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

Abstract

The invention relates to the field of batteries, in particular to 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 a first mapping relation between a first open-circuit voltage of an electrode in a to-be-detected battery module and a second open-circuit voltage of a battery; performing parameter identification on the battery parameters of the battery module according to the first mapping relation 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 change condition of the OCV of the battery can be described more accurately, so that the accuracy of estimating the health state of the battery is 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] The state of health (SOH) of a battery indicates the ratio of the total available capacity of a battery under certain conditions to the available capacity of a new battery. As the battery is used, many physical and chemical factors cause the battery capacity to decay, resulting in a decrease in the battery SOH. At present, the main parameters that characterize battery aging include the decay of the maximum battery capacity and the increase in the battery's internal resistance. SOH is an important status parameter of the battery management system (BMS) and an important tool to ensure the safe, effective and reliable operation of the battery. Therefore, accurate SOH estimation is of great significance for the safe operation and life management of the battery. 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 can effectively improve the 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] Acquire a first mapping relationship between a first open circuit voltage of an electrode in a battery module to be tested and a second open circuit voltage of a battery;

[0007] Performing parameter identification on the battery parameters of the battery module according to the first mapping relationship 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 open circuit voltage of the battery is represented by the open circuit voltage of the electrode, which can more accurately describe the change of the battery OCV. Therefore, parameter identification through the mapping relationship between the two can effectively improve the accuracy of parameter identification, thereby improving the accuracy of battery health status estimation.

[0010] In an implementation of the first aspect, the step of obtaining a first mapping relationship between a first open circuit voltage of an electrode in a battery module to be tested and a second open circuit voltage of a battery includes:

[0011] Acquire an equivalent circuit model of the battery module, wherein the battery in the equivalent circuit model is represented by electrodes;

[0012] The first mapping relationship is acquired according to the equivalent circuit model.

[0013] In the embodiment of the present application, the electrochemical reaction and ion transport in the positive electrode and the negative electrode are considered respectively according to the electrochemical mechanism model, and the battery charging and discharging reaction is described more accurately from the structure and mechanism. The OCV of the battery is replaced by the positive electrode OCV and the negative electrode OCV. After the battery ages, the OCV curve of the battery will also change to a certain extent. Therefore, through the equivalent circuit model in the embodiment of the present application, the change process of the battery OCV can be more accurately described, which is conducive to improving the accuracy of the subsequent battery health status estimation.

[0014] In an implementation of the first aspect, the step of performing parameter identification on the battery parameters of the battery module according to the first mapping relationship to obtain an identification result includes:

[0015] Constructing a mathematical model of the replaced equivalent circuit model according to the first mapping relationship;

[0016] The battery parameters are identified according to the mathematical model to obtain the identification results.

[0017] 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.

[0018] In an implementation of the first aspect, constructing a mathematical model of the replaced equivalent circuit model according to the first mapping relationship includes:

[0019] The mathematical model is constructed according to the voltage relationship between the various sub-circuits in the equivalent circuit model and the first mapping relationship.

[0020] In other implementations, a mathematical model can also be constructed based on the current relationship between the sub-circuits in the equivalent circuit model. In the embodiment of the present application, a mathematical model is constructed based on the voltage relationship. Since the voltage expression involves operations such as multiplication and addition and subtraction, the calculation method is relatively simple and the error rate of the calculation result is relatively low.

[0021] In an implementation of the first aspect, constructing the mathematical model according to the voltage relationship between the sub-circuits in the equivalent circuit model and the first mapping relationship includes:

[0022] Constructing a first expression according to the voltage relationship between the various sub-circuits in the equivalent circuit model;

[0023] Constructing a second expression according to the first mapping relationship;

[0024] The mathematical model is constructed according to the first expression and the second expression.

[0025] In the embodiment of the present application, a mathematical model is constructed based on the voltage relationship. Since the voltage expression involves multiplication, addition and subtraction, the calculation method is relatively simple and the error rate of the calculation result is relatively low. In addition, the second expression of the mathematical model is constructed based on the first mapping relationship, which is equivalent to the voltage of the battery in the mathematical model being represented by the voltage of the electrode, which can more accurately describe the change process of the battery OCV and is conducive to improving the accuracy of the subsequent battery health status estimation.

[0026] In an implementation of the first aspect, constructing the mathematical model according to the first expression and the second expression includes:

[0027] Acquire a second mapping relationship between the first state of charge of the electrode and the second state of charge of the battery;

[0028] Acquire a third mapping relationship between the first open circuit voltage and the first state of charge;

[0029] Acquire a fourth mapping relationship between the second open circuit voltage and the second state of charge;

[0030] Determine a third expression according to the second mapping relationship, the third mapping relationship, the fourth mapping relationship and the second expression;

[0031] The mathematical model is constructed according to the third expression and the first expression.

[0032] In the embodiment of the present application, it is equivalent to converting the mapping relationship between the voltage of the battery and the electrode into a mapping relationship between the charge state of the battery and the charge state of the electrode. The mathematical model constructed in this way is used for subsequent parameter identification, which is conducive to improving the estimation accuracy.

[0033] In an implementation of the first aspect, the electrode includes a positive electrode and a negative electrode, and the first state of charge includes a third state of charge of the positive electrode and a fourth state of charge of the negative electrode;

[0034] The acquiring a second mapping relationship between the first state of charge of the electrode and the second state of charge of the battery includes:

[0035] Constructing a fifth mapping relationship between a third state of charge of the positive electrode in the electrode and the second state of charge of the battery;

[0036] A sixth mapping relationship between a fourth state of charge of the negative electrode in the electrodes and the second state of charge of the battery is constructed.

[0037] In the embodiment of the present application, the electrode is split into a positive electrode and a negative electrode. In this way, the charge state of the electrode can be more accurately reflected, so that the constructed mathematical model can more accurately reflect the actual circuit state of the battery.

[0038] In an implementation of the first aspect, the third state of charge includes a seventh state of charge and an eighth state of charge, wherein the seventh state of charge represents the state of charge of the positive electrode when the battery is fully discharged, and the eighth state of charge represents the state of charge of the positive electrode when the battery is fully charged;

[0039] The constructing a fifth mapping relationship between the third state of charge of the positive electrode in the electrode and the second state of charge of the battery includes:

[0040] Acquire a first relationship between the second state of charge, the maximum capacity of the battery, and the current storage capacity of the battery;

[0041] Obtaining a second relationship between the third state of charge, the maximum capacity of the battery, and the current storage capacity of the positive electrode;

[0042] Obtaining a third relationship between the maximum capacity of the positive electrode, the seventh state of charge, the current storage capacity of the positive electrode, and the current storage capacity of the battery;

[0043] Obtaining a fourth relationship between the maximum capacity of the battery, the maximum capacity of the positive electrode, the seventh state of charge, and the eighth state of charge;

[0044] The fifth mapping relationship is calculated according to the first relationship expression, the second relationship expression, the third relationship expression and the fourth relationship expression.

[0045] In the embodiment of the present application, constructing a mapping relationship based on parameters such as the maximum capacity and storage capacity of the electrode is equivalent to constructing a mapping relationship based on the internal mechanism of the electrode. In this way, the mapping relationship between the electrode and the charge state of the battery can be more accurately reflected, thereby facilitating the construction of a more accurate mathematical model.

[0046] In an implementation of the first aspect, performing parameter identification on the battery parameter according to the mathematical model to obtain the identification result includes:

[0047] Discretizing the mathematical model to obtain a discrete model;

[0048] Parameter identification is performed on battery parameters of the battery module according to the discrete model to obtain the identification result.

[0049] In practical applications, the sampled data of 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.

[0050] In an implementation of the first aspect, the discrete model includes independent variables and dependent variables, and the independent variables include observed quantities and the battery parameters to be identified;

[0051] The step of performing parameter identification on the battery parameters of the battery module according to the discrete model to obtain the identification result includes:

[0052] Acquiring actual operating condition data of the battery module, wherein the actual operating condition data includes first data of the observed variable and second data of the dependent variable;

[0053] Inputting the first data into the discrete model to obtain third data of the dependent variable of the discrete model;

[0054] Adjusting the battery parameter to be identified in the discrete model according to the third data to obtain a first adjustment value of the battery parameter;

[0055] Inputting the first adjustment value and the first data into the discrete model to obtain fourth data of the dependent variable of the discrete model;

[0056] If the difference between the second data and the fourth data satisfies a first preset condition, determining the current value of the battery parameter as the identification result;

[0057] If the difference between the second data and the fourth data does not satisfy the first preset condition, continue to adjust the battery parameter to be identified in the discrete model according to the fourth data until the difference between the data of the dependent variable of the discrete model and the second data satisfies the first preset condition.

[0058] In the embodiment of the present application, when the difference between the identified data and the real data meets the first preset condition, it means that the identified data is close to the real data, otherwise the identification will continue. In this way, it can ensure that the identified data is close to the real data, which is conducive to improving the estimation accuracy of the subsequent battery health status.

[0059] In an implementation of the first aspect, adjusting the battery parameter to be identified in the discrete model according to the third data to obtain a first adjustment value of the battery parameter includes:

[0060] Determining a feasible range of the battery parameter to be identified according to the third data;

[0061] The first adjustment value is determined from the feasible range, wherein a difference between data of the dependent variable corresponding to the first adjustment value and the second data is smaller than a difference between data of the dependent variable corresponding to any adjustment value within the feasible range and the second data.

[0062] In the embodiment of the present application, the feasible range of the non-observable quantity is continuously adjusted according to the least squares model, and then the point with the minimum value of the least squares model 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.

[0063] In an implementation of the first aspect, the obtaining actual operating condition data of the battery module includes:

[0064] The actual operating condition data is subjected to data cleaning processing to obtain the processed actual operating condition data, wherein the data cleaning processing is used to filter out invalid values.

[0065] In the embodiment of the present application, the data cleaning process can filter out invalid values ​​in the data, which can effectively reduce the impact of invalid data on subsequent parameter identification results, and is conducive to improving the accuracy of subsequent parameter identification.

[0066] In an implementation of the first aspect, the obtaining actual operating condition data of the battery module includes:

[0067] Selecting data satisfying a second preset condition from the actual operating condition data to obtain the processed actual operating condition data;

[0068] The second preset condition includes that the actual operating condition data is continuous charge and discharge data, and the difference between the maximum state of charge and the minimum state of charge in the actual operating condition data is greater than a preset value.

[0069] In the embodiment of the present application, continuous charge and discharge data and actual operating condition data in which the SOC span during charging is less than a preset value are screened out, which can effectively reduce the impact of abnormal data on subsequent parameter identification results and help improve the accuracy of subsequent parameter identification.

[0070] In an implementation of the first aspect, the obtaining actual operating condition data of the battery module includes:

[0071] Filtering out abnormal data in the actual operating condition data to obtain the filtered actual operating condition data;

[0072] The filtered actual operating condition data are sorted according to a preset sampling interval to obtain the sorted actual operating condition data.

[0073] Through the above implementation method, effective and relatively accurate actual working condition data can be obtained, providing a reliable data basis for subsequent estimation.

[0074] In an implementation of the first aspect, the discretizing the mathematical model to obtain a discrete model includes:

[0075] Acquiring the sampling frequency of the actual working condition data;

[0076] The mathematical model is discretized according to the sampling frequency to obtain the discrete model.

[0077] 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.

[0078] In an implementation of the first aspect, the identification result includes a current battery capacity of the battery module;

[0079] The predicting the health status of the battery in the battery module according to the identification result includes:

[0080] The health status of the battery in the battery module is calculated according to the value of the battery capacity in the identification result.

[0081] 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.

[0082] In a second aspect, an embodiment of the present application provides an electronic device, 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, a battery health status estimation method as described in any one of the first aspects above is implemented.

[0083] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the battery health status estimation method as described in any one of the above-mentioned first aspects is implemented.

[0084] 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.

[0085] 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.

[0086] 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

[0087] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Moreover, the same reference numerals are used throughout the drawings to represent the same components. In the drawings:

[0088] Figure 1 It is a flowchart of a battery health status estimation method provided in an embodiment of the present application;

[0089] Figure 2 is a schematic diagram of a second-order equivalent circuit provided in an embodiment of the present application;

[0090] Figure 3 is a schematic diagram of an equivalent circuit model provided in an embodiment of the present application;

[0091] Figure 4 is a schematic diagram of an equivalent circuit model provided by another embodiment of the present application;

[0092] Figure 5 It is a flow chart of the parameter identification method provided in the embodiment of the present application;

[0093] Figure 6 It is a module schematic diagram of a battery health status estimation device provided in an embodiment of the present application;

[0094] Figure 7 It is a schematic diagram of the OCV-SOC mapping curve of the positive and negative electrodes of the battery cell provided in the embodiment of the present application;

[0095] Figure 8 It is a schematic diagram of the simulated and measured battery OCV-SOC mapping curves provided in the embodiments of the present application;

[0096] Fig. 9It is a schematic diagram of the battery voltage, current, simulated voltage and OCV curve provided in the embodiment of the present application;

[0097] Fig.10 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0098] The following embodiments of the technical solution of the present application are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0104] In the description of the embodiments of the present application, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the embodiments of the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the embodiments of the present application.

[0105] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0106] The state of health (SOH) of a battery indicates the ratio of the total available capacity of a battery under certain conditions to the available capacity of a new battery. As the battery is used, many physical and chemical factors cause the battery capacity to decay, resulting in a decrease in the battery SOH. At present, the main parameters that characterize battery aging include the decay of the maximum battery capacity and the increase in the battery's internal resistance. SOH is an important status parameter of the battery management system (BMS) and an important tool to ensure the safe, effective and reliable operation of the battery. Therefore, accurate SOH estimation is of great significance for the safe operation and life management of the battery.

[0107] The equivalent circuit model is used to estimate the state of the battery, such as SOH and battery state of charge (SOC). Usually, a set of state equations are established using the equivalent circuit model to describe the load current and terminal voltage of the battery. A set of optimized parameters that minimize the error between the model predicted voltage and the battery terminal voltage is found through a parameter identification algorithm, and this parameter value is used to describe the state of the battery. The parameters that need to be identified in the equivalent circuit model include resistance R, capacitance C, capacity Q, state of charge SOC, etc. The battery open circuit voltage OCV (open circuit voltage) is input into the equivalent circuit model as a characteristic parameter of the battery, indicating the equilibrium voltage value of the battery under different SOC states. OCV greatly affects the values ​​of other parameters to be identified in the battery. In the use of the equivalent circuit model, it is assumed that the battery OCV remains unchanged, and the mapping relationship between the voltage and SOC in the OCV is proportionally adjusted to represent the change of the battery OCV with aging.

[0108] From a mechanistic perspective, the battery OCV is obtained by the difference between the OCV of the positive and negative electrodes of the battery. As the battery ages, the OCV of the positive and negative electrodes shrink and shift, resulting in changes in the battery OCV. Compared with the OCV in the initial state, not only does the mapping relationship between the battery OCV voltage and SOC change, but the OCV voltage platform also shrinks and shifts at a non-fixed ratio. As the degree of aging deepens, the degree of shrinkage and shift intensifies. The strategy used by the existing equivalent circuit model, namely proportionally regulating the mapping relationship between the voltage and SOC in the OCV, cannot accurately reflect the changes in the battery OCV, and therefore errors occur. This error affects the optimization values ​​of other ECM parameters to be identified, and ultimately reduces the estimation accuracy of the battery status SOH and SOC.

[0109] Based on this, 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 positive electrode OCV and the negative electrode OCV of the battery are used as characteristic parameters, and the reduction and offset of the positive electrode OCV and the negative electrode OCV are adjusted to replace the OCV of the battery for subsequent parameter identification. In the above manner, the changing trend of the battery OCV can be described more accurately, thereby effectively improving the estimation accuracy of the battery health status.

[0110] 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:

[0111] S101, obtaining a first mapping relationship between a first open circuit voltage of an electrode in a battery module to be tested and a second open circuit voltage of a battery.

[0112] 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.

[0113] From the battery mechanism, it can be known that the OCV of the battery is obtained by the difference between the positive electrode OCV and the negative electrode OCV of the battery. Therefore, there is a mapping relationship between the open circuit voltage of the electrode and the open circuit voltage of the battery. In the embodiment of the present application, the first mapping relationship is Among them, U OC Indicates the battery OCV, Indicates the positive electrode OCV of the battery, Indicates the negative electrode OCV of the battery.

[0114] In some embodiments, S101 may include:

[0115] Obtaining an equivalent circuit model of the battery module, wherein the battery in the equivalent circuit model is represented by electrodes;

[0116] The first mapping relationship is acquired according to the equivalent circuit model.

[0117] Among them, the equivalent circuit model refers to replacing a relatively complex structure of a part of the circuit with a relatively simple structure, and the circuit after the replacement maintains the same effect on the untransformed part (or external circuit) as the original circuit.

[0118] In some implementations, the equivalent circuit model may use a second-order RC equivalent circuit. Figure 2 , 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 2 The second-order RC equivalent circuit shown in FIG. 1 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.

[0119] In the embodiment of the present application, in order to simplify the calculation, the equivalent circuit model can adopt a first-order RC equivalent circuit. Figure 3 , is a schematic diagram of an equivalent circuit model provided in an embodiment of the present application. Figure 3As shown, the equivalent circuit includes a first resistor R0, a second resistor R1, a first capacitor C1 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 R1 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 C1 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.

[0120] 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.

[0121] According to the battery mechanism, the battery is replaced by the electrode to obtain the equivalent circuit model after replacement. Figure 4 , is a schematic diagram of an equivalent circuit model provided by another embodiment of the present application. Figure 4 As shown, Figure 3 The battery B0 in is replaced by the positive electrode Ep and the negative electrode En. Figure 4 From the equivalent circuit model shown, we can see that

[0122] Taking lithium batteries as an example, during the charging and discharging process, the positive and negative electrodes of lithium-ion batteries undergo oxidation and reduction reactions respectively, and lithium ions diffuse in the positive and negative electrode materials. The existing first-order RC equivalent circuit model (such as Figure 3 The equivalent circuit model shown in the figure considers the battery reaction as a whole, and the electrochemical reaction of the positive and negative electrodes is equivalent to a polarization reaction resistor R1 and a capacitor C1. The resistance caused by the solution and other factors is equivalent to the ohmic internal resistance R0, and it is believed that the battery OCV curve will not change with battery aging. In the embodiment of the present application, the electrochemical reaction and ion transport in the positive and negative electrodes are considered separately according to the electrochemical mechanism model, so as to more accurately describe the battery charging and discharging reactions in terms of structure and mechanism. The OCV of the battery is replaced with the positive electrode OCV and the negative electrode OCV. After the battery ages, the OCV curve of the battery will also change to a certain extent. Therefore, through Figure 4 The equivalent circuit model can more accurately describe the change process of battery OCV, which is beneficial to improve the accuracy of subsequent battery health status estimation.

[0123] S102: Perform parameter identification on battery parameters of the battery module according to the first mapping relationship to obtain an identification result.

[0124] 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.

[0125] 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 non-observable quantities (parameter values ​​cannot be obtained through observation). The data of the non-observable quantities in the battery module can be fitted through parameter identification, thereby obtaining the working state of the battery module, and then predicting the health state of the battery in the battery module. Among them, in the embodiment of the present application, the non-observable quantity is referred to as the battery parameter to be identified.

[0126] The specific process of step S102 can be found in the following Figure 5 The description in the embodiments will not be repeated here.

[0127] S103: predicting the health status of the batteries in the battery module according to the identification result.

[0128] Figure 1 In the illustrated embodiment, the open circuit voltage of the battery is represented by the open circuit voltage of the electrode, which can more accurately describe the change of the battery OCV. Therefore, parameter identification through the mapping relationship between the two can effectively improve the accuracy of parameter identification, thereby improving the accuracy of battery health status estimation.

[0129] In the embodiment of the present application, the identification result includes the current battery capacity of the battery module. Accordingly, step S103 may include:

[0130] The health status of the battery in the battery module is calculated according to the value of the battery capacity in the identification result.

[0131] For example, according to the formula Calculate the battery's health status. Indicates the current battery capacity of the battery module. Indicates the initial nominal capacity of the battery module. 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.

[0132] 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.

[0133] 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, S102 may include the following steps:

[0134] S501: construct a mathematical model of the replaced equivalent circuit model according to the first mapping relationship.

[0135] The mathematical model constructed in the embodiment of the present application can reflect the circuit operation state of the battery module. The mathematical model includes dependent variables and independent variables.

[0136] In some implementations of constructing a mathematical model, the mathematical model may be constructed based on Kirchhoff's voltage law, that is, the mathematical model may be constructed based on the voltage relationship between the various sub-circuits in the equivalent circuit model. 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 some other implementations of constructing a mathematical model, a mathematical model may be constructed based on Kirchhoff's current law, that is, a mathematical model may be constructed based on the current relationship between the sub-circuits in the equivalent circuit. In some 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] From the voltage calculation method U=IR, it can be seen that the voltage calculation method involves multiplication operations. Correspondingly, the expressions related to voltage involve addition and subtraction operations. The calculation method is relatively simple and the error rate of the calculation result is low. Therefore, it is preferred to use the method of constructing a mathematical model based on Kirchhoff's voltage law.

[0139] In some implementations, when the mathematical model is constructed using Kirchhoff's voltage law, S501 may include:

[0140] S601: construct a first expression according to the voltage relationship between the sub-circuits in the equivalent circuit.

[0141] Specifically, according to Kirchhoff's voltage law, the first expression is constructed as Ut =U OC (SOC)-R0I-U1. Among them, U t Indicates the output voltage of the battery module; U OC (SOC) represents the open circuit voltage of the battery, which is related to the battery's state of charge SOC; R0 is Figure 4 The first resistor shown; U1 is Figure 4 The voltage of the branch where R1 and C1 are located is shown; I represents the charge and discharge current of the battery module.

[0142] S602: Construct a second expression according to the first mapping relationship.

[0143] Specifically, according to the voltage relationship between the battery and the electrode in the battery mechanism, that is, the first mapping relationship, the second expression is constructed as follows: in, Indicates the positive electrode's OCV and the positive electrode's state of charge SOC p Related, Indicates the negative electrode's OCV and negative electrode's state of charge SOC n Related.

[0144] S603: construct the mathematical model according to the first expression and the second expression.

[0145] Specifically, the mathematical model may include the following formula:

[0146] U t =U OC (SOC)-R0I-U1 (1)

[0147]

[0148]

[0149]

[0150] Among them, Q max Indicates the maximum capacity that the battery can store under the upper and lower voltage conditions specified in the current health state.

[0151] Optionally, step S603 may include:

[0152] 1. Obtain a second mapping relationship between the first state of charge of the electrode and the second state of charge of the battery.

[0153] The state of charge is the ratio of the remaining capacity of a battery after it has been used for a period of time or has been left unused for a long time to its capacity in a fully charged state, usually expressed as a percentage. Usually, its value range is 0 to 1. When SOC = 0, it means that the battery is fully discharged; when SOC = 1, it means that the battery is fully charged (fully charged).

[0154] Wherein, the electrode includes a positive electrode and a negative electrode. Accordingly, the first state of charge includes a third state of charge of the positive electrode and a fourth state of charge of the negative electrode.

[0155] Step 1 may include: constructing a fifth mapping relationship between the third state of charge of the positive electrode in the electrode and the second state of charge of the battery;

[0156] A sixth mapping relationship between a fourth state of charge of the negative electrode in the electrodes and the second state of charge of the battery is constructed.

[0157] The following describes the construction process of the fifth mapping relationship as an example.

[0158] The third state of charge includes a seventh state of charge and an eighth state of charge, wherein the seventh state of charge indicates the state of charge of the positive electrode when the battery is fully discharged, and the eighth state of charge indicates the state of charge of the positive electrode when the battery is fully charged.

[0159] In one implementation, the fifth mapping relationship is constructed in the following manner:

[0160] Acquire a first relationship between the second state of charge, the maximum capacity of the battery, and the current storage capacity of the battery;

[0161] Obtaining a second relationship between the third state of charge, the maximum capacity of the positive electrode, and the current storage capacity of the positive electrode;

[0162] Obtaining a third relationship between the maximum capacity of the positive electrode, the seventh state of charge, the current storage capacity of the positive electrode, and the current storage capacity of the battery;

[0163] Obtaining a fourth relationship between the maximum capacity of the battery, the maximum capacity of the positive electrode, the seventh state of charge, and the eighth state of charge;

[0164] The fifth mapping relationship is calculated according to the first relationship expression, the second relationship expression, the third relationship expression and the fourth relationship expression.

[0165] It should be noted that the construction method of the sixth mapping relationship is similar to the construction method of the fifth mapping relationship, which will not be repeated here.

[0166] 2. Obtain a third mapping relationship between the first open circuit voltage and the first state of charge.

[0167] 3. Obtain a fourth mapping relationship between the second open circuit voltage and the second state of charge.

[0168] In some implementations, a SOC-OCV mapping relationship table may be pre-constructed. For example, actual OCV data may be collected, and corresponding OCV may be calculated based on the actual data, thereby establishing the SOC-OCV mapping relationship table.

[0169] 4. Determine a third expression according to the second mapping relationship, the third mapping relationship, the fourth mapping relationship and the second expression.

[0170] 5. Construct the mathematical model based on the third expression and the first expression.

[0171] For example, according to the definition of state of charge:

[0172]

[0173] Among them, SOC represents the second state of charge, Q represents the current storage capacity of the battery, and Q max Indicates the maximum capacity of the battery.

[0174] The electrode SOC is defined as follows:

[0175]

[0176]

[0177] Among them, SOC p and SOC n They represent the state of charge of the positive electrode (third state of charge) and the state of charge of the negative electrode (fourth state of charge), respectively. p and Q n Respectively represent the current storage capacity of the positive electrode and the current storage capacity of the negative electrode, and They represent the maximum capacity of the positive electrode and the maximum capacity of the negative electrode respectively.

[0178] Take lithium battery as an example. Inside the battery, Li + As an energy carrier, it shuttles between the positive and negative electrodes. + The amount of Li+ can be equivalent to the energy stored in the battery. The active Li+ inside the battery is all provided by the positive electrode material. During the battery's charge and discharge cycle, the amount of Li+ is conserved.

[0179]

[0180] in, Indicates the initial state of the positive electrode Li + The amount, Q loss It indicates the capacity lost during battery cycling, which can be equivalently expressed as active Li + The amount of loss.

[0181] According to the battery voltage range [V low ,V up ], Q is defined in [0, Q max ] range, corresponding to the battery SOC changes in the range of [0, 100%], and the boundary is defined as SOC0 and SOC 100 It is worth noting that at SOC0=0, and Not 0%; at SOC 100 =100%, and Not 100%. Among them, (seventh state of charge) and Indicates the SOC of the positive and negative electrodes when the battery's SOC is 0%. (eighth state of charge) and Indicates the SOC of the positive electrode and the negative electrode when the SOC of the battery is 100%.

[0182] At any other time,

[0183]

[0184]

[0185] Substituting formula (9-10) into formula (5-7), we can get

[0186]

[0187]

[0188] According to the above description, the maximum capacity of the battery can be determined by the positive and negative electrode materials in the battery SOC b0 and SOC b100 The corresponding electrode charge state is expressed as shown in formula (13):

[0189]

[0190] Substituting formula (13) into formula (11-12), we can get

[0191]

[0192]

[0193] Substituting formula (14-15) into formula (2), we can get

[0194]

[0195] It should be noted that, in the above example, formula (5) is recorded as the first relational formula, formula (6) is recorded as the second relational formula, formula (9) is recorded as the third relational formula, and formula (13) is recorded as the fourth relational formula. as well as They represent the third mapping relationship and the fourth mapping relationship respectively. Formula (16) is recorded as the third expression. The above mathematical model (1-4) can be replaced by:

[0196] U t =U OC (SOC)-R0I-U1 (1)

[0197]

[0198]

[0199]

[0200] It can be seen that the mathematical model described in formulas (1), (16), (3) and (4) replaces the mapping relationship between battery OCV and battery SOC with the mapping relationship between electrode OCV and electrode SOC compared to the mathematical model described in formulas (1-4). The mathematical model described in formulas (1), (16), (3) and (4) can more accurately describe the change of battery OCV, thereby helping to improve the subsequent estimation accuracy.

[0201] S502, performing parameter identification on the battery parameters according to the mathematical model to obtain the identification result.

[0202] 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.

[0203] In some embodiments, step S502 may include:

[0204] The mathematical model is discretized to obtain a discrete model; and the battery parameters of the battery module are identified according to the discrete model to obtain the identification result.

[0205] 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.

[0206] In the embodiment of the present application, one implementation method of the discretization processing is:

[0207] The sampling frequency of the actual working condition data is obtained; and the mathematical model is discretized according to the sampling frequency to obtain the discrete model.

[0208] The discrete model equation after discretization of the mathematical model described in the above formulas (1), (16), (3) and (4) is as follows:

[0209] U t (k) = U Oc (SOC(k))-U1(k)-R0I(k) (17)

[0210]

[0211]

[0212]

[0213] in, is the battery parameter to be identified. Δt represents the sampling interval, which is determined by the sampling frequency, U t I(k) represents the output voltage of the battery at the kth sampling moment, and I(k) represents the current at the kth sampling moment.

[0214] 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.

[0215] In practical applications, the sampled data of 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.

[0216] In some implementations, the discrete model includes independent variables and dependent variables, and the independent variables include observed quantities and the battery parameters to be identified.

[0217] Accordingly, the parameter identification process may include:

[0218] Acquiring actual operating condition data of the battery module, wherein the actual operating condition data includes first data of the observed variable and second data of the dependent variable;

[0219] Inputting the first data into the discrete model to obtain third data of the dependent variable of the discrete model;

[0220] Adjusting the battery parameter to be identified in the discrete model according to the third data to obtain a first adjustment value of the battery parameter;

[0221] Inputting the first adjustment value and the first data into the discrete model to obtain fourth data of the dependent variable of the discrete model;

[0222] If the difference between the second data and the fourth data satisfies a first preset condition, determining the current value of the battery parameter as the identification result;

[0223] If the difference between the second data and the fourth data does not satisfy the first preset condition, continue to adjust the battery parameter to be identified in the discrete model according to the fourth data until the difference between the data of the dependent variable of the discrete model and the second data satisfies the first preset condition.

[0224] In one implementation, 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 least squares model is:

[0225]

[0226] Where K 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, U k Represents the estimated value of the output voltage of the battery module at the kth sampling moment.

[0227] This optimization is a nonlinear least squares problem. By adding empirical constraints to narrow the parameter optimization space, and selecting reliable parameter initial values ​​to help the algorithm quickly converge to the optimal solution. For this nonlinear multi-parameter optimization problem with upper and lower bound constraints, the first adjustment value can be determined by:

[0228] Determining a feasible range of the battery parameter to be identified according to the third data;

[0229] The first adjustment value is determined from the feasible range, wherein a difference between data of the dependent variable corresponding to the first adjustment value and the second data is smaller than a difference between data of the dependent variable corresponding to any adjustment value within the feasible range and the second data.

[0230] In the embodiment of the present application, in each iteration, a polynomial function is first selected to approximate the local information of the objective function and solve the optimal solution of the polynomial function in the current neighborhood, and the update of the trust region radius is determined according to the ratio of the target descent of the polynomial function to the true target descent. If the obtained solution is outside the upper and lower bounds of the parameter to be identified (that is, the current solution is an infeasible solution), the parameter is projected back into the feasible domain, and the feasible point is used as the current iteration point. Repeat this process until the algorithm termination criterion is met to obtain the identification result of the parameter.

[0231] The feasible range here is equivalent to a neighborhood of the current value of the non-observable quantity, or a trust region. In the above method, if the third data is large, the feasible range can be appropriately reduced; if the third data is small, the feasible range can be appropriately expanded.

[0232] 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 discrete model, calculate the value of the corresponding least squares model, and determine the sampled value corresponding to the minimum value of the calculated least squares model 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.

[0233] In the embodiment of the present application, the feasible range of the non-observable quantity is continuously adjusted according to the least squares model, and then the point with the minimum value of the least squares model 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.

[0234] In an embodiment of the present application, one implementation method for obtaining actual operating condition data includes:

[0235] I. Performing data cleaning processing on the actual operating condition data to obtain the processed actual operating condition data, wherein the data cleaning processing is used to filter out invalid values.

[0236] The actual operating data obtained may include the operating data of the battery under different operating conditions, such as current, voltage, temperature, SOC, time and capacity, etc. The characteristic parameters of the battery cells in the battery can also be obtained, such as the OCV curves of the positive and negative electrodes of the battery cells, characteristic data that change with temperature, the voltage range of the battery cells, and the initial capacity of the battery cells, etc.

[0237] Invalid values ​​may include values ​​exceeding a preset threshold.

[0238] II. Select data that meets the second preset condition from the actual operating condition data to obtain the processed actual operating condition data; wherein the second preset condition includes that the actual operating condition data is continuous charge and discharge data, and the difference between the maximum state of charge and the minimum state of charge in the actual operating condition data is greater than a preset value.

[0239] For example, the input data is required to be a continuous charging condition, and the SOC span of the battery during the charging process is greater than 50%, that is, SOC(end)-SOC(start)>50%.

[0240] III. Sort the filtered actual operating condition data according to a preset sampling interval to obtain the sorted actual operating condition data.

[0241] For example, based on the data in step II, filter out abnormal data. Sort the filtered data in chronological order to obtain a data sequence. Calculate the average sampling interval (i.e., the preset sampling interval) based on the data sequence. If the data is insufficient, the data can be supplemented by interpolation. Finally, sort the data sequence according to the preset sampling interval to obtain the sorted actual working condition data.

[0242] Through the above implementation method, effective and relatively accurate actual working condition data can be obtained, providing a reliable data basis for subsequent estimation.

[0243] It should be noted that in other implementations, steps I to III may be processed one or more. However, when steps I to III are all processed, the actual operating data obtained is more accurate and more in line with the calculation requirements of the subsequent model.

[0244] See also Figure 6 , is a schematic diagram of a module of a battery health status estimation device provided in an embodiment of the present application. As an example and not a limitation, Figure 6 As shown, the battery health state estimation device may include:

[0245] The data acquisition module 61 is used to acquire real-time operating condition data.

[0246] The data processing module 62 is used to process the real-time operating condition data, such as the above steps I-III.

[0247] The model calculation module 63 is used to implement the steps of constructing an equivalent circuit model, constructing a mathematical model, discretizing the mathematical model, and performing parameter identification according to the mathematical model in the above embodiment.

[0248] The result processing module 64 is used to predict the current SOH of the battery according to the identification result obtained by parameter identification.

[0249] The result storage module 65 is used to store the predicted SOH and identification results.

[0250] See also Figure 7 , is a schematic diagram of the OCV-SOC mapping curve of the positive and negative electrodes of the battery cell provided in the embodiment of the present application. Figure 7 As shown, the solid line represents the mapping relationship of negative electrode OCV-SOC, and the dotted line represents the mapping relationship of positive electrode OCV-SOC. The positive / negative electrode SOC represents the ratio of the Li content in the electrode material to the maximum content, as defined in formula (6-7).

[0251] Using the battery and electrode OCV-SOC mapping relationship derived in the above embodiment, combined with Figure 7 The mapping relationship between the electrode OCV and SOC can simulate the OCV curve of the initial state of the battery. Figure 8 , is a schematic diagram of the simulation and measured battery OCV-SOC mapping curves provided in the embodiment of the present application. Figure 8 As shown, the simulated battery OCV-SOC mapping curve (solid line) is close to the measured battery OCV-SOC mapping curve (dashed line). It can be seen that the method in the embodiment of the present application can accurately describe the change trend of the battery OCV-SOC.

[0252] See also Fig. 9 , is a schematic diagram of the battery voltage, current, simulated voltage and OCV curve provided in the embodiment of the present application. Fig. 9 As shown, the charging method is step charging. The gray solid line is the charging step current curve, and the gray dotted line is the charging voltage curve. The black solid line is the simulated voltage curve, and the black dotted line is the simulated OCV curve. Among them, the gray dotted line is close to the black solid line, which shows that the method in the embodiment of the present application can estimate the voltage more accurately.

[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] Fig.10 Schematic diagram of the structure of the electronic device provided in the embodiment of the present application. Fig.10As shown, the electronic device 10 of this embodiment includes: at least one processor 100 ( Fig.10 Only one is shown in the figure) a processor, a memory 101, and a computer program 102 stored in the memory 101 and executable on the at least one processor 100, and when the processor 100 executes the computer program 102, the steps in any of the above-mentioned battery health status estimation method embodiments are implemented.

[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 Fig.10 This is only an example of the electronic device 10 and does not constitute a limitation on the electronic device 10 . The electronic device 10 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 100 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 101 may be an internal storage unit of the electronic device 10, such as a hard disk or memory of the electronic device 10. In other embodiments, the memory 101 may also be an external storage device of the electronic device 10, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 10. Further, the memory 101 may also include both an internal storage unit of the electronic device 10 and an external storage device. The memory 101 is used to store an operating system, an application program, a boot loader (Boot Loader), data and other programs, such as the program code of the computer program, etc. The memory 101 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 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.

[0259] 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.

[0260] 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.

[0261] 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.

[0262] 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: Acquire a first mapping relationship between a first open circuit voltage of an electrode in a battery module to be tested and a second open circuit voltage of a battery; Performing parameter identification on the battery parameters of the battery module according to the first mapping relationship to obtain an identification result; The health status of the batteries in the battery module is predicted according to the identification result.

2. The battery health status estimation method according to claim 1, characterized in that: The step of obtaining a first mapping relationship between a first open circuit voltage of an electrode in a battery module to be tested and a second open circuit voltage of a battery comprises: Acquire an equivalent circuit model of the battery module, wherein the battery in the equivalent circuit model is represented by electrodes; The first mapping relationship is acquired according to the equivalent circuit model.

3. The battery health status estimation 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 first mapping relationship to obtain an identification result includes: Constructing a mathematical model of the replaced equivalent circuit model according to the first mapping relationship; The battery parameters are identified according to the mathematical model to obtain the identification results.

4. The battery health status estimation method according to claim 3, characterized in that: The step of constructing a mathematical model of the replaced equivalent circuit model according to the first mapping relationship includes: The mathematical model is constructed according to the voltage relationship between the various sub-circuits in the equivalent circuit model and the first mapping relationship.

5. The battery health status estimation method according to claim 4, characterized in that: The step of constructing the mathematical model according to the voltage relationship between the sub-circuits in the equivalent circuit model and the first mapping relationship includes: Constructing a first expression according to the voltage relationship between the various sub-circuits in the equivalent circuit model; Constructing a second expression according to the first mapping relationship; The mathematical model is constructed according to the first expression and the second expression.

6. The battery health status estimation method according to claim 5, characterized in that: The step of constructing the mathematical model according to the first expression and the second expression comprises: Acquire a second mapping relationship between the first state of charge of the electrode and the second state of charge of the battery; Acquire a third mapping relationship between the first open circuit voltage and the first state of charge; Acquire a fourth mapping relationship between the second open circuit voltage and the second state of charge; Determine a third expression according to the second mapping relationship, the third mapping relationship, the fourth mapping relationship and the second expression; The mathematical model is constructed according to the third expression and the first expression.

7. The battery health status estimation method according to claim 6, characterized in that: The electrode comprises a positive electrode and a negative electrode, and the first state of charge comprises a third state of charge of the positive electrode and a fourth state of charge of the negative electrode; The acquiring a second mapping relationship between the first state of charge of the electrode and the second state of charge of the battery includes: Constructing a fifth mapping relationship between a third state of charge of the positive electrode in the electrode and the second state of charge of the battery; A sixth mapping relationship between a fourth state of charge of the negative electrode in the electrodes and the second state of charge of the battery is constructed.

8. The battery health status estimation method according to claim 7, characterized in that: The third state of charge includes a seventh state of charge and an eighth state of charge, wherein the seventh state of charge represents the state of charge of the positive electrode when the battery is fully discharged, and the eighth state of charge represents the state of charge of the positive electrode when the battery is fully charged; The constructing a fifth mapping relationship between the third state of charge of the positive electrode in the electrode and the second state of charge of the battery includes: Acquire a first relationship between the second state of charge, the maximum capacity of the battery, and the current storage capacity of the battery; Obtaining a second relationship between the third state of charge, the maximum capacity of the battery, and the current storage capacity of the positive electrode; Obtaining a third relationship between the maximum capacity of the positive electrode, the seventh state of charge, the current storage capacity of the positive electrode, and the current storage capacity of the battery; Obtaining a fourth relationship between the maximum capacity of the battery, the maximum capacity of the positive electrode, the seventh state of charge, and the eighth state of charge; The fifth mapping relationship is calculated according to the first relationship expression, the second relationship expression, the third relationship expression and the fourth relationship expression.

9. The battery health status estimation method according to any one of claims 3 to 8, characterized in that: The performing parameter identification on the battery parameters according to the mathematical model to obtain the identification result includes: Discretizing the mathematical model to obtain a discrete model; Parameter identification is performed on battery parameters of the battery module according to the discrete model to obtain the identification result.

10. The battery health status estimation method according to claim 9, characterized in that: The discrete model includes independent variables and dependent variables, and the independent variables include observed quantities and the battery parameters to be identified; The step of performing parameter identification on the battery parameters of the battery module according to the discrete model to obtain the identification result includes: Acquiring actual operating condition data of the battery module, wherein the actual operating condition data includes first data of the observed variable and second data of the dependent variable; Inputting the first data into the discrete model to obtain third data of the dependent variable of the discrete model; Adjusting the battery parameter to be identified in the discrete model according to the third data to obtain a first adjustment value of the battery parameter; Inputting the first adjustment value and the first data into the discrete model to obtain fourth data of the dependent variable of the discrete model; If the difference between the second data and the fourth data satisfies a first preset condition, determining the current value of the battery parameter as the identification result; If the difference between the second data and the fourth data does not satisfy the first preset condition, continue to adjust the battery parameter to be identified in the discrete model according to the fourth data until the difference between the data of the dependent variable of the discrete model and the second data satisfies the first preset condition.

11. The battery health status estimation method according to claim 10, characterized in that: The step of adjusting the battery parameter to be identified in the discrete model according to the third data to obtain a first adjustment value of the battery parameter includes: Determining a feasible range of the battery parameter to be identified according to the third data; The first adjustment value is determined from the feasible range, wherein a difference between data of the dependent variable corresponding to the first adjustment value and the second data is smaller than a difference between data of the dependent variable corresponding to any adjustment value within the feasible range and the second data.

12. The battery health status estimation method according to claim 10, characterized in that: The obtaining of actual operating condition data of the battery module includes: The actual operating condition data is subjected to data cleaning processing to obtain the processed actual operating condition data, wherein the data cleaning processing is used to filter out invalid values.

13. The battery health status estimation method according to claim 10, characterized in that: The obtaining of actual operating condition data of the battery module includes: Selecting data satisfying a second preset condition from the actual operating condition data to obtain the processed actual operating condition data; The second preset condition includes that the actual operating condition data is continuous charge and discharge data, and the difference between the maximum state of charge and the minimum state of charge in the actual operating condition data is greater than a preset value.

14. The battery health status estimation method according to claim 10, characterized in that: The obtaining of actual operating condition data of the battery module includes: Filtering out abnormal data in the actual operating condition data to obtain the filtered actual operating condition data; The filtered actual operating condition data are sorted according to a preset sampling interval to obtain the sorted actual operating condition data.

15. The battery health status estimation method according to claim 10, characterized in that: The step of discretizing the mathematical model to obtain a discrete model includes: Acquiring the sampling frequency of the actual working condition data; The mathematical model is discretized according to the sampling frequency to obtain the discrete model.

16. The battery health status estimation method according to any one of claims 1 to 15, characterized in that: The identification result includes the current 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 calculated according to the value of the battery capacity in the identification result.

17. An electronic device 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 state estimation method according to any one of claims 1 to 16 is implemented.

18. 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 16 is implemented.

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