit model.

[0070] In the embodiment of the present application, the mathematical model of the battery module is constructed according to the equivalent circuit model of the battery module. Since a relatively complex structure of a part is usually replaced with a relatively simple structure in the equivalent circuit model, and the circuit after the replacement and the original circuit have the same effect on the untransformed part, through the above method, while ensuring that the mathematical model can accurately reflect the state of the battery module, the model can be simplified, thereby simplifying the subsequent calculations.

[0071] 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 obtain a parallel circuit, and the battery, the first internal resistance, and the parallel circuit are connected in series;

[0072] 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:

[0073] 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;

[0074] 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;

[0075] 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;

[0076] The mathematical model is constructed according to the first expression, the second expression and the third expression.

[0077] 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 has a relatively simple structure and a linear state equation. Therefore, the mathematical model constructed based on the equivalent circuit model is relatively simple and convenient for subsequent calculations.

[0078] In addition, in the embodiment of the present application, the first expression, the second expression and the third expression are all expressions representing voltage, 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, the above method provides a model basis for subsequent parameter identification.

[0079] 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;

[0080] The first expression for the open circuit voltage of the battery is constructed according to the independent variable, comprising:

[0081] 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;

[0082] 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.

[0083] Independent variables such as battery capacity and initial charge state are closely related to the health state of the battery. In the embodiment of the present application, an expression between the above independent variables and the branch voltage of the battery module is constructed. In the subsequent parameter identification process, the independent variables related to the health state of the battery can be identified, so as to facilitate the calculation of the health state of the battery 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 health state of the battery, but the health state of the battery can be directly calculated using the identified parameters, thereby simplifying the steps of the estimation method.

[0084] In an implementation of the first aspect, the independent variable includes the first resistance; and constructing a second expression of the internal resistance voltage of the first internal resistance according to the independent variable includes:

[0085] The second expression is constructed based on the output current, the first resistance and the fourth expression among the independent variables.

[0086] The independent variables such as the first resistance, battery capacity and initial charge state are unobservable parameters in the battery module, and the independent variables such as battery capacity and initial charge state are closely related to the health state of the battery. 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 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 the subsequent parameter identification, which facilitates obtaining a more accurate state of the battery module, thereby providing the estimation accuracy of the health state of the battery.

[0087] 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;

[0088] The third expression for the parallel voltage of the parallel circuit is constructed according to the independent variable, comprising:

[0089] The third expression is constructed according to the output current, the first capacitance and the second resistance among the independent variables.

[0090] 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 estimated accuracy of the battery's health state.

[0091] In an implementation of the first aspect, the identification result includes a battery capacity of the battery module;

[0092] The predicting the health status of the battery in the battery module according to the identification result includes:

[0093] The health status of the battery in the battery module is predicted according to the value of the battery capacity in the identification result.

[0094] In the embodiment of the present application, since the battery parameters of the identified battery module are relatively accurate, the health status of the battery estimated based on the identification result is also more accurate.

[0095] 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.

[0096] According to 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.

[0097] 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.

[0098] 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.

[0099] 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

[0100] Figure 1 It is a flowchart of a battery health status estimation method provided in an embodiment of the present application;

[0101] Figure 2 It is a flow chart of the mathematical model construction method provided in the embodiment of the present application;

[0102] Figure 3 is a schematic diagram of a second-order equivalent circuit provided in an embodiment of the present application;

[0103] Figure 4 is a schematic diagram of an equivalent circuit provided in an embodiment of the present application;

[0104] Figure 5 It is a flow chart of the parameter identification method provided in the embodiment of the present application;

[0105] Figure 6 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] With the development of electric vehicles, the service life of batteries has received more and more attention. The battery state of health (SOH), also known as the battery attenuation coefficient, is used to reflect the aging and health of the battery. The larger the SOH value, the lower the battery aging and the higher the health; the smaller the SOH value, the higher the battery aging and the lower the health. However, the relevant SOH value estimation methods usually have low estimation accuracy, which affects the user's understanding of the battery status.

[0112] In order to solve the above problems, an embodiment of the present application provides a method for estimating the health status of a battery. In the embodiment of the present application, the battery module to be tested is converted into a mathematical model, and parameter identification is performed in combination with the mathematical model to identify the parameters of the battery module, and then the health status of the battery is predicted based on the identified parameters of the battery module. Through this method, the estimation accuracy of the health status of the battery can be effectively improved.

[0113] The following is a specific example of a method for estimating the battery health status provided in the embodiment of the present application.

[0114] See also Figure 1 , is a flowchart of a battery health status estimation method provided in an embodiment of the present application. As an example and not a limitation, Figure 1 As shown, the battery health status estimation method may include the following steps: S101, obtaining historical operating condition data of the battery module to be tested.

[0115] 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.

[0116] S102, performing parameter identification on battery parameters of the battery module according to the historical operating condition data to obtain an identification result.

[0117] In the embodiment of the present application, the battery module includes a battery (or battery cell) and a circuit element for adjusting 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 elements in the battery module.

[0118] Parameter identification technology is a technology 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 (regarded as a curve fitting problem), thereby predicting 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 test 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, it is considered that this model has a high degree of credibility.

[0119] In the 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). The data of the unobservable quantities in the battery module can be fitted through parameter identification to obtain the working state of the battery module, and then predict the health state of the batteries in the battery module.

[0120] S103: predicting the health status of the batteries in the battery module according to the identification result.

[0121] Figure 1In the embodiment, the battery parameters are identified based on the historical operating data of the battery module, which is equivalent to using an offline identification method. Compared with the real-time identification method, the offline identification method can avoid the situation where the identification result is not stable enough due to recursive solution. In addition, since the parameter identification is performed based on the historical operating data of the battery module, the battery parameters of the battery module can be fitted more accurately, thereby effectively improving the estimation accuracy of the battery health status.

[0122] In some embodiments, S102 may include:

[0123] Acquire a mathematical model of the battery module, wherein the mathematical model is constructed according to battery parameters of the battery module;

[0124] 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.

[0125] The mathematical model constructed in the embodiment of the present application can reflect the circuit operation state of the battery module. The construction process of the mathematical model is described in the following mathematical model optimization embodiment, which will not be repeated here.

[0126] In the embodiment of the present application, parameter identification is performed in combination with a mathematical model to identify the parameters of the battery module, and then the health status of the battery is predicted based on the identified parameters of the battery module. Since the mathematical model is constructed based on the battery parameters of the battery module, the mathematical model can more accurately reflect the state of the battery module and provide a reliable model basis for subsequent parameter identification; in addition, through the parameter identification method, the battery parameters of the battery module can be more accurately fitted. Combining the mathematical model and the parameter identification method can effectively improve the estimation accuracy of the battery health status.

[0127] In some embodiments, the method further comprises:

[0128] The mathematical model of the battery module is optimized according to a preset period.

[0129] 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 the situation where poor identification results are caused by inaccurate mathematical models, and helping to improve the reliability of the identification results.

[0130] In some embodiments, see Figure 2 , is a flow chart of the mathematical model construction method provided in the embodiment of the present application. As an example and not a limitation, Figure 2 As shown, the steps of optimizing the mathematical model of the battery module each time may include:

[0131] S201, determining dependent variables of the mathematical model according to the battery parameters.

[0132] S202: Determine an independent variable related to the dependent variable according to the battery parameter.

[0133] S203: construct the mathematical model according to the independent variables and the dependent variables.

[0134] 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.

[0135] 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.

[0136] In practical applications, the calculations involved in output voltage are simpler than those of output current. Therefore, the method in which the dependent variable is output voltage can be adopted.

[0137] 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.

[0138] In some embodiments, an implementation of S201 may include:

[0139] 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.

[0140] Among them, the equivalent circuit refers to a circuit in which a relatively complex structure is replaced by a relatively simple structure, and the circuit after the replacement maintains the same effect as the original circuit on the unchanged part (or external circuit).

[0141] Figure 2 In the embodiment, the mathematical model of the battery module is constructed according to the equivalent circuit model of the battery module. Since a relatively complex structure of a part is usually replaced with a relatively simple structure in the equivalent circuit model, and the circuit after the replacement and the original circuit maintain the same effect on the unchanged part, through the above method, while ensuring that the mathematical model can accurately reflect the state of the battery module, the model can be simplified, thereby simplifying the subsequent calculations.

[0142] In some implementations, the equivalent circuit model may use a second-order RC equivalent circuit. Figure 3, 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, such as Figure 3 The second-order RC equivalent circuit shown in , includes 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.

[0143] 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 obtain a parallel circuit, and the battery, the first internal resistance and the parallel circuit are connected in series.

[0144] See also Figure 4 , is a schematic diagram of an equivalent circuit provided in an embodiment of the present application. As an example and not a limitation, Figure 4 As shown, 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 electrode materials, electrolytes, diaphragm internal resistance and contact resistance of various parts. The second resistor Rp is equivalent to the polarization internal resistance of the battery module, that is, the internal resistance caused by polarization during electrochemical reactions, usually including resistance caused by electrochemical polarization and concentration polarization. The first capacitor Cp is equivalent to the polarization capacitor of the battery module, that is, a capacitor with positive and negative poles. The polarization capacitor and the polarization internal resistance are connected in parallel to form a capacitive resistance loop, which is used to simulate the dynamic characteristics exhibited during the generation and elimination of battery polarization.

[0145] 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 has a relatively simple structure and a linear state equation. Therefore, the mathematical model constructed based on the equivalent circuit model is relatively simple and convenient for subsequent calculations.

[0146] 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.

[0147] The step of constructing the mathematical model between the independent variable and the dependent variable according to the equivalent circuit model comprises:

[0148] 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.

[0149] The open circuit voltage of the battery is Figure 4 The U shown ocv .

[0150] 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 by:

[0151] 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;

[0152] 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.

[0153] Exemplarily, the fourth expression is Among them, SOC(t) represents the real-time state of charge at the tth moment, SOC(0) represents the initial state of charge, I(x) represents the output current at the xth moment, and Q0 represents the battery capacity. It should be noted that the battery charging current is usually defined as positive and the discharge current is negative. The output current I(x) in the expression shown in the embodiment of the present application is the charging current. If the discharge current is used, the sign of the output current in the expression is modified accordingly.

[0154] The first expression is Among them, U OCV (t) represents the open circuit voltage at time t.

[0155] Independent variables such as battery capacity and initial charge state are closely related to the health state of the battery. In the embodiment of the present application, an expression between the above independent variables and the branch voltage of the battery module is constructed. In the subsequent parameter identification process, the independent variables related to the health state of the battery can be identified, so as to facilitate the calculation of the health state of the battery 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 health state of the battery, but the health state of the battery can be directly calculated using the identified parameters, thereby simplifying the steps of the estimation method.

[0156] 2. Construct a second expression of the internal resistance voltage of the first internal resistor according to the independent variable, wherein the internal resistance voltage represents the potential difference between two connection ends of the first internal resistor.

[0157] The internal resistance voltage of the first internal resistance is Figure 4 The U shown in R0 .

[0158] Optionally, the independent variable includes the first resistor. The second expression in step 2 is constructed in the following manner:

[0159] The second expression is constructed based on the output current, the first resistance and the fourth expression among the independent variables.

[0160] Exemplarily, the second expression is Among them, U R0 (t) represents the internal resistance voltage of the first internal resistance at the tth moment.

[0161] The independent variables such as the first resistance, battery capacity and initial charge state are unobservable parameters in the battery module, and the independent variables such as battery capacity and initial charge state are closely related to the health state of the battery. 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 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 the subsequent parameter identification, which facilitates obtaining a more accurate state of the battery module, thereby providing the estimation accuracy of the health state of the battery.

[0162] 3. Construct a third expression of the parallel voltage of the parallel circuit according to the independent variable, wherein the parallel voltage represents the potential difference between two connection terminals of the parallel circuit.

[0163] The parallel voltage of a parallel circuit is Figure 4 U1 shown in .

[0164] Optionally, the independent variables include the output current of the battery module, the first capacitor and the second resistor.

[0165] The third expression in step 3 is constructed by:

[0166] The third expression is constructed according to the output current, the first capacitance and the second resistance among the independent variables.

[0167] Exemplarily, the third expression is Among them, U1(t) represents the parallel voltage at the tth moment, C p Represents the first capacitor, R p represents the second resistor.

[0168] 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 estimated accuracy of the battery's health state.

[0169] 4. Construct the mathematical model according to the first expression, the second expression and the third expression.

[0170] According to Kirchhoff's voltage law, the output voltage of the battery module is the sum of the voltages of each branch, 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:

[0171]

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

[0173] In the embodiment of the present application, the first expression, the second expression and the third expression are all expressions representing voltage, 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, the above method provides a model basis 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 application, 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 Figure 5 , is a flow chart of the parameter identification method provided in the embodiment of the present application. As an example and not a limitation, Figure 5 As shown, 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 manually, and the mathematical model is discretized according to the preset sampling interval. However, in this way, the preset sampling interval may be different from the actual sampling interval of the battery module, resulting in the discretized mathematical model being unable to reflect the actual working state 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]

[0185] Where Δt represents the sampling interval, which is determined by the sampling frequency, U k represents 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.

[0186] In the embodiment of the present application, the mathematical model is discretized according to the sampling frequency of the battery module so that the time interval in the mathematical model is consistent with the actual sampling frequency. In this way, the mathematical model can more accurately reflect the actual state of the battery module, which is conducive to improving the estimation accuracy of the battery health state.

[0187] 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.

[0188] In practical applications, the sampling data of observable parameters of battery modules are usually discrete. Figure 5 In the embodiment, the mathematical model is discretized so that the mathematical model can more accurately reflect the actual state of the battery module, which is beneficial to improving the estimation accuracy of the health state of the battery.

[0189] Specifically, S502 includes:

[0190] I. Establish an identification model based on the discretized mathematical model.

[0191] II. Perform parameter identification based on the historical operating condition data and the identification model to obtain the identification result.

[0192] 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.

[0193] 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:

[0194]

[0195] Wherein, 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].

[0196] It can be seen from the above mathematical model example that the expression of the output voltage of the battery module is highly nonlinear and non-convex, which will lead to the possibility of multiple stationary points in the identification result. In addition, in practical applications, the battery parameters of the battery module cannot have unlimited values. In order to ensure that the identification model is more in line with the actual working state of the battery module and quickly search for the optimal solution, in some embodiments, step I may include:

[0197] I-1 constructs the objective function according to the discretized mathematical model.

[0198] I-2 obtains the constraint conditions of the independent variables in the mathematical model.

[0199] I-3 establishes the identification model according to the objective function and the constraint conditions.

[0200] In the embodiment of the present application, adding constraints to the independent variables of the mathematical model is equivalent to limiting the value range of the battery parameters in the battery module, that is, identifying the battery parameters within a certain value range. In this way, the optimal solution can be quickly identified, and the obtained optimal solution is more in line with the actual situation, thereby helping to improve the estimation accuracy of the battery health status.

[0201] 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.

[0202] Optionally, the independent variable includes the real-time state of charge. For the real-time state of charge, its constraint condition can be limited to belong to the interval [0,1].

[0203] Optionally, the independent variable includes the open circuit voltage of the battery. For the open circuit voltage, its constraint condition can be limited to be 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:

[0204] Acquiring historical data of the initial state of charge from the historical operating condition data;

[0205] 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.

[0206] In practical applications, a battery management system (BMS) can report the state of charge of the battery. The historical data of the initial state of charge refers to the state of charge of the battery reported historically by the BMS.

[0207] 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.

[0208] The first frame of data in the 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 samplings), the first frame of data collected is the first frame of data, and the last frame of data collected is the Kth frame of data, then the first frame of data in the historical data is the first frame of data in the K frames of data.

[0209] Optionally, the independent variable includes the battery capacity of the battery module. Accordingly, step I-2 includes:

[0210] Acquiring historical charging data of the battery module from the historical operating condition data;

[0211] estimating the battery capacity of the battery module according to the historical charging data to obtain an estimated value of the battery capacity;

[0212] The constraint condition of the battery capacity is determined according to the estimated value of the battery capacity.

[0213] The historical charging data includes the percentage and charge amount of each charge. Based on the percentage and charge amount of any charge, the battery capacity of the battery module can be estimated. For example, in a certain charging behavior, the battery is charged 40%, and the corresponding charge amount is 40mA, then the corresponding battery capacity is 40 / 40% = 100mA.

[0214] In one implementation, the estimated value of the battery capacity can be determined as the upper limit of the constraint condition of the battery capacity. However, in actual applications, the estimated battery capacity may have a certain error. In another implementation, the estimated value of the battery capacity can be floated up or down by a preset value, and the floating estimated value is used as the upper limit of the constraint condition.

[0215] 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.

[0216] 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 result.

[0217] It can be seen from the above embodiments that the constraints include linear constraints (such as the constraints of the first resistor, the second resistor and the first capacitor) and nonlinear constraints (such as the constraints of the initial state of charge, the battery capacity, the open circuit voltage of the battery and the real-time state of charge). For nonlinear constraints, the calculation complexity is relatively high.

[0218] In order to reduce the computational complexity of the identification parameter model, in some embodiments, step I-3 includes:

[0219] Adding a multiplier to the nonlinear constraint in the constraint condition to obtain a nonlinear constraint term;

[0220] Adding the nonlinear constraint term to the objective function to obtain an updated objective function;

[0221] The identification model is established according to the updated objective function and the linear constraints in the constraint conditions.

[0222] In one implementation, nonlinear constraints can be added to the objective function through Lagrange multipliers. For example, the identification model with nonlinear constraints of real-time state of charge is:

[0223]

[0224]

[0225] The nonlinear constraints are added to the objective function using Lagrange multipliers, and the updated model is:

[0226]

[0227] Among them, λ1 and λ2 are Lagrange multipliers.

[0228] The above identification model is only an example and only shows the part of nonlinear constraints. In this implementation, only nonlinear constraints can be added to the objective function, and linear constraints are still retained outside the objective function as constraint conditions.

[0229] 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 a problem with nonlinear constraints into a problem without nonlinear constraints, thereby reducing the algorithm complexity of parameter identification.

[0230] In some other implementations, linear constraints can also be added to the objective function, that is, to convert a constrained problem into an unconstrained problem. In an embodiment of the present application, the independent variables of the mathematical model include observed quantities and non-observed quantities. Among them, observed quantities refer to parameters whose actual values ​​can be detected by equipment or devices, and non-observed quantities refer to parameters whose actual values ​​cannot be detected by equipment or devices. 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.

[0231] Exemplarily, 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.

[0232] 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 previous frame of data can be used as the open circuit voltage U of the battery. ocv, search in the SOC-OCV table (correspondence table of state of charge and open circuit voltage) to obtain the value of the initial state of charge. For the initial value of the battery capacity, the estimated value of the battery capacity can be used. For the first resistor, the second resistor and the first capacitor, an initial value can be set according to the factory parameters or manually.

[0233] In some embodiments, step II may include:

[0234] 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;

[0235] Adjusting the value of the non-observable quantity according to the first value to obtain a first adjusted value of the non-observable quantity;

[0236] 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;

[0237] If the second value satisfies a preset condition, determining the current value of the non-observed quantity as the identification result;

[0238] If the second value does not satisfy the preset condition, the value of the non-observation quantity continues to be adjusted according to the second value until the value of the objective function satisfies the preset condition.

[0239] 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.

[0240] The objective function is For example, the value of the objective function can represent the error between the identification result and the actual observed value of the output voltage of the battery module.

[0241] Exemplarily, it is assumed that the historical observation data includes sampling data corresponding to each of K sampling moments. In the first iteration process, the initial values ​​of the first resistor, the second resistor, the first capacitor and the battery capacity, the initial state of charge, the first sampling data in the historical observation data of the output current, and the first sampling data in the historical observation data of the output voltage are input into the identification model to obtain the first value of the objective function. According to the first value, the values ​​of the real-time state of charge, the first resistor, the second resistor, the first capacitor and the battery capacity are adjusted to obtain their respective first adjustment values; the first adjustment value, the second sampling data in the historical data of the output current, and the second sampling data in the historical observation data of the output voltage are input into the identification model to obtain the second value of the objective function. If the second value meets the 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 adjustment values; the second adjustment value, the third sampled data in the historical data of the output current, and the third sampled data in the historical observation data of the output voltage are input into the identification model to obtain the third value of the objective function. And so on.

[0242] 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.

[0243] Optionally, 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:

[0244] Determining a feasible range of the non-observable quantity according to the first value;

[0245] 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.

[0246] The feasible range here is equivalent to a neighborhood of the current value of the non-observable 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.

[0247] Among them, one implementation method of determining the first adjustment value from the feasible range is to input each sampled value within the feasible range into the objective function, calculate the value of the corresponding objective function, and determine the sampled value corresponding to the calculated minimum 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 value of the objective function within the feasible range. The embodiments of the present application do not specifically limit this.

[0248] In the embodiment of the present application, the feasible range of the non-observable quantity is continuously adjusted according to the value of the objective function, and then the point with the minimum value of the objective function is sought within the feasible range, which is equivalent to converting the point search problem into a local range search problem. In this way, it is possible to quickly converge to the optimal solution, effectively improving the efficiency of parameter identification, thereby improving the efficiency of battery health state estimation.

[0249] In some embodiments, the identification result includes the battery capacity of the battery module. Accordingly, S103 may include:

[0250] The health status of the battery in the battery module is predicted according to the value of the battery capacity in the identification result.

[0251] 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 when the battery is designed and manufactured.

[0252] In the embodiment of the present application, since the battery parameters of the identified battery module are relatively accurate, the health status of the battery estimated based on the identification result is also more accurate.

[0253] 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.

[0254] Figure 6 Schematic diagram of the structure of the electronic device provided in the embodiment of the present application. Figure 6 As shown, the electronic device 6 of this embodiment includes: at least one processor 60 ( Figure 6 Only one is shown in the figure) a processor, a memory 61, and a computer program 62 stored in the memory 61 and executable on the at least one processor 60, wherein the processor 60 implements the steps in any of the above-mentioned battery health status estimation method embodiments when executing the computer program 62.

[0255] The electronic device may be a computing device such as a desktop computer, a notebook, 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 understand that Figure 6 It is only an example of the electronic device 6 and does not constitute a limitation on the electronic device 6. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0256] 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, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0257] 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. Further, the memory 61 may also include both an internal storage unit and an external storage device of the electronic device 6. The memory 61 is used to store an operating system, an application program, 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 to be output.

[0258] 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, wherein the processor implements the battery health status estimation method in the above-mentioned embodiments when executing the computer program.

[0259] An embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the battery health status estimation method in the above embodiments is implemented.

[0260] 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 executes the above-mentioned related steps to implement the battery health status estimation method in the above-mentioned embodiment.

[0261] 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, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the device / terminal device, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), 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.

[0262] 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.

[0263] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

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, characterized in that: When the computer program is executed by a processor, the battery health state estimation method according to any one of claims 1 to 19 is implemented.

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