Battery state of charge detection method, device, battery management system and battery pack

Abstract

Embodiments of the present application provide a battery state-of-charge detection method, device, battery management system, and battery pack. The method includes determining the state of charge (SOC) of a target battery based on the target battery's current theoretical characteristics, the values ​​of at least two component parameters included in an equivalent circuit model of the target battery, and a target monotonic function. The target monotonic function is determined based on the actual battery voltage corresponding to each SOC of the test battery when the test battery is at each theoretical characteristic, and the test battery and the target battery are of the same type. This method is used to improve the accuracy of predicted SOC.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a battery state of charge detection method, device, battery management system, and battery pack. Background Art

[0002] With the development of the battery industry, lithium batteries have become a widely used energy storage device. For lithium batteries, the state of charge (SOC) is a key indicator of their status. SOC essentially reflects the remaining charge in the battery. Accurately knowing the battery's SOC is crucial to its overall performance, safety, and lifespan. However, current methods for measuring SOC using the open circuit voltage (OCV)-SOC lookup table method and the ampere-hour integration method suffer from low accuracy.

[0003] Therefore, how to improve the accuracy of SOC detection is an urgent problem to be solved. Summary of the Invention

[0004] The battery state of charge detection method, device, battery management system and battery pack provided in the embodiments of the present application are used to improve the accuracy of predicting SOC.

[0005] In a first aspect, an embodiment of the present application provides a method for constructing a state of charge function, comprising:

[0006] The state of charge (SOC) of the target battery is determined based on the current theoretical characteristics of the target battery, the values ​​of at least two component parameters included in the equivalent circuit model of the battery, and a target monotonic function. The target monotonic function is determined based on the actual battery voltage corresponding to each SOC of the test battery when the test battery is at each theoretical characteristic. The test battery and the target battery are of the same type.

[0007] Optionally, the current theoretical characteristic includes at least one of a current target battery temperature and a current target battery current.

[0008] Optionally, also include:

[0009] Acquire values ​​of at least two component parameters included in an equivalent circuit model of the target battery at the current target battery temperature and the current target battery current.

[0010] Optionally, acquiring values ​​of at least two component parameters included in the equivalent circuit model of the target battery at the current target battery temperature and the current target battery current includes:

[0011] Obtaining a current target battery temperature, a current target battery current, and a current target battery voltage of the target battery within a preset time period;

[0012] Based on the equivalent circuit model of the target battery, the current target battery temperature, the current target battery current, and the current target battery voltage, values ​​of at least two component parameters included in the equivalent circuit model are acquired.

[0013] Optionally, also include:

[0014] When the initial SOC of the target battery is different from the determined SOC of the target battery, the initial SOC is calibrated according to the determined SOC of the target battery.

[0015] Optionally, calibrating the initial SOC according to the determined SOC of the target battery includes:

[0016] determining a correction parameter for a rate of change of the initial SOC according to the determined difference between the target battery SOC and the initial SOC;

[0017] The change rate of the initial SOC is adjusted based on the correction parameter to gradually correct the initial SOC until the difference between the initial SOC and the SOC of the target battery is less than or equal to a preset difference threshold.

[0018] Optionally, the method further includes:

[0019] Obtaining, according to actual battery voltages corresponding to respective SOCs of the test battery when the test battery is under respective theoretical characteristics, a mapping relationship between at least two component parameters of an equivalent circuit model of the test battery under each theoretical characteristic and the SOC of the test battery;

[0020] According to the mapping relationship between at least two component parameters of the equivalent circuit model of the test battery under each theoretical characteristic and the SOC of the test battery, the target monotonic function is determined, and the target monotonic function characterizes the mapping relationship between the theoretical characteristics, the SOC, and at least two component parameters.

[0021] Optionally, the theoretical characteristic includes at least one of a test battery temperature and a test battery current.

[0022] Optionally, determining a target monotonic function according to a mapping relationship between at least two component parameters of the equivalent circuit model of the test battery under each of the theoretical characteristics and the SOC of the test battery includes:

[0023] Determining, based on a mapping relationship between each component parameter of the equivalent circuit model of the test battery and the SOC under each theoretical characteristic, a first function corresponding to each component parameter, wherein the first function represents a functional relationship between each component parameter and the SOC under each theoretical characteristic;

[0024] The target monotonic function is generated according to each of the first functions under each of the theoretical characteristics.

[0025] Optionally, generating the target monotonic function according to each of the first functions under each of the theoretical characteristics includes:

[0026] determining an initial function according to each of the first functions;

[0027] The coefficients corresponding to the component parameters in the initial function are adjusted to generate the target monotonic function under the theoretical characteristics.

[0028] Optionally, adjusting the coefficients corresponding to the component parameters in the initial function to generate the target monotonic function includes:

[0029] determining a derivative of the initial function with respect to the SOC;

[0030] Determining target values ​​of the coefficients that meet the preset adjustment target based on the derivative function and a preset adjustment target, wherein the preset adjustment target includes: the SOC value is within the charge and discharge range, and the derivative function is always greater than 0 or the derivative function is always less than 0;

[0031] Substitute the target value of each coefficient into the initial function to generate the target monotonic function.

[0032] Optionally, obtaining, based on the actual battery voltage corresponding to each state of charge (SOC) of the test battery when the test battery is under the theoretical characteristics, a mapping relationship between at least two component parameters of the equivalent circuit model of the test battery under the theoretical characteristics and the SOC includes:

[0033] Obtaining a set of error equations based on a changing relationship between at least two of the component parameters and the battery voltage corresponding to a plurality of time periods when the test battery is under each of the theoretical characteristics, and the actual battery voltage;

[0034] Obtaining at least two target component parameters corresponding to the actual battery voltage according to the error equation group;

[0035] According to the SOC corresponding to the actual battery voltage and the at least two target component parameters, a mapping relationship between the at least two component parameters and the SOC of the test battery is determined.

[0036] Optionally, obtaining at least two target component parameters corresponding to the actual battery voltage according to the error equation group includes:

[0037] Obtaining initial values ​​of the error equation group;

[0038] After the error equation group is iteratively updated according to the initial value until the error function of the error equation group meets a preset convergence condition, at least two target component parameters are obtained.

[0039] Optionally, the target component parameter includes at least one of ohmic internal resistance, polarization resistance, and capacitance, and obtaining the initial value of the error equation group includes:

[0040] determining an initial value of the ohmic internal resistance based on a transient voltage change of the test battery at the start of discharge;

[0041] and / or,

[0042] Obtaining an initial value of the polarization resistance according to a voltage value of the test battery when it is discharged to a steady state and a changing relationship between at least two of the component parameters and the battery voltage of the test battery;

[0043] and / or,

[0044] An initial value of the capacitance is obtained according to the change relationship.

[0045] Optionally, the change relationship includes at least two capacitors, and the initial values ​​of at least two capacitors are the same;

[0046] and / or,

[0047] The change relationship includes at least two polarization resistors, and the initial values ​​of at least two polarization resistors are the same.

[0048] Optionally, the equivalent circuit model includes at least one of the following: Rint model, Thevenin model, PNGV model, RC model, Randles model, and FOM model.

[0049] In a second aspect, an embodiment of the present application provides a device for constructing a state of charge function, the device comprising:

[0050] a processing module for determining a state of charge (SOC) of a target battery based on the current theoretical characteristics of the target battery, values ​​of at least two component parameters included in an equivalent circuit model of the target battery, and a target monotonic function, wherein the target monotonic function is determined based on actual battery voltages corresponding to respective SOCs of the test battery when the test battery is at respective theoretical characteristics, and the test battery and the target battery are of the same type.

[0051] In a third aspect, an embodiment of the present application provides a battery management system, which is used to execute the battery state of charge detection method as described in any one of the first aspects.

[0052] In a fourth aspect, an embodiment of the present application provides a battery pack, which includes the battery management system in the third aspect.

[0053] In a fifth aspect, an embodiment of the present application provides an electric device, wherein the battery pack of the electric device is the battery pack in the fourth aspect.

[0054] In a sixth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement various possible implementation methods in the first aspect above.

[0055] In a seventh aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements various possible implementation methods in the first aspect above.

[0056] The battery state-of-charge detection method, device, battery management system, and battery pack provided in the embodiments of the present application pre-construct a target monotonic function based on the mapping relationship between the theoretical characteristics characterizing the test battery, the SOC, and at least two component parameters. The current theoretical characteristics of the target battery corresponding to the test battery during actual use and at least two component parameters included in the target battery's equivalent circuit model are substituted into the target monotonic function to determine the target battery's SOC. This method predicts the battery's SOC based on the real-time status of multiple component parameters in the battery's equivalent circuit. Compared to the current OCV-SOC lookup table method, this method can reduce the problem of inaccurate SOC prediction caused by the lithium battery's voltage platform range, thereby improving the accuracy of the predicted SOC. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0058] Figure 1A schematic diagram of the structure of an equivalent circuit model provided in an embodiment of the present application;

[0059] Figure 2 A flowchart of a battery state of charge detection method provided in an embodiment of the present application;

[0060] Figure 3 A flowchart of another battery state of charge detection method provided in an embodiment of the present application;

[0061] Figure 4 A flowchart of another battery state of charge detection method provided in an embodiment of the present application;

[0062] Figure 5 A schematic diagram of a mapping relationship provided in an embodiment of the present application;

[0063] Figure 6 A flowchart of another battery state of charge detection method provided in an embodiment of the present application;

[0064] Figure 7 A flowchart of another battery state of charge detection method provided in an embodiment of the present application;

[0065] Figure 8 A schematic structural diagram of a battery state of charge detection device provided in an embodiment of the present application.

[0066] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0067] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0068] Currently, SOC is primarily measured using the OCV-SOC lookup table method and the ampere-hour integration method. While this method is simple, for lithium batteries (such as ternary lithium batteries and lithium iron phosphate batteries), the OCV of a lithium battery exhibits a voltage plateau within a specific SOC range (for example, the OCV of a lithium iron phosphate battery exhibits a voltage plateau when the SOC is between 20% and 90%). Within the SOC range corresponding to the voltage plateau, the OCV value is relatively stable and does not change significantly with SOC. Therefore, within the SOC range corresponding to the voltage plateau, using the OCV-SOC lookup table method and the ampere-hour integration method to measure SOC cannot accurately obtain the SOC, resulting in low SOC prediction accuracy.

[0069] In view of this, the present application provides a battery state-of-charge detection method that determines the state of charge (SOC) of a target battery based on the current theoretical characteristics of the target battery, the values ​​of at least two component parameters included in the target battery's equivalent circuit model, and a target monotonic function determined based on the actual battery voltage corresponding to each SOC when the target battery's corresponding test battery is at each theoretical characteristic. This method predicts the battery's SOC based on the real-time status of multiple component parameters in the battery's equivalent circuit. Compared to the current OCV-SOC lookup table method, this method can reduce the problem of inaccurate SOC prediction caused by the voltage platform range of the lithium battery, thereby improving the accuracy of the predicted SOC.

[0070] The execution entity of the battery state of charge detection method can be the core control unit of the battery pack, such as the battery management system (BMS), the microprocessor inside the battery pack, etc.; or it can be a dedicated SOC prediction chip. Alternatively, it can be a processing chip of the electrical device carrying the battery pack. Alternatively, the execution entity can also be a third-party device with computing capabilities, such as a mobile phone, computer, tablet computer, server, etc. When the execution entity is a third-party device, the current battery temperature, current battery current, and the values ​​of the component parameters included in the battery's equivalent circuit model can be input into the third-party device, and the third-party device can determine the battery's SOC through calculation.

[0071] The following describes in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems through specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0072] The state of charge (SOC) of the target battery is determined according to the current theoretical characteristics of the target battery, values ​​of at least two component parameters included in the equivalent circuit model of the target battery, and a target monotonic function.

[0073] The target monotonic function is determined based on the actual battery voltage corresponding to each SOC of the test battery when the test battery is in each theoretical characteristic. The test battery and the target battery are of the same type. For example, if the test battery and the target battery are of the same model, and the difference in battery characteristics (such as capacity, operating parameters, etc.) between the test battery and the target battery is less than or equal to the preset difference threshold, the operating data of the test battery can be used as the reference operating data of the target battery. For details on how to construct the target monotonic function, please refer to the subsequent Figure 6 The embodiments will not be described in detail here.

[0074] It should be understood that the current theoretical characteristics and theoretical characteristics are only used to distinguish whether they are used in the target battery SOC determination stage or the test battery in the target monotonic function construction stage. The parameters contained in the two should be the same so that the target monotonic function can accurately detect the target battery SOC. Among them, the target battery corresponds to the current theoretical characteristics, and the test battery corresponds to the theoretical characteristics.

[0075] For example, the current theoretical characteristic may include at least one of the current target battery temperature and the current target battery current. The theoretical characteristic has the same content as the current theoretical characteristic. For example, if the current theoretical characteristic includes the current target battery temperature and the current target battery current, the theoretical characteristic also includes the battery temperature and the battery current.

[0076] The equivalent circuit model of the target battery can be, for example, any one of the models such as the Rint model, the Thevenin model, the PNGV model, the RC model, the Randles model, and the FOM model, which can be determined according to actual needs and is not limited in this application. For different equivalent circuit models corresponding to the target battery, the component parameters included in the model may also be different.

[0077] For example, Figure 1 This is a schematic diagram of the structure of an equivalent circuit model provided in an embodiment of the present application. Figure 1 As shown, taking the equivalent circuit model of the target battery as a second-order RC model as an example, the component parameters included may include: power supply voltage , ohmic internal resistance , the first polarization resistance , the first capacitor , the second polarization resistance , the second capacitor ,in, Figure 1 V in ois the battery voltage of the target battery.

[0078] In one possible implementation, different current theoretical characteristics correspond to different target monotonic functions. Taking the current theoretical characteristics including the current target battery temperature and the current target battery current as an example, the target monotonic function corresponding to the current target battery current of I1 is different from the target monotonic function corresponding to the current target battery current of I2; the target monotonic function corresponding to the current target battery temperature of T1 and the current target battery current of I1 is different from the target monotonic function corresponding to the current target battery temperature of T2 and the current target battery current of I1, etc. In this implementation, the corresponding target monotonic function can be determined according to the current theoretical characteristics of the target battery, and then the corresponding SOC can be determined based on the corresponding target monotonic function and the values ​​of at least two component parameters included in the equivalent circuit model of the target battery at the current moment.

[0079] In another possible implementation, the target monotonic function directly represents the mapping relationship between the current theoretical characteristics and the values ​​of at least two component parameters included in the equivalent circuit model of the target battery. In this implementation, for example, the current theoretical characteristics and the values ​​of at least two component parameters included in the equivalent circuit model of the target battery can be directly substituted into the target monotonic function to obtain the battery SOC.

[0080] The method provided in an embodiment of the present application pre-constructs a target monotonic function based on the mapping relationship between the theoretical characteristics characterizing the test battery, the SOC, and at least two component parameters. The current theoretical characteristics of the target battery corresponding to the test battery during actual use and at least two component parameters included in the target battery's equivalent circuit model are substituted into the target monotonic function to determine the SOC of the target battery. This method predicts the battery's SOC based on the real-time status of multiple component parameters in the battery's equivalent circuit. Compared to the current OCV-SOC lookup table method, this method can reduce the problem of inaccurate SOC prediction caused by the voltage platform range of the lithium battery, thereby improving the accuracy of the predicted SOC.

[0081] Optionally, before determining the SOC of the battery based on the current battery temperature, the current battery current, the values ​​of the component parameters included in the equivalent circuit model of the battery, and the target monotonic function, the method may also include obtaining the component parameters included in the equivalent circuit model of the battery.

[0082] In one possible implementation, at least two component parameters included in the target battery's equivalent circuit model can be directly acquired by collecting parameters from the target battery's circuit. For example, if the target battery's equivalent circuit model includes three component parameters: ohmic internal resistance, polarization resistance, and capacitance, the ohmic internal resistance can be acquired through methods such as direct current discharge and alternating current impedance analysis; and the polarization resistance and capacitance can be acquired through methods such as pulse discharge and electrochemical impedance spectroscopy. The specific methods for acquiring parameters from the target battery's circuit using these methods can be referenced to existing technologies and will not be elaborated upon here.

[0083] Another possible implementation method is to solve the equivalent circuit model of the target battery based on the current battery temperature, current current, and current battery voltage of the target battery within a preset time period through the iterative algorithm in the embodiment constructed by the subsequent target monotonic function, and obtain the values ​​of at least two component parameters included in the equivalent circuit model of the target battery.

[0084] When the equivalent circuit model of the target battery includes at least two component parameters, the corresponding Figure 6 When the iterative algorithm in the embodiment solves and obtains the equivalent circuit model of the target battery, the following method can be used: Figure 2 Included steps to achieve. Figure 2 This is a flow chart of a battery state of charge detection method provided in an embodiment of the present application. Figure 2 As shown, the method may include:

[0085] S201 : Obtain a current target battery temperature, a current target battery current, and a current target battery voltage of a target battery within a preset time period.

[0086] The preset duration is based on the subsequent Figure 6 For example, the subsequent Figure 6 When the iterative algorithm constructs a mapping relationship between a certain SOC (for example, the SOC at time 1) and at least two component parameters included in the corresponding equivalent circuit model, the component parameters are iteratively obtained based on the battery temperature, battery current, and actual battery voltage corresponding to time 1 to time 10. The preset time length is the time length between time 1 and time 10.

[0087] The current target battery temperature can be obtained through a temperature sensor pre-deployed on the target battery; or it can be obtained based on historical target battery temperature predictions; or it can be obtained based on the current ambient temperature, the temperature of the target battery at the previous moment, the heat generated by the target battery during operation, etc.

[0088] The current target battery current can be obtained by a current sensor pre-deployed in the circuit where the target battery is located, or can be calculated based on the internal resistance of the target battery and the current target battery voltage. The current target battery voltage can be obtained, for example, by a voltage sensor pre-deployed in the circuit where the target battery is located, or determined based on the OCV method.

[0089] S202 : Based on the equivalent circuit model of the target battery, the current target battery temperature, the current target battery current, and the current target battery voltage, obtain values ​​of at least two component parameters included in the equivalent circuit model.

[0090] In this step, you can refer to the following Figure 6 The only difference is that the current target battery temperature and current target battery current in this step are used instead of Figure 6 The method tests the battery temperature and battery current of the battery, and then replaces the current target battery voltage in this step Figure 6 The actual battery voltage in the method. The specific algorithm is Figure 6 The content is consistent with that in , so I will not repeat it here.

[0091] Next, we will introduce how to construct the target monotonic function.

[0092] The execution subject of the process of constructing the target monotonic function can be implemented by the execution subject of the aforementioned battery state of charge detection method, or it can be implemented by a separate execution subject. When implemented by a separate execution subject, the execution subject can be, for example, an electronic device with computing capabilities, such as a computer, tablet computer, server, smart phone and other electronic devices. Alternatively, the execution subject can be hardware with computing capabilities such as the chip of the above-mentioned electronic devices. When the execution subject is an electronic device, a computer program that executes the method of constructing the target monotonic function can be deployed on the electronic device, and by executing the computer program, a target monotonic function is constructed that characterizes the mapping relationship between the theoretical characteristics, SOC, and at least two component parameters of the equivalent circuit model of the test battery.

[0093] Figure 3 A flow chart of another battery state of charge detection method provided in an embodiment of the present application is shown as follows: Figure 3 As shown, the method may further include:

[0094] S301. Obtain a mapping relationship between at least two component parameters of an equivalent circuit model of the test battery under the theoretical characteristics and the SOC of the test battery according to actual battery voltages corresponding to respective SOCs of the test battery under the theoretical characteristics.

[0095] The theoretical characteristics of the test battery include the same content as the current theoretical characteristics of the target battery, for example, may include at least one of the battery temperature and the battery current.

[0096] In this step, the actual battery voltage of the test battery can be collected when the test battery is at different battery temperatures and / or different battery currents and when the test battery is at different SOCs (different SOCs correspond to different collection times, for example, SOC1 corresponds to time 1, SOC2 corresponds to time 2, etc.). The time corresponding to the collected actual battery voltage is recorded, for example, by adding a corresponding timestamp to each recorded actual battery voltage.

[0097] The equivalent circuit model of the test battery can be, for example, any one of the equivalent circuit models such as the Rint model, the Thevenin model, the PNGV model, the RC model, the Randles model, and the FOM model, which can be determined according to actual needs and is not limited in this application. The equivalent circuit model of the test battery should be the same as the equivalent circuit model of the target battery.

[0098] Taking the equivalent circuit model as the above Figure 1 Taking the corresponding second-order RC model as an example, the mathematical expression of the equivalent circuit model can include the following formulas (1)-(3):

[0099] (1)

[0100] (2)

[0101] (3)

[0102] in, is the battery current.

[0103] Based on the above formulas (1)-(3), the battery voltage of the test battery during discharge can be determined as Over time The changing relationship of is shown in formula (4):

[0104] (4)

[0105] Here, time t is the time difference from the preset initial time. For example, assuming that time 0 of the battery discharge state is used as the initial time, the time corresponding to the first battery voltage can be determined by subtracting the time at time 0 from the time of the first battery voltage (assuming time 0 is 10:01:10 and the time of the first battery voltage is 10:01:11, then time t is 1 second).

[0106] Based on the actual battery voltage of the test battery collected when the test battery is at different SOCs and the above formula (4), a mapping relationship between at least two component parameters of the equivalent circuit model of the test battery and the SOC can be obtained through a nonlinear minimization method (such as the Levenberg-Marquard algorithm). This mapping relationship is used to determine the values ​​of the at least two component parameters corresponding to the SOC based on different SOCs, and can be, for example, a mapping table or a mapping curve.

[0107] Among them, the power supply voltage It can be obtained by static OCV after the test battery has been left to stand (for example, after standing for 2 hours). When the battery voltage is within the voltage platform range, its voltage change is small and can be determined based on the power supply voltage. As a constant voltage source. When OCV changes significantly with SOC, the power supply voltage can be further It is also used as one of the at least two component parameters and is processed together using a nonlinear minimization method.

[0108] In one possible implementation, if the ambient temperature of the test battery is the same as the initial temperature of the test battery, it can be assumed that the heat generated during the test battery's charge and discharge processes has little impact on the test battery's battery temperature, and the battery temperature can be considered an unchanging constant. In this case, the mapping relationship between at least two component parameters of the test battery's equivalent circuit model and the SOC can be determined by collecting the test battery's actual battery voltage at different battery currents and at different SOCs, i.e., without considering the battery temperature factor.

[0109] S302 : Determine a target monotonic function according to a mapping relationship between at least two component parameters of an equivalent circuit model of the test battery and the SOC of the test battery under various theoretical characteristics.

[0110] The target monotonic function represents the mapping relationship between the theoretical characteristics, SOC, and at least two component parameters.

[0111] In this step, the mapping relationships between at least two component parameters and SOC can be fused to generate functions of the at least two component parameters varying with SOC. Furthermore, during the fusion process, the fusion coefficients of the component parameters can be controlled so that the functions of the at least two component parameters varying with SOC are monotonic, i.e., a target monotonic function is generated.

[0112] In this target monotonic function, the same SOC value only corresponds to the parameter value of a unique component parameter, so that the corresponding SOC value can be uniquely determined based on the parameter values ​​of at least two component parameters according to the target monotonic function, avoiding the generation of multiple solutions that make it impossible to determine which solution the predicted SOC value corresponds to.

[0113] Optionally, a monotonicity constraint can be introduced by performing a parametric fit (e.g., a polynomial or spline function) on the data to ensure that at least two component parameters are monotonic functions of the SOC. For example, the sign of the first-order derivative of the objective function can be constrained to be constant to ensure monotonicity.

[0114] Alternatively, the mapping relationship between each component parameter and SOC can be converted into a corresponding monotonic function, and then the mapping relationships between all component parameters and SOC in at least two component parameters are fused to generate a target monotonic function in which at least two component parameters change with SOC.

[0115] The method provided in the embodiment of the present application determines a target monotonic function representing the mapping relationship between theoretical characteristics, SOC, and at least two component parameters of the equivalent circuit model of the test battery based on the actual battery voltage corresponding to each SOC of the test battery when the test battery is under different theoretical characteristics, thereby providing a data basis for subsequent high-precision prediction of SOC through comprehensive multi-component parameter prediction.

[0116] The following describes in detail how to determine the target monotonic function based on the mapping relationship between at least two component parameters of the equivalent circuit model of the battery tested under theoretical characteristics and the SOC in the aforementioned step S302. Figure 4 A flow chart of another method for detecting the state of charge of a battery provided in an embodiment of the present application is shown as follows: Figure 4 As shown, the aforementioned step S302 may specifically include the following steps:

[0117] S401 : Determine a first function corresponding to each component parameter based on a mapping relationship between each component parameter and the SOC of an equivalent circuit model of a test battery under theoretical characteristics.

[0118] The first function represents the functional relationship between the parameters of each component and the SOC under various theoretical characteristics.

[0119] In this step, it is assumed that the mapping relationship between each component parameter and SOC is a mapping table, that is, for a component parameter, different SOC values ​​correspond to different values ​​of the component parameter. Based on this mapping table, a curve of the component parameter relative to SOC can be generated by interpolation, and a function of the curve, i.e., the first function, is determined based on this curve.

[0120] For example, Figure 5 A schematic diagram of a mapping relationship provided in an embodiment of the present application. Figure 5 As shown, the mapping relationship between the values ​​of each component parameter in the second-order RC model corresponding to the test battery and different SOC values ​​under a specific battery temperature and a specific battery current is shown. That is, the ohmic internal resistance , the first polarization resistance , the second polarization resistance , the first capacitor , the second capacitor The mapping relationship with SOC. Figure 5 The points in the figure are the values ​​of the component parameters at the corresponding SOC values.

[0121] Specifically, according to the mapping table, the curve of each component parameter relative to SOC can be generated by Hermite interpolation method, cubic spline interpolation method, etc. (ie Figure 5 ), and based on the curve, determine a function of the curve, that is, the first function.

[0122] S402: Generate a target monotonic function based on each first function under each theoretical characteristic.

[0123] In one possible implementation, each first function may be converted into a monotonic function, and then a target monotonic function may be generated by fusing the converted first functions.

[0124] Another possible implementation method is to first fuse the first functions, and then convert the fused function into a monotonic function to generate a target monotonic function. This implementation method can be specifically implemented through the following sub-steps:

[0125] S4021. Generate an initial function based on each first function.

[0126] The initial function represents the functional relationship between at least two component parameters and the SOC.

[0127] In this step, the method of S401 above can be used to obtain a first function corresponding to each of the at least two component parameters. Then, by fusing the first functions corresponding to all component parameters, a functional relationship between the at least two fused component parameters and the SOC is obtained, i.e., the initial function.

[0128] Optionally, for example, the first functions corresponding to all component parameters may be fused by weighted average fusion, linear fusion, nonlinear fusion, etc. to obtain the initial function.

[0129] For example, taking linear fusion as an example, assuming that there are 5 component parameters, the first functions corresponding to the component parameters are , , , , Then the initial function can be expressed as the following formula (5):

[0130] (5)

[0131] in, 、 、 、 、 is the coefficient of each first function, which can be determined according to actual needs or experiments.

[0132] S4022. Adjust the coefficients corresponding to the parameters of each component in the initial function to generate a target monotonic function under various theoretical characteristics.

[0133] The target monotonic function represents the correlation between at least two component parameters and the SOC.

[0134] One possible implementation method is to define a deviation function between the initial function and the monotonicity, use optimization algorithms such as gradient descent and Newton's method to adjust the coefficients corresponding to the parameters of each component in the initial function, and determine the coefficients corresponding to the parameters of each component in the final target monotonic function while minimizing the deviation function.

[0135] Another possible implementation method is to adjust the coefficients by using derivative function constraints to generate the target monotonic function. In this implementation method, for example, the derivative of the initial function with respect to SOC can be obtained. That is, for the initial function, the derivative with respect to SOC is taken to obtain the derivative function, which is shown in the following formula (6):

[0136] (6)

[0137] Then, by adjusting the values ​​of each coefficient to meet the preset adjustment target, that is, when the SOC value is within the charge-discharge range (for example, 0%-100%), adjust the value of each coefficient so that the derivative function is greater than or less than 0. At this time, the initial function F can be made to remain monotonically increasing (the derivative function is always greater than 0) or monotonically decreasing (the derivative function is always less than 0). The value of each coefficient at this time is the target value of each coefficient. Finally, the target value of each coefficient is substituted into the initial function to generate the target monotonic function.

[0138] The method provided in the embodiment of the present application determines the first function corresponding to each component parameter according to the mapping relationship between each component parameter and SOC in at least two component parameters, generates an initial function representing the functional relationship between at least two component parameters and SOC according to each first function, and finally adjusts the coefficients corresponding to each component parameter in the initial function to generate a target monotonic function, so that the corresponding SOC value can be uniquely determined based on the parameter value of each component parameter according to the target monotonic function, avoiding the generation of multiple solutions that make it impossible to determine which solution the predicted SOC value corresponds to, thereby realizing the function of the target monotonic function to accurately detect the SOC through the component parameters.

[0139] The following describes in detail how, in step S301, the mapping relationship between at least two component parameters of the equivalent circuit model of the test battery under theoretical characteristics and the SOC is obtained based on the actual battery voltage corresponding to each SOC of the test battery when the test battery is under theoretical characteristics.

[0140] Figure 6 A flow chart of another method for detecting the state of charge of a battery provided in an embodiment of the present application is shown as follows: Figure 6 As shown, the aforementioned step S301 may specifically include the following steps:

[0141] S601 , obtaining a set of error equations based on the changing relationships between at least two component parameters and the battery voltage corresponding to multiple time periods when the test battery is under various theoretical characteristics, and the actual battery voltage.

[0142] Taking the equivalent circuit model of the test battery as a second-order RC model as an example, the variation relationship between the component parameters included in the at least two component parameters and the battery voltage is the formula (4) mentioned in the aforementioned embodiment. If the equivalent circuit model of the test battery is another model, the variation relationship corresponding to the other model can be selected. The specific details can be referred to in the prior art and are not detailed here.

[0143] Because this variation relationship characterizes the relationship between the component parameters included in the at least two component parameters and the battery voltage, that is, the corresponding battery voltage can be obtained by using the component parameters included in the at least two component parameters and this variation relationship. Furthermore, since there are multiple component parameters, that is, multiple unknown quantities, the nonlinear least squares problem can be solved using a nonlinear minimization method. For example, an error equation can be constructed based on the actual battery voltage corresponding to a time and the above variation relationship. Multiple error equations constructed at multiple times can be used to obtain a corresponding set of error equations.

[0144] For example, taking the actual battery voltage collected at m different moments as an example, m error equations can be generated based on the actual battery voltage at the m different moments and the above-mentioned change relationship to form an error equation group. The error equation group is shown below:

[0145] (7)

[0146] in, Refers to the difference between the actual battery voltage and the corresponding change relationship.

[0147] S602 : Obtain at least two target component parameters corresponding to the actual battery voltage according to the error equation group.

[0148] In this step, for ease of understanding, the above example is used as an example.

[0149] According to the error equation group, the error function corresponding to the error equation group can be obtained. The error function can be, for example, a function that represents the sum of squares of the errors of the error equation group, or can also be generated based on the error, the standard deviation of the error, etc., which is not limited in this application. Taking the sum of squares as an example, , then the error function can be expressed as the following formula (8):

[0150] (8)

[0151] By setting The initial value is used to iterate and minimize the error function (i.e., minimize the error). When the iteration meets the preset convergence condition, the iterative result obtained at this time can be converted into The values ​​of the corresponding component parameters in are used as the values ​​of the target component parameters. Based on the values ​​of the target component parameters, at least two target component parameters corresponding to the actual battery voltage can be obtained.

[0152] S603 : Determine a mapping relationship between at least two component parameters and the SOC of the test battery according to the SOC corresponding to the actual battery voltage and at least two target component parameters.

[0153] The at least two target component parameters obtained through the aforementioned steps, wherein the values ​​of the target component parameters represent the values ​​of the component parameters in the equivalent circuit model of the battery when the battery voltage is the actual battery voltage.

[0154] Therefore, the mapping relationship between at least two component parameters and the SOC can be determined based on the corresponding relationship between the actual battery voltage and the at least two target component parameters, and the corresponding relationship between the actual battery voltage and the corresponding SOC.

[0155] Next, for the specific acquisition of the above The initial value of is described in detail. Figure 7 A flow chart of another method for detecting the state of charge of a battery provided in an embodiment of the present application is shown as follows: Figure 7 As shown, continuing to take the equivalent circuit model of the battery as a second-order RC model as an example, the at least two component parameters include at least one of ohmic internal resistance, polarization resistance (two polarization resistances in the second-order RC model), and capacitance (two capacitances in the second-order RC model) (and continuing to ensure that the component parameters are at least two, for example, two polarization resistances can be included). Correspondingly, the initial value includes at least one of the initial value of the ohmic internal resistance, the initial value of the polarization resistance, and the initial value of the capacitance. In this case, taking the example of at least two component parameters including ohmic internal resistance, polarization resistance, and capacitance, obtaining the initial value can be specifically achieved by the following steps:

[0156] S701 : Determine an initial value of the ohmic internal resistance according to a transient voltage change of the test battery at the start of discharge.

[0157] In this step, since the battery voltage is 0 + to 0 - changes (i.e. arrive The change of ohmic resistance is mainly due to Therefore, the open circuit voltage (i.e., the power supply voltage) of the test battery can be measured after the test battery has been left to stand for a long enough time (e.g., 2 hours). Then, a known discharge current is suddenly applied at the start of discharge, and the transient voltage change at that moment is recorded. This transient voltage change is due to the ohmic resistance Therefore, according to Ohm's law, the following formula (9) can be obtained to calculate the ohmic internal resistance: Initial value of:

[0158] (9)

[0159] in, Equivalent to , which is the battery voltage at the moment of discharge.

[0160] S702 : Obtain an initial value of the polarization resistance according to a voltage value of the test battery when it is discharged to a steady state and a relationship between a change in at least two component parameters and the battery voltage.

[0161] In this step, when the discharge time in the equivalent circuit model reaches a certain length, for example, the discharge time is infinite, the capacitor is fully charged and the battery voltage Converges to a stable value. Therefore, based on the above formula (4), we can get the formula (10) in this case:

[0162] (10)

[0163] The polarization resistance can be obtained by solving the above formula (10): and The initial value of .

[0164] Optionally, for the convenience of calculation, when there are multiple polarization resistors in the equivalent circuit model, the initial values ​​of the multiple polarization resistors can be set to the same. For example, if there are two polarization resistors in the above example, the initial values ​​of the two polarization resistors can be set to the same to reduce the complexity of the model and improve the above Figure 6 The fitting speed and stability of the method. At this time, due to and Same, and known to obtain The initial value, battery current, power supply voltage, can be calculated based on the battery voltage The polarization resistance is calculated by converging to a stable value and The initial value of .

[0165] S703: Obtain an initial value of the capacitance according to the change relationship.

[0166] In this step, the exponential terms in the above formula (4) can be Taylor expanded, and the second-order and higher-order terms after Taylor expansion can be ignored to obtain the following formula (11):

[0167] (11)

[0168] The capacitance can be obtained by solving the above formula (10) and The initial value of .

[0169] Optionally, in order to facilitate calculation, when there are multiple capacitors in the equivalent circuit model, the initial values ​​of the multiple capacitors can be set to the same. For example, if there are two capacitors in the above example, the initial values ​​of the two capacitors can be set to the same to reduce the complexity of the model and improve the above Figure 6 The fitting speed and stability of the method. At this time, due to and Same, and known to obtain The initial value, battery current, power supply voltage, can be calculated based on the battery voltage exist The value at the moment is calculated to obtain the capacitance and The initial value of .

[0170] In one possible implementation, after determining the SOC of the target battery through the aforementioned embodiment, the SOC can be used to calibrate the initial SOC of the target battery. This initial SOC is the SOC parameter to be calibrated. For example, if the initial SOC and the determined SOC differ, the initial SOC value can be directly calibrated to the determined SOC of the target battery. Alternatively, the rate of change of the initial SOC can be changed so that after a preset period of time, the value of the initial SOC changes to the same as the current SOC of the target battery, thereby calibrating the initial SOC of the target battery and preventing SOC value jumps during the calibration.

[0171] For example, to prevent SOC value jumps during calibration, a correction parameter for the rate of change of the initial SOC can be determined based on the difference between the determined target battery SOC and the initial SOC. The rate of change of the initial SOC is then adjusted based on the correction parameter to gradually correct the initial SOC until the difference between the initial SOC and the target battery SOC is less than or equal to a preset difference threshold.

[0172] The correction parameter is used to correct the rate of change of the initial SOC. For example, during discharge, when the determined SOC of the target battery is greater than the initial SOC, the discharge rate indicating the initial SOC is too high, resulting in an inaccurate SOC. Therefore, a negative correction parameter can be determined to slow the rate of decrease of the initial SOC, thereby gradually correcting the initial SOC during discharge. After a preset period of time, the difference between the initial SOC value and the target battery's SOC value at that moment is less than or equal to a preset difference threshold, that is, the initial SOC value is closer to or the same as the target battery's SOC value at that moment, thereby gradually correcting the initial SOC and improving the accuracy of the initial SOC.

[0173] Specifically, for example, by establishing an association between the difference between the SOC of the target battery and the initial SOC and the correction parameter, the corresponding correction parameter can be determined using the difference. For example, a linear mapping method can be used, that is, a fixed coefficient is set, and the difference is multiplied by the coefficient to obtain the correction parameter. The positive or negative sign of the correction parameter is related to the charge and discharge state of the target battery and the relationship between the SOC of the target battery and the initial SOC. If the SOC of the target battery is greater than the initial SOC in the charging state, it indicates that the initial SOC change rate is slow and inaccurate, and the correction parameter can take a positive value; conversely, if the SOC of the target battery is less than the initial SOC, it indicates that the initial SOC change rate is fast and inaccurate, and the correction parameter can take a negative value. If in the discharging state, the positive and negative values ​​are opposite to those in the charging state.

[0174] The method provided in the embodiments of the present application determines a correction parameter for the rate of change of the initial SOC based on the difference between the determined target battery SOC and the initial SOC. The rate of change of the initial SOC is then adjusted based on the correction parameter to gradually correct the initial SOC until the difference between the initial SOC and the target battery SOC is less than or equal to a preset difference threshold. This reduces the problem of SOC value jumps caused by SOC corrections and improves the user experience.

[0175] Figure 8 This is a schematic diagram of the structure of a battery state of charge detection device provided in an embodiment of the present application. Figure 8 As shown, the battery state of charge detection device may include: a processing module 11. In a possible implementation, it may also include: an acquisition module 12.

[0176] The processing module 11 is used to determine the state of charge (SOC) of the target battery based on the current theoretical characteristics of the target battery, the values ​​of at least two component parameters included in the equivalent circuit model of the target battery, and a target monotonic function, wherein the target monotonic function is determined based on the actual battery voltage corresponding to each SOC of the test battery when the test battery is at each theoretical characteristic, and the test battery and the target battery are of the same type.

[0177] Optionally, the current theoretical characteristic includes at least one of a current target battery temperature and a current target battery current.

[0178] Optionally, the acquisition module 12 is configured to acquire values ​​of at least two component parameters included in the equivalent circuit model of the target battery at the current target battery temperature and the current target battery current.

[0179] Optionally, the acquisition module 12 is specifically configured to acquire a current target battery temperature, a current target battery current, and a current target battery voltage of the target battery within a preset time period. Based on an equivalent circuit model of the target battery and the current target battery temperature, the current target battery current, and the current target battery voltage, the module acquires values ​​of at least two component parameters included in the equivalent circuit model.

[0180] Optionally, the processing module 11 is further configured to calibrate the initial SOC according to the determined SOC of the target battery when the initial SOC of the target battery is different from the determined SOC of the target battery.

[0181] Optionally, the processing module 11 is specifically configured to determine a correction parameter for the rate of change of the initial SOC based on the difference between the determined target battery SOC and the initial SOC. The rate of change of the initial SOC is adjusted based on the correction parameter to gradually correct the initial SOC until the difference between the initial SOC and the target battery SOC is less than or equal to a preset difference threshold.

[0182] Optionally, the processing module 11 is further configured to obtain, based on actual battery voltages corresponding to respective states of charge (SOCs) of the test battery when the test battery is under the theoretical characteristics, a mapping relationship between at least two component parameters of an equivalent circuit model of the test battery under the theoretical characteristics and the SOC of the test battery. Based on the mapping relationship between the at least two component parameters of the equivalent circuit model of the test battery under the theoretical characteristics and the SOC of the test battery, a target monotonic function is determined, where the target monotonic function represents the mapping relationship between the theoretical characteristics, the SOC, and the at least two component parameters.

[0183] Optionally, the theoretical characteristic includes at least one of a test battery temperature and a test battery current.

[0184] Optionally, the processing module 11 is specifically configured to determine a first function corresponding to each component parameter based on a mapping relationship between each component parameter and the SOC of an equivalent circuit model of the battery under test under theoretical characteristics. A target monotonic function is generated based on each first function under each theoretical characteristic. The first function represents the functional relationship between each component parameter and the SOC under each theoretical characteristic.

[0185] Optionally, the processing module 11 is specifically configured to determine an initial function based on each first function, adjust coefficients corresponding to each component parameter in the initial function, and generate a target monotonic function under each theoretical characteristic.

[0186] Optionally, processing module 11 is specifically configured to determine a derivative of the initial function with respect to the SOC. Based on the derivative and a preset adjustment target, target values ​​for each coefficient that meet the preset adjustment target are determined. The target values ​​for each coefficient are substituted into the initial function to generate a target monotonic function. The preset adjustment target includes: the SOC value being within the charge / discharge range, and the derivative being constantly greater than 0 or constantly less than 0.

[0187] Optionally, the processing module 11 is specifically configured to obtain a set of error equations based on the changing relationship between at least two component parameters and the battery voltage corresponding to multiple times when the test battery is under various theoretical characteristics, as well as the actual battery voltage. Based on the set of error equations, at least two target component parameters corresponding to the actual battery voltage are obtained. Based on the SOC corresponding to the actual battery voltage and the at least two target component parameters, a mapping relationship between the at least two component parameters and the SOC of the test battery is determined.

[0188] Optionally, the processing module 11 is specifically configured to obtain initial values ​​of the error equation group, and obtain at least two target component parameters after iteratively updating the error equation group according to the initial values ​​until the error function of the error equation group satisfies a preset convergence condition.

[0189] Optionally, when the target component parameters include at least one of ohmic internal resistance, polarization resistance, and capacitance, the processing module 11 is specifically used to determine the initial value of the ohmic internal resistance based on the transient voltage change of the test battery at the start of discharge; and / or, based on the voltage value of the test battery when it is discharged to a steady state, and the changing relationship between at least two component parameters and the battery voltage, obtain the initial value of the polarization resistance; and / or, obtain the initial value of the capacitance based on the changing relationship.

[0190] Optionally, the changing relationship includes at least two capacitors, and the initial values ​​of the at least two capacitors are the same; and / or, the changing relationship includes at least two polarization resistors, and the initial values ​​of the at least two polarization resistors are the same.

[0191] Optionally, the equivalent circuit model includes at least one of the following: Rint model, Thevenin model, PNGV model, RC model, Randles model, and FOM model.

[0192] The battery state of charge detection device provided in the embodiment of the present application can execute the battery state of charge detection method in the above method embodiment. Its implementation principle and technical effects are similar and will not be repeated here.

[0193] An embodiment of the present application further provides a battery management system, which is used to execute any one of the battery state of charge detection methods in the aforementioned method embodiments.

[0194] An embodiment of the present application also provides a battery pack, which includes the aforementioned battery management system.

[0195] An embodiment of the present application further provides an electric device, wherein the battery pack of the electric device is a battery pack including the aforementioned battery management system.

[0196] An embodiment of the present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0197] An embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above-mentioned method is implemented.

[0198] The readable storage medium may be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0199] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0200] The division of units is merely a logical functional division; actual implementations may employ alternative divisions, such as combining or integrating multiple units or components into another system, or omitting or disabling certain features. Furthermore, any direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units, either through an interface, electrical, mechanical, or other means.

[0201] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0202] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0203] If a function is implemented as 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 technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0204] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0205] Finally, it should be noted that those skilled in the art will readily identify other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The present invention is not limited to the precise structure described above and illustrated in the accompanying drawings, and various modifications and variations may be made without departing from the scope thereof. The scope of the present invention is limited solely by the appended claims.

Claims

1. A method for detecting a battery state of charge, characterized in that: The method comprises: Obtaining, based on actual battery voltages corresponding to respective SOCs of the test battery when the test battery is under respective theoretical characteristics, a mapping relationship between at least two component parameters of an equivalent circuit model of the test battery and the SOC of the test battery under the respective theoretical characteristics, wherein the theoretical characteristics include at least one of a test battery temperature and a test battery current; Determining, based on a mapping relationship between each component parameter of the equivalent circuit model of the test battery and the SOC under each theoretical characteristic, a first function corresponding to each component parameter, wherein the first function represents a functional relationship between each component parameter and the SOC under each theoretical characteristic; generating a target monotonic function according to each of the first functions under each of the theoretical characteristics, wherein the target monotonic function represents a mapping relationship between the theoretical characteristics, the SOC, and at least two of the component parameters; The state of charge (SOC) of the target battery is determined based on the current theoretical characteristics of the target battery, the values ​​of at least two component parameters included in the equivalent circuit model of the target battery, and the target monotonic function. The target monotonic function is determined based on the actual battery voltage corresponding to each SOC of the test battery when the test battery is at each theoretical characteristic. The test battery and the target battery are of the same type. The current theoretical characteristics include at least one of the current target battery temperature and the current target battery current.

2. The method according to claim 1, characterized in that Also includes: Acquire values ​​of at least two component parameters included in an equivalent circuit model of the target battery at the current target battery temperature and the current target battery current.

3. The method according to claim 2, characterized in that The acquiring of values ​​of at least two component parameters included in the equivalent circuit model of the target battery at the current target battery temperature and the current target battery current includes: Obtaining a current target battery temperature, a current target battery current, and a current target battery voltage of the target battery within a preset time period; Based on the equivalent circuit model of the target battery, the current target battery temperature, the current target battery current, and the current target battery voltage, values ​​of at least two component parameters included in the equivalent circuit model are acquired.

4. The method according to claim 1, wherein Also includes: When the initial SOC of the target battery is different from the determined SOC of the target battery, the initial SOC is calibrated according to the determined SOC of the target battery.

5. The method according to claim 4, characterized in that The calibrating the initial SOC according to the determined SOC of the target battery includes: determining a correction parameter for a rate of change of the initial SOC according to the determined difference between the target battery SOC and the initial SOC; The change rate of the initial SOC is adjusted based on the correction parameter to gradually correct the initial SOC until the difference between the initial SOC and the SOC of the target battery is less than or equal to a preset difference threshold.

6. The method according to claim 1, characterized in that Generating the target monotonic function according to each of the first functions under each of the theoretical characteristics includes: determining an initial function according to each of the first functions; The coefficients corresponding to the component parameters in the initial function are adjusted to generate the target monotonic function under the theoretical characteristics.

7. The method according to claim 6, characterized in that The step of adjusting the coefficients corresponding to the component parameters in the initial function to generate the target monotonic function includes: determining a derivative of the initial function with respect to the SOC; Determining target values ​​of the coefficients that meet the preset adjustment target based on the derivative function and a preset adjustment target, wherein the preset adjustment target includes: the SOC value is within the charge and discharge range, and the derivative function is always greater than 0 or the derivative function is always less than 0; Substitute the target value of each coefficient into the initial function to generate the target monotonic function.

8. The method according to claim 1, characterized in that The obtaining, based on the actual battery voltage corresponding to each state of charge (SOC) of the test battery when the test battery is under each theoretical characteristic, a mapping relationship between at least two component parameters of the equivalent circuit model of the test battery under the theoretical characteristics and the SOC includes: Obtaining a set of error equations based on a changing relationship between at least two of the component parameters and the battery voltage corresponding to a plurality of time periods when the test battery is under each of the theoretical characteristics, and the actual battery voltage; Obtaining at least two target component parameters corresponding to the actual battery voltage according to the error equation group; According to the SOC corresponding to the actual battery voltage and the at least two target component parameters, a mapping relationship between the at least two component parameters and the SOC of the test battery is determined.

9. The method according to claim 8, characterized in that The obtaining, according to the error equation group, at least two target component parameters corresponding to the actual battery voltage includes: Obtaining initial values ​​of the error equation group; After the error equation group is iteratively updated according to the initial value until the error function of the error equation group meets a preset convergence condition, at least two target component parameters are obtained.

10. The method according to claim 9, characterized in that The target component parameter includes at least one of ohmic internal resistance, polarization resistance, and capacitance. The obtaining of the initial value of the error equation group includes: determining an initial value of the ohmic internal resistance based on a transient voltage change of the test battery at the start of discharge; and / or, Obtaining an initial value of the polarization resistance according to a voltage value of the test battery when it is discharged to a steady state and a changing relationship between at least two of the component parameters and the battery voltage of the test battery; and / or, An initial value of the capacitance is obtained according to the change relationship.

11. The method according to claim 10, characterized in that The change relationship includes at least two of the capacitors, and the initial values ​​of at least two of the capacitors are the same; and / or, The change relationship includes at least two polarization resistors, and the initial values ​​of at least two polarization resistors are the same.

12. The method according to any one of claims 1 to 11, characterized in that The equivalent circuit model includes at least one of the following: a Rint model, a Thevenin model, a PNGV model, an RC model, a Randles model, and a FOM model.

13. A battery state of charge detection device, characterized in that: The device comprises: A processing module is used to obtain a mapping relationship between at least two component parameters of the equivalent circuit model of the test battery and the SOC of the test battery under each theoretical characteristic according to the actual battery voltage corresponding to each SOC of the test battery when the test battery is in each theoretical characteristic, wherein the theoretical characteristic includes at least one of the test battery temperature and the test battery current; according to the mapping relationship between each component parameter of the equivalent circuit model of the test battery under each theoretical characteristic and the SOC, determine a first function corresponding to each component parameter, wherein the first function represents the functional relationship between each component parameter and the SOC under each theoretical characteristic; according to the mapping relationship between each component parameter of the equivalent circuit model of the test battery under each theoretical characteristic and the SOC Each of the first functions generates a target monotonic function, which characterizes the mapping relationship between the theoretical characteristics, the SOC, and at least two of the component parameters; the state of charge (SOC) of the target battery is determined according to the current theoretical characteristics of the target battery, the values ​​of at least two component parameters included in the equivalent circuit model of the target battery, and the target monotonic function, wherein the target monotonic function is determined according to the actual battery voltage corresponding to each SOC of the test battery when the test battery is in each theoretical characteristic, the test battery and the target battery are of the same type, and the current theoretical characteristics include at least one of the current target battery temperature and the current target battery current.

14. A battery management system, characterized in that: The battery management system is used to execute the battery state of charge detection method according to any one of claims 1 to 12.

15. A battery pack, characterized in that: The battery pack includes the battery management system according to claim 14.

16. An electrical device, characterized in that: The battery pack of the electrical equipment is the battery pack according to claim 15.

17. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 12 when executed by a processor.

18. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 12 when executed by a processor.

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