Battery cell charge state determination method and device, storage medium and electronic equipment

Abstract

The invention belongs to the technical field of batteries, and particularly relates to a method and device for determining the state of charge of a battery cell, a computer readable storage medium and electronic equipment. The method comprises the following steps: acquiring operation data of a battery cell, the operation data of the battery cell at least comprising voltage and current; performing parameter identification on a target equivalent circuit model based on the operation data of the battery cell, and constructing a state space model of the battery cell in a current operation state according to a parameter identification result; wherein the target equivalent circuit model is constructed on the basis of a basic equivalent circuit model; based on the operation data of the battery cell and the state space model, performing joint estimation on the correction parameter of the polarization voltage and the charge state of the battery cell to obtain the charge state of the battery cell; wherein the correction parameter of the polarization voltage is used for correcting the polarization voltage in the basic equivalent circuit model to obtain the target polarization voltage of the state space model in different operation states.

Description

Technical Field

[0001] This application belongs to the field of battery technology, and in particular relates to a method, apparatus, computer-readable storage medium and electronic device for determining the state of charge of a battery cell. Background Technology

[0002] Accurate State of Charge (SOC) estimation is crucial for battery management and use, improving battery efficiency, lifespan, and safety. However, SOC cannot be directly measured; factors such as operating temperature, charge / discharge current, cycle count, and self-discharge all affect its accuracy, making SOC estimation difficult.

[0003] In existing technologies, SOC estimation can be performed using methods such as ampere-hour integration, open-circuit voltage method, internal resistance method, discharge experiment method, neural network method, and Kalman filtering method. Each method has its own advantages, disadvantages, and applicable scenarios. However, in practical applications, when the battery is in a scenario where the operating state (such as temperature) changes significantly, the actual capacity of the battery will change due to the change in the operating state. The uncertainty of the capacity will ultimately lead to inaccurate SOC estimation results. Summary of the Invention

[0004] In view of this, embodiments of this application provide a method, apparatus, computer-readable storage medium, and electronic device for determining the state of charge (SOC) of a battery cell, in order to solve the problem of inaccurate SOC estimation results in scenarios with large changes in operating state in the prior art.

[0005] The first aspect of this application provides a method for determining the state of charge of a battery cell, which may include:

[0006] Acquire the operating data of the battery cell, which includes at least voltage and current;

[0007] Based on the cell's operating data, the parameters of the target equivalent circuit model are identified, and a state space model of the cell in its current operating state is constructed based on the parameter identification results; the target equivalent circuit model is constructed on the basis of the basic equivalent circuit model.

[0008] Based on the cell's operating data and state-space model, the correction parameters of the polarization voltage and the cell's state of charge are jointly estimated to obtain the cell's state of charge.

[0009] Among them, the polarization voltage correction parameter is used to correct the polarization voltage in the basic equivalent circuit model to obtain the target polarization voltage of the state space model under different operating states; the target polarization voltage is used to estimate the polarization voltage correction parameter and the state of charge of the cell.

[0010] Using the above method, the polarization voltage in the basic equivalent circuit model can be corrected to obtain a target polarization voltage that can adapt to changes in operating state. Based on this, parameter identification and state estimation can be performed, which has strong adaptability even in scenarios with large changes in operating state, and can obtain more accurate state of charge estimation results.

[0011] In one specific implementation of the first aspect, based on the cell's operating data and state-space model, the correction parameters of the polarization voltage and the cell's state of charge are jointly estimated to obtain the cell's state of charge, which may include:

[0012] Output prediction is performed based on the state-space model and the target polarization voltage at the previous moment to obtain the output prediction data at the current moment.

[0013] Determine the prediction residual between the cell's operating data and the current output prediction data;

[0014] Based on the prediction residual, the correction parameters of the polarization voltage at the current moment and the state of charge of the cell at the current moment are estimated to obtain the correction parameters of the polarization voltage at the current moment and the state of charge of the cell at the current moment.

[0015] Using the above method, output prediction and residual calculation are performed based on the target polarization voltage that can adapt to changes in operating state. The resulting prediction residual can also adapt to changes in operating state. Based on this prediction residual, state estimation can be performed to obtain more accurate estimation results.

[0016] In one specific implementation of the first aspect, the correction parameters of the polarization voltage at the current moment and the state of charge of the battery cell at the current moment are estimated based on the prediction residual to obtain the correction parameters of the polarization voltage at the current moment and the state of charge of the battery cell at the current moment, which may include:

[0017] The gain value of the correction parameter for polarization voltage and the gain value of the state of charge of the cell are determined based on the predicted residual.

[0018] Based on the cell's operating data, the correction parameters of the polarization voltage at the previous moment, and the cell's state of charge at the previous moment, the correction parameters of the polarization voltage at the current moment and the cell's state of charge at the current moment are predicted to obtain the predicted values ​​of the correction parameters of the polarization voltage at the current moment and the predicted values ​​of the cell's state of charge at the current moment.

[0019] Based on the predicted value of the polarization voltage correction parameter and the gain value of the polarization voltage correction parameter at the current moment, determine the polarization voltage correction parameter at the current moment.

[0020] The current state of charge (SOC) of the battery cell is determined based on the predicted SOC and the gain value of the SOC.

[0021] Using the above method, the gain value is calculated based on the prediction residual that can adapt to changes in operating state. The obtained gain value can also adapt to changes in operating state. Based on this gain value, state estimation can be performed to obtain more accurate estimation results.

[0022] In one specific implementation of the first aspect, the process of constructing the target equivalent circuit model may include:

[0023] Determine the basic equivalent circuit model;

[0024] Based on a pre-defined polarization correction sub-model, the polarization voltage in the basic equivalent circuit model is corrected to obtain the target polarization voltage; wherein, the polarization correction sub-model is used to characterize the mapping relationship between the polarization voltage and the target polarization voltage.

[0025] Based on the basic equivalent circuit model, the polarization voltage in the basic equivalent circuit model is corrected to the target polarization voltage in order to construct the target equivalent circuit model.

[0026] Using the above method, a target equivalent circuit model can be constructed based on the basic equivalent circuit model. Since the polarization voltage is modified to a target polarization voltage that can adapt to changes in operating state, the constructed target equivalent circuit model has strong adaptability even in scenarios with large changes in operating state. Based on this target equivalent circuit model, state estimation can be performed to obtain more accurate estimation results.

[0027] In one specific implementation of the first aspect, the correction parameter for the polarization voltage may include: a linear correction parameter, a quadratic transformation correction parameter, or a higher-order transformation correction parameter.

[0028] The above method allows for more flexible and diverse settings of polarization voltage correction parameters, enabling the selection of appropriate correction parameters based on actual conditions. This allows for more flexible control over the complexity and accuracy of the calculations, resulting in wider applicability.

[0029] In one specific implementation of the first aspect, when the correction parameter for the polarization voltage is a linear correction parameter, the polarization voltage correction process may include:

[0030] Based on the linear correction parameters, the polarization voltage is linearly transformed and corrected to obtain the target corrected voltage.

[0031] In one specific implementation of the first aspect, the polarization voltage is linearly transformed and corrected based on the linear correction parameter to obtain the target corrected voltage, which may include:

[0032] Based on the proportional parameter in the linear correction parameter, the polarization voltage is proportionally transformed to obtain the proportionally corrected voltage.

[0033] Based on the bias parameter in the linear correction parameter, the proportional correction voltage is superimposed with a bias to obtain the target correction voltage.

[0034] The above method can be used to correct polarization voltage based on linear correction parameters. Since the linear correction parameters only involve relatively simple parameters such as proportional parameters and bias parameters, they do not add too much computational complexity and can be better suited for application scenarios with limited computing and storage resources.

[0035] In one specific implementation of the first aspect, parameter identification of the target equivalent circuit model based on the cell's operating data may include:

[0036] The terminal voltage error, the rate of change of terminal voltage error, and the rate of change of current of the battery cell are determined based on the cell's operating data.

[0037] The forgetting factor is obtained by performing online fuzzy inference on the terminal voltage error, the rate of change of terminal voltage error, and the rate of change of current using a fuzzy controller.

[0038] The recursive least squares algorithm with a forgetting factor is used to perform online parameter identification of the target equivalent circuit model, and the model parameters of the target equivalent circuit model are obtained.

[0039] Based on the model parameters of the target equivalent circuit model, the parameters of the state-space model are determined.

[0040] Using the above method, a recursive least squares algorithm with a forgetting factor can be used for parameter identification. The forgetting factor is dynamically updated through a fuzzy controller, which can improve the adaptability of the forgetting factor under changes in the running state, thereby improving the accuracy and robustness of the model parameter estimation results.

[0041] In one specific implementation of the first aspect, the parameter identification process and the state estimation process run iteratively to each other, with the result of the parameter identification process serving as the input to the state estimation process, and the result of the state estimation process serving as the input to the parameter identification process.

[0042] Using the above method, the parameter identification process and the state estimation process can be treated as an organic whole, with each serving as an input to the other and iterating continuously to obtain more accurate estimation results.

[0043] A second aspect of this application provides a method for determining the state of charge of a battery, which may include:

[0044] Based on any of the above-mentioned methods for determining the state of charge in the first aspect, the state of charge of each cell in the battery is determined respectively.

[0045] The state of charge (SOC) of the battery is determined based on the SOC of each individual cell.

[0046] Using the above method, the state of charge (SOC) of each cell can be determined based on the first aspect of the method for determining the SOC of the cell, resulting in a more accurate SOC of each cell. Based on this, the SOC of the battery can be determined, resulting in a more accurate SOC of the battery.

[0047] In one specific implementation of the second aspect, determining the state of charge (SOC) of the battery based on the SOC of each individual cell may include:

[0048] Select the maximum and minimum state of charge values ​​from the states of charge of each cell;

[0049] The state of charge (SOC) of the battery is determined based on the maximum and minimum SOC values.

[0050] Using the above method, the battery's state of charge can be determined solely based on the maximum and minimum values ​​of the state of charge, effectively reducing the consumption of computing and storage resources.

[0051] A third aspect of this application provides a device for determining the state of charge of a battery cell, which may include:

[0052] The acquisition module is used to acquire the operating data of the battery cell, wherein the operating data of the battery cell includes at least voltage and current;

[0053] The parameter identification module is used to identify the parameters of the target equivalent circuit model based on the cell's operating data, and to construct the state space model of the cell in its current operating state based on the parameter identification results; wherein, the target equivalent circuit model is constructed on the basis of the basic equivalent circuit model;

[0054] The joint estimation module is used to jointly estimate the polarization voltage correction parameters and the state of charge of the battery cell based on the cell's operating data and state-space model, thereby obtaining the cell's state of charge. Specifically, the polarization voltage correction parameters are used to correct the polarization voltage in the basic equivalent circuit model to obtain the target polarization voltage of the state-space model under different operating states. The target polarization voltage is used to estimate the polarization voltage correction parameters and the cell's state of charge.

[0055] The aforementioned device can correct the polarization voltage in the basic equivalent circuit model, thereby obtaining a target polarization voltage that can adapt to changes in operating state. Based on this, parameter identification and state estimation can be performed, which has strong adaptability even in scenarios with large changes in operating state, and can obtain more accurate state of charge estimation results.

[0056] In one specific implementation of the third aspect, the joint estimation module may include:

[0057] The output prediction submodule is used to predict the output based on the state-space model and the target polarization voltage at the previous moment, and obtain the output prediction data at the current moment.

[0058] The prediction residual determination submodule is used to determine the prediction residual between the cell's operating data and the current output prediction data;

[0059] The state estimation submodule is used to estimate the correction parameters of the polarization voltage at the current moment and the state of charge of the cell at the current moment based on the prediction residual, so as to obtain the correction parameters of the polarization voltage at the current moment and the state of charge of the cell at the current moment.

[0060] Using the above-mentioned device, output prediction and residual calculation are performed based on the target polarization voltage that can adapt to changes in operating state. The obtained prediction residual can also adapt to changes in operating state. Based on the prediction residual, state estimation can be performed to obtain more accurate estimation results.

[0061] In one specific implementation of the third aspect, the state estimation submodule may include:

[0062] Gain value determination unit, used to determine the gain value of the correction parameter of polarization voltage and the gain value of the state of charge of the cell based on the prediction residual;

[0063] The state prediction unit is used to predict the polarization voltage correction parameters and the state of charge of the battery cell at the current moment based on the battery cell's operating data, the polarization voltage correction parameters at the previous moment, and the battery cell's state of charge at the previous moment, so as to obtain the predicted values ​​of the polarization voltage correction parameters and the battery cell's state of charge at the current moment.

[0064] The state estimation unit is used to determine the correction parameters of the polarization voltage at the current moment based on the predicted value of the correction parameters of the polarization voltage at the current moment and the gain value of the correction parameters of the polarization voltage at the current moment; and to determine the state of charge of the cell at the current moment based on the predicted value of the state of charge of the cell at the current moment and the gain value of the state of charge of the cell at the current moment.

[0065] Using the aforementioned device, the gain value is calculated based on the prediction residual that can adapt to changes in operating state. The obtained gain value can also adapt to changes in operating state. Based on this gain value, state estimation can be performed to obtain more accurate estimation results.

[0066] In one specific implementation of the third aspect, the state of charge determination device may further include:

[0067] The basic equivalent circuit model determination module is used to determine the basic equivalent circuit model;

[0068] The polarization voltage correction module is used to correct the polarization voltage in the basic equivalent circuit model based on a preset polarization correction sub-model to obtain the target polarization voltage; wherein, the polarization correction sub-model is used to characterize the mapping relationship between the polarization voltage and the target polarization voltage.

[0069] The target equivalent circuit model construction module is used to modify the polarization voltage in the basic equivalent circuit model to the target polarization voltage in order to construct the target equivalent circuit model.

[0070] Using the above-mentioned device, a target equivalent circuit model can be constructed based on the basic equivalent circuit model. Since the polarization voltage is modified to a target polarization voltage that can adapt to changes in operating state, the constructed target equivalent circuit model has strong adaptability even in scenarios with large changes in operating state. Based on this target equivalent circuit model, state estimation can be performed to obtain more accurate estimation results.

[0071] In one specific implementation of the third aspect, the correction parameter for the polarization voltage may include: a linear correction parameter, a quadratic transformation correction parameter, or a higher-order transformation correction parameter.

[0072] With the above-mentioned device, the setting of the polarization voltage correction parameters is more flexible and diverse, so that appropriate correction parameters can be selected according to the actual situation, and the complexity and accuracy of the calculation can be more flexibly controlled, thus having a wider range of applicability.

[0073] In one specific implementation of the third aspect, when the polarization voltage correction parameter is a linear correction parameter, the polarization voltage correction module may include:

[0074] The linear correction submodule is used to perform linear transformation correction on the polarization voltage based on the linear correction parameters to obtain the target corrected voltage.

[0075] In one specific implementation of the third aspect, the linear correction submodule may include:

[0076] The proportional transformation unit is used to proportionally transform the polarization voltage based on the proportional parameter in the linear correction parameter to obtain the proportional correction voltage.

[0077] The bias superposition unit is used to superimpose the bias parameters in the linear correction parameters to obtain the target correction voltage by bias superposition of the proportional correction voltage.

[0078] The above-mentioned device can be used to correct polarization voltage based on linear correction parameters. Since the linear correction parameters only involve relatively simple parameters such as proportional parameters and bias parameters, they do not add too much computational complexity and can be better suited for application scenarios with limited computing and storage resources.

[0079] In one specific implementation of the third aspect, the parameter identification module can be used to: determine the terminal voltage error, the rate of change of terminal voltage error, and the rate of change of current of the battery cell based on the cell's operating data; perform online fuzzy inference on the terminal voltage error, the rate of change of terminal voltage error, and the rate of change of current using a fuzzy controller to obtain a forgetting factor; use a recursive least squares algorithm carrying the forgetting factor to perform online parameter identification on the target equivalent circuit model to obtain the model parameters of the target equivalent circuit model; and determine the parameters of the state space model based on the model parameters of the target equivalent circuit model.

[0080] With the above-mentioned device, a recursive least squares algorithm carrying a forgetting factor can be used for parameter identification. The forgetting factor is dynamically updated through a fuzzy controller, which can improve the adaptability of the forgetting factor under changes in the running state, thereby improving the accuracy and robustness of the model parameter estimation results.

[0081] In one specific implementation of the third aspect, the parameter identification module and the joint estimation module run iteratively to each other, with the result of the parameter identification submodule serving as the input to the state estimation submodule, and the result of the state estimation submodule serving as the input to the parameter identification submodule.

[0082] The above-mentioned device allows the parameter identification process and the state estimation process to be treated as an organic whole, with each serving as an input to the other and iterating continuously to obtain more accurate estimation results.

[0083] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of any of the above-described methods for determining the state of charge.

[0084] A fifth aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above-described methods for determining the state of charge.

[0085] A sixth aspect of this application provides a computer program product that, when run on an electronic device, causes the electronic device to execute the steps of any of the above-described methods for determining the state of charge. Attached Figure Description

[0086] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0087] Figure 1 A schematic flowchart illustrating the process of constructing the target equivalent circuit model;

[0088] Figure 2 This is a schematic diagram of a first-order RC equivalent circuit model;

[0089] Figure 3 A schematic diagram of the target equivalent circuit model;

[0090] Figure 4 This is a flowchart of one embodiment of a method for determining the state of charge of a battery cell according to the present application.

[0091] Figure 5 This is a schematic diagram illustrating the adaptive adjustment of the forgetting factor using a fuzzy controller.

[0092] Figure 6 A schematic diagram of the membership functions of the input and output variables of a fuzzy controller;

[0093] Figure 7 Example graph showing model parameters calibrated offline;

[0094] Figure 8 A schematic diagram of the overall framework for parameter identification and state estimation;

[0095] Figure 9 This is a flowchart of one embodiment of a method for determining the state of charge of a battery according to the present application.

[0096] Figure 10 A schematic diagram of a typical operating condition for testing;

[0097] Figure 11 A schematic diagram of comparative test results for methods of determining the state of charge;

[0098] Figure 12 This is a structural diagram of one embodiment of a state of charge determination device according to the present application.

[0099] Figure 13 This is a schematic block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0100] To make the inventive objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0101] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0102] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0103] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0104] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [the described condition or event] is detected," or "in response to detection of [the described condition or event]."

[0105] Furthermore, in the description of this application, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0106] Accurate State of Charge (SOC) estimation is crucial for battery management and use, improving battery efficiency, lifespan, and safety. However, SOC cannot be directly measured; factors such as operating temperature, charge / discharge current, cycle count, and self-discharge all affect its accuracy, making SOC estimation difficult.

[0107] In existing technologies, SOC estimation can be performed using methods such as ampere-hour integration, open-circuit voltage method, internal resistance method, discharge experiment method, neural network method, and Kalman filter method. Each method has its own advantages, disadvantages, and applicable scenarios. However, in practical applications, when the battery operating conditions (such as temperature) change significantly, the actual capacity of the battery will change due to the change in operating conditions. The uncertainty of the capacity will ultimately lead to inaccurate SOC estimation results.

[0108] In view of this, embodiments of this application provide a method, apparatus, computer-readable storage medium, and electronic device for determining the state of charge (SOC) of a battery cell, in order to solve the problem of inaccurate SOC estimation results in scenarios with large changes in operating state in the prior art.

[0109] In the embodiments of this application, parameter identification and state estimation can be performed based on the target equivalent circuit model. The target polarization voltage in the target equivalent circuit model is related to the operating state and can be adaptively adjusted as the operating state changes. It has strong adaptability even in scenarios with large changes in operating state and can obtain more accurate state of charge estimation results.

[0110] Please see Figure 1 The process of constructing the target equivalent circuit model in the embodiments of this application may include:

[0111] Step S101: Determine the basic equivalent circuit model.

[0112] To reduce computational complexity, the basic equivalent circuit model can be a relatively simple equivalent circuit model, which may include, but is not limited to, a first-order RC equivalent circuit model, a second-order equivalent circuit model, or a higher-order equivalent circuit model.

[0113] Figure 2 The diagram shown is a schematic of a first-order RC equivalent circuit model, where U ocv U is the open circuit voltage (OCV). t R0 is the terminal voltage across the battery cell, R1 is the internal resistance in ohms, R1 is the polarization resistor, and C1 is the polarization capacitor. The polarization resistor and polarization capacitor are connected in parallel to form an RC network structure, which is then connected in series with the internal resistance in ohms.

[0114] Based on the first-order RC equivalent circuit model, the dynamic behavior of the battery cell can be described as follows:

[0115]

[0116] U t,k =U ocv (z k )-U 1,k -R 0,k Ik

[0117] Among them, z k Let z be the state of charge of the cell at time k. k+1 Let I be the state of charge of the battery cell at time k+1, Δt be the sampling interval, and I be the state of charge of the battery cell at time k+1. k Let Q be the current at time k (positive for discharging, negative for charging). cell R represents the capacity of the battery cell. 0,k R 1,k and C 1,k The ohmic internal resistance, polarization resistance, and polarization capacitance at time k are respectively, U 1,k U is the polarization voltage at time k, which is also the voltage across the RC network structure. ocv (·) represents the OCV-SOC relationship model, used to characterize the relationship between the open-circuit voltage and the state of charge of a battery cell, U t,k Let k be the terminal voltage across the battery cell at time k.

[0118] The discrete state-space model of the first-order RC equivalent circuit is shown below:

[0119] x k =Ax k-1 +Bu k-1 +ω k-1

[0120] y k =Cx k +Du k +v k

[0121]

[0122] B = [-Δt / Q] cell ,(1-α)R1] T

[0123]

[0124] D = [-R0]

[0125] x k =[z k U 1,k ] T

[0126] u k =[I k ]

[0127] y k =[U t,k ]

[0128]

[0129] Among them, α=exp(-Δt / R1C1), ω k Assuming that the process noise follows a Gaussian distribution, Q k Its covariance matrix is ​​υ, where 0 represents a row vector of all zeros. k To observe the noise, we assume it follows a Gaussian distribution, R k Let its covariance be.

[0130] Step S102: Based on the preset polarization correction sub-model, the polarization voltage in the basic equivalent circuit model is corrected to obtain the target polarization voltage.

[0131] The polarization correction sub-model is used to characterize the mapping relationship between the polarization voltage and the target polarization voltage, as shown below:

[0132]

[0133] Among them, f corr (·) represents the polarization correction sub-model, θ k This is the parameter set of the polarization correction sub-model at time k, which is also the correction parameter of the polarization voltage. Let k be the target polarization voltage at time k.

[0134] The specific form of the polarization voltage correction parameters can be flexibly set according to the actual situation, and may include, but is not limited to, linear correction parameters, quadratic transformation correction parameters, or higher-order transformation correction parameters. In this way, the setting of polarization voltage correction parameters is more flexible and diverse, allowing for the selection of appropriate correction parameters according to the actual situation, and enabling more flexible control over the complexity and accuracy of the calculation, thus having wider applicability.

[0135] Taking the simplest linear correction parameter as an example, its correction expression is as follows:

[0136]

[0137] Where, θ k =[a k ,b k ] T a k As a proportional parameter, based on the proportional parameter, the polarization voltage can be proportionally transformed to obtain a proportionally corrected voltage, b. k The bias parameter is used to superimpose the proportional correction voltage to obtain the target polarization voltage.

[0138] Since the linear correction parameters only involve relatively simple parameters such as scaling parameters and bias parameters, they do not add much extra computational complexity, making them more suitable for application scenarios with limited computing and storage resources.

[0139] Similarly, when the correction parameter of the polarization voltage is a quadratic transformation correction parameter, the target polarization voltage can be expressed as a quadratic function of the polarization voltage. When the correction parameter of the polarization voltage is a higher-order transformation correction parameter, the target polarization voltage can be expressed as a higher-order function of the polarization voltage. This will not be elaborated further in the embodiments of this application.

[0140] Step S103: Based on the basic equivalent circuit model, replace the polarization voltage in the basic equivalent circuit model with the target polarization voltage to construct the target equivalent circuit model.

[0141] Taking the first-order RC equivalent circuit model as an example, Figure 3 The figure shows a schematic diagram of the corresponding target equivalent circuit model. As shown in the figure, the polarization voltage in the first-order RC equivalent circuit model is replaced with the target polarization voltage after being processed by the polarization correction sub-model.

[0142] Based on the target equivalent circuit model, the dynamic behavior of the battery cell can be described as follows:

[0143]

[0144] The discrete state-space model of the target equivalent circuit is shown below:

[0145] x k =Ax k-1 +Bu k-1 +ω k-1

[0146] y k =Cx k +Du k +v k

[0147]

[0148] B = [-Δt / Q] cell ,0] T

[0149]

[0150] D = [-R0]

[0151] x k =[z k ,θ k ] T

[0152] u k =[I k ]

[0153] y k =[Ut,k ]

[0154]

[0155] Where I is the identity matrix, 0 is a row vector of all zeros, and their dimensions are the same as the parameter set θ. k The dimensions are consistent.

[0156] When the correction parameter for the polarization voltage is a linear correction parameter, its discrete state-space model is as follows:

[0157] x k =Ax k-1 +Bu k-1 +ω k-1

[0158] y k =Cx k +Du k +v k

[0159]

[0160] B = [-Δt / Q] cell ,0,0] T

[0161]

[0162] D = [-R0]

[0163] x k =[z k ,a k ,b k ] T

[0164] u k =[I k ]

[0165] y k =[U t,k ]

[0166]

[0167] For cases where the correction parameters for polarization voltage are second-order or higher-order transformation correction parameters, similar model derivations can be performed, which will not be elaborated further in the embodiments of this application.

[0168] Through the above process, a target equivalent circuit model can be constructed based on the basic equivalent circuit model. Since the polarization voltage is modified to a target polarization voltage that can adapt to changes in operating state, the constructed target equivalent circuit model has strong adaptability even in scenarios with large changes in operating state. Based on this target equivalent circuit model, state estimation can be performed to obtain more accurate estimation results.

[0169] Based on the target equivalent circuit model constructed above, one embodiment of the method for determining the state of charge of a battery cell in this application may include, as follows: Figure 4 The process shown:

[0170] Step S401: Obtain the operating data of the battery cell.

[0171] The operating data of the battery cell includes at least voltage and current. In this embodiment, the operating data of the battery cell can be sampled at preset time intervals. The specific value of the time interval can be flexibly set according to the actual situation, and may include, but is not limited to, 1 second, 2 seconds, 3 seconds, and other values.

[0172] Step S402: Based on the cell's operating data, perform parameter identification on the target equivalent circuit model, and construct a state space model of the cell in its current operating state based on the parameter identification results.

[0173] The target equivalent circuit model is constructed based on the basic equivalent circuit model. The specific construction process can be found in the detailed description of steps S101 to S103 above, and will not be repeated here.

[0174] The specific algorithm for parameter identification can be flexibly set according to the actual situation, and may include, but is not limited to, recursive least squares algorithm and recursive least squares algorithm with forgetting factor.

[0175] Taking the recursive least squares algorithm as an example, we can let E k =U ocv (z k )-U t,k Then we have:

[0176]

[0177] Where, φ k =[E k-1 ,I k-1 ,I k ] is a data matrix, The parameter matrix can be specifically represented as:

[0178]

[0179] γ=R0

[0180] The model parameters can be calculated using the following formula:

[0181] R0=γ

[0182]

[0183] The recursive least squares algorithm can be used to identify the parameter matrix and then estimate the model parameters.

[0184] Considering that the model parameters will change due to changes in temperature and operating conditions during the operation of the battery cell, in a specific implementation of this application, a recursive least squares algorithm with a forgetting factor can be used for parameter identification. That is, a forgetting factor (denoted as λ) can be added to the recursive least squares algorithm to reduce the influence of historical data on the model parameters, increase the weight of new observations on the parameters, and avoid data saturation.

[0185] In parameter identification using a recursive least squares algorithm with a forgetting factor, the parameter matrix θ0, covariance matrix P0, and forgetting factor λ can be initialized first. At each time step, the following iterative calculations can be performed:

[0186] Calculate the corrected gain matrix:

[0187] Calculate the covariance matrix:

[0188] Update parameter matrix:

[0189] Inversely derived battery model parameters: R 0,k R 1,k C 1,k .

[0190] In recursive least squares algorithms with a forgetting factor, the magnitude of the forgetting factor directly affects the performance of parameter identification. A larger forgetting factor results in more stable parameter identification, but it cannot respond promptly to parameter changes, leading to poor real-time performance and the model not necessarily obtaining the optimal parameter solution. Conversely, a smaller forgetting factor results in greater fluctuations in parameter identification; while it can respond promptly to parameter changes, it may cause overfitting and divergence in the parameter identification results. A constant forgetting factor set empirically is often not optimal across wide operating temperature ranges or in complex operating conditions. Therefore, to address both stability and real-time performance issues, the forgetting factor needs to be appropriately valued.

[0191] In one specific implementation of the embodiments of this application, it can be done by, as follows Figure 5 The fuzzy controller shown comes from an adaptively adjusted forgetting factor.

[0192] The control variable of the fuzzy controller is the forgetting factor, while the state variables can be flexibly set according to actual conditions. Based on expert experience, when the model error is large, the forgetting factor should be reduced to quickly shrink the model error and enhance real-time performance; when the model error is small, the forgetting factor should be increased to maintain the current model state and enhance stability. Furthermore, the model parameters are actually closely related to the operating conditions and will change with these conditions. When the operating conditions change significantly (e.g., a sudden current change), the forgetting factor should be reduced to enhance real-time performance; when the operating conditions change slightly (the current change is small or even constant), the forgetting factor should be increased or no parameter update should be performed to enhance stability. Therefore, selectable state variables can include the estimated terminal voltage error (e) and the rate of change of current. In addition, to improve dynamic performance, the rate of change of terminal voltage error can also be adjusted. As a state variable.

[0193] The inference method of the fuzzy controller can be flexibly set according to the actual situation, and may include, but is not limited to, the Mamdani parallel fuzzy inference method; the fuzzification method of the fuzzy controller can be flexibly set according to the actual situation, and may include, but is not limited to, the fuzzy number method (isosceles triangle).

[0194] During the input-output scaling process, the specific level of the fuzzy universe of discourse can be flexibly set according to the actual situation. As an example, the terminal voltage error (e) and the rate of change of current can be used as parameters. and terminal voltage error change rate The fuzzy universe of discourse is set to a 5-level discrete fuzzy universe, i.e., {2,-1,0,1,2}, and the fuzzy universe of discourse for the forgetting factor (λ) is set to a 9-level discrete fuzzy universe, i.e., {-4,-3,-2,-1,0,1,2,3,4}. The actual input universe of discourse is discretized into 5 levels of discrete values, corresponding to the aforementioned 5-level discrete fuzzy universes. The actual output universe of discourse can be discretized into 9 levels of discrete values, corresponding to the aforementioned 9-level discrete fuzzy universes. Based on the setting of the fuzzy universe of discourse, a discrete quantization table of the corresponding input and output variables within their actual range of variation can be created.

[0195] The specific partitioning of the fuzzy spaces of the input and output universes can be flexibly set according to the actual situation. As an example, the universe of discourse for the input variable can be divided into 3 fuzzy spaces, i.e., taking linguistic values ​​of N, Z, and P. The universe of discourse for the output variable can be divided into 5 fuzzy spaces, i.e., taking linguistic values ​​of NB, NS, ZE, PS, and PB. Then, the membership function μ of the input and output variables can be defined as follows: Figure 6 The form shown is used where N, Z, and P represent negative, zero, and positive, respectively, and NB, NS, ZE, PS, and PB represent negative large, negative small, zero, positive small, and positive large, respectively.

[0196] The fuzzy control rules of the fuzzy controller can be flexibly set according to the actual situation. As an example, the fuzzy control rules can be set as follows:

[0197]

[0198] In the fuzzy control process, the quantized values ​​of the input variables can be calculated based on the actual input variables and the discrete quantization table of the input variables, and the membership values ​​of the input variables can be calculated based on the membership function. Then, according to the fuzzy control rules, a preset fuzzy inference method can be used for inference calculation, and the inference results can be declaratively processed to obtain the declarative output value. Then, the output scaling is performed based on the discrete quantization table of the output variables to calculate the actual control value, and after smoothing by a filter, the required forgetting factor is obtained.

[0199] Through the above process, the terminal voltage error, rate of change of terminal voltage error, and rate of change of current of the battery cell can be determined based on the cell's operating data. A fuzzy controller is then used to perform online fuzzy inference on these parameters to obtain the forgetting factor. After obtaining the forgetting factor, a recursive least squares algorithm carrying the forgetting factor can be used to identify the parameters of the target equivalent circuit model, obtaining its model parameters. Based on these parameters, the parameters of the state-space model are determined, thus constructing the state-space model of the battery cell under its current operating state. Since the forgetting factor is dynamically updated through the fuzzy controller, its adaptability to changes in operating state is improved, thereby enhancing the accuracy and robustness of the model parameter estimation results.

[0200] In one specific implementation of this application, the model parameters can also be subject to reasonable constraints, that is, the values ​​of the model parameters at different temperatures and SOCs can be calibrated offline. Figure 7 The image shows an example of offline calibrated model parameters. R0Max, R1Max, and C1Max represent the ohmic internal resistance, polarization resistance, and polarization capacitance corresponding to the maximum voltage within the battery, respectively. R0Min, R1Min, and C1Min represent the ohmic internal resistance, polarization resistance, and polarization capacitance corresponding to the minimum voltage within the battery, respectively. It is important to note that model parameters are affected by operating conditions, and offline calibration cannot cover all possible temperature, operating conditions, and SOC scenarios during operation. However, offline calibrated model parameters can still serve as reference values ​​for model parameters, allowing for the setting of reasonable ranges for parameter variation. This further accelerates algorithm convergence and enhances the reliability of parameter identification.

[0201] Step S403: Based on the cell's operating data and state-space model, the correction parameters of the polarization voltage and the cell's state of charge are jointly estimated to obtain the cell's state of charge.

[0202] The polarization voltage correction parameter is used to correct the polarization voltage in the basic equivalent circuit model, obtaining the target polarization voltage of the state-space model under different operating states. The specific correction process can be found in the detailed description of step S102 above, and will not be repeated here. The target polarization voltage is used to estimate the polarization voltage correction parameter and the state of charge of the battery cell.

[0203] The specific algorithm for state estimation can be flexibly set according to the actual situation, and may include, but is not limited to, the extended Kalman filter algorithm and the extended Kalman filter algorithm with a fading factor.

[0204] Taking the extended Kalman filter algorithm with a fading factor as an example, a fading memory mechanism can be introduced on the basis of the extended Kalman filter algorithm, and online adaptive estimation of process noise and observation noise can be performed to improve the steady-state performance of the state estimation algorithm and its adaptability to complex environmental conditions.

[0205] The prediction process of the extended Kalman filter algorithm with a fading factor is shown below:

[0206] State prediction:

[0207] x k,k-1 =Ax k-1,k-1 +Bu k-1 +q k-1

[0208] Output prediction:

[0209] y k,k-1 =Cx k,k-1 +Du k +r k-1

[0210] Calculate the residuals:

[0211] ε k =y k -y k,k-1

[0212] Calculate the state prediction covariance:

[0213] P k,k-1 =λ EKF AP k-1,k-1 A T +Q k-1

[0214] The update process of the extended Kalman filter algorithm with a fading factor is shown below:

[0215] Calculate the Kalman gain:

[0216] K k =P k,k-1 C T (CP k,k-1 C T +R k-1 ) -1

[0217] State estimation:

[0218] x k,k =x k,k-1 +K k ε k

[0219] Calculate the state estimate covariance:

[0220] P k,k =P k,k-1 -K k CP k,k-1

[0221] The noise adaptation process of the extended Kalman filter algorithm with a fading factor is shown below:

[0222] Calculate the noise weighting factor:

[0223]

[0224] Estimated state prediction error:

[0225] q k =(1-ρ k )q k-1 +ρ k (x k,k -Ax k-1,k-1 -Bu k-1 )

[0226] Estimating process noise covariance:

[0227] Q k =(1-ρ k )Q k-1 +ρ k [K k ε k (K k ε k ) T +P k -AP k-1 A T ]

[0228] Estimate system output error:

[0229] rk =(1-ρ k )r k-1 +ρ k (y k -Cx k,k-1 -Du k )

[0230] Estimate the observation noise covariance:

[0231] R k =(1-ρ k )R k-1 +ρ k [ε k ε k T -CP k,k-1 C T ]

[0232] in,

[0233]

[0234] M k =CAP k-1,k-1 AC T

[0235] N k =P v,k -CQ k-1 C T -R k-1

[0236]

[0237] In the above calculation process, (·) k,k-1 Let be the variable value at time k predicted based on time k-1, (·) k,k Let λ be the variable value output at time k, τ be the window length for calculating the mean, and λ be the variable value output at time k. EKF It is a gradually diminishing factor.

[0238] During the state estimation of the battery cell, output prediction can be performed based on the state-space model and the target polarization voltage at the previous moment to obtain the output prediction data (i.e., y) at the current moment. k,k-1 And determine the prediction residual (i.e. ε) between the cell's operating data and the current output prediction data. k Based on the predicted residual (i.e., ε) k It can estimate the correction parameters of the polarization voltage at the current moment and the state of charge of the cell at the current moment, thereby obtaining the correction parameters of the polarization voltage at the current moment and the state of charge of the cell at the current moment (i.e., x). k,kThrough the above process, output prediction and residual calculation are performed based on the target polarization voltage, which can adapt to changes in operating state. The resulting prediction residual can also adapt to changes in operating state. Based on this prediction residual, state estimation can be performed to obtain more accurate estimation results.

[0239] Specifically, based on the predicted residuals (i.e., ε) k This allows us to determine the gain value of the polarization voltage correction parameter and the gain value of the cell's state of charge (i.e., K). k ε k Based on the cell's operating data, the correction parameters of the polarization voltage at the previous moment, and the cell's state of charge at the previous moment (i.e., x), k-1,k-1 This allows for the prediction of the correction parameters for the polarization voltage at the current moment and the state of charge (SOC) of the battery cell at the current moment, thereby obtaining the predicted values ​​of the correction parameters for the polarization voltage at the current moment and the predicted values ​​of the SOC of the battery cell at the current moment (i.e., x). k,k-1 ); based on the predicted value of the correction parameter of the polarization voltage at the current moment and the predicted value of the cell's state of charge at the current moment (i.e., x k,k-1 The gain values ​​of the polarization voltage correction parameters and the gain values ​​of the cell's state of charge (i.e., K) k ε k This allows us to determine the correction parameters for the polarization voltage at the current moment and the current state of charge of the cell (i.e., x). k,k Specifically, the correction parameter for the polarization voltage at the current moment can be determined based on the predicted value of the correction parameter and the gain value of the correction parameter. Similarly, the state of charge (SOC) of the cell at the current moment can be determined based on the predicted value of the SOC and the gain value of the SOC. Through this process, the gain value is calculated based on the prediction residual, which can adapt to changes in operating state. The resulting gain value also adapts to changes in operating state. Using this gain value for state estimation yields more accurate estimation results.

[0240] Through the above embodiments, the extended Kalman filter algorithm with a fading factor can be used to estimate the state of charge of the battery cell, thereby obtaining the state of charge of the battery cell. The fading factor can enhance the weight of new data, reduce the influence of old data on the state estimation, and further improve the steady-state performance of the state estimation and its adaptability under complex environmental conditions.

[0241] The above explains the parameter identification process and the state estimation process separately. In fact, the parameter identification process and the state estimation process can run iteratively to each other, that is, the result of the parameter identification process is used as the input of the state estimation process, and the result of the state estimation process is used as the input of the parameter identification process, and the two are continuously optimized iteratively.

[0242] Figure 8The figure shows a schematic diagram of the overall framework for parameter identification and state estimation. As shown in the figure, the algorithm parameters can be initialized first, including but not limited to the initialization of algorithm parameters such as parameter matrix, covariance matrix, forgetting factor, state estimate, process noise covariance, observation noise covariance, and system output error.

[0243] During parameter identification, the corrected gain matrix can be calculated based on the covariance matrix, forgetting factor, and state estimate. The covariance matrix can be updated based on the corrected gain matrix. The system residual can be calculated based on the corrected gain matrix and the cell's operating data. Based on the system residual and the cell's operating data, online fuzzy inference can be performed using a fuzzy controller to update the forgetting factor. The parameter matrix can be updated based on the corrected gain matrix and the system residual. Based on the rationality constraints, the parameters of the state-space model can be inversely derived from the parameter matrix, thereby determining the state-space model.

[0244] In the state estimation prediction process, state and output predictions can be performed based on the determined state-space model. The prediction residuals can be calculated based on the cell's operating data and the output prediction results, and the state prediction covariance is updated according to the fading factor. During the state estimation update process, the Kalman gain can be calculated based on the state prediction covariance, and the state estimate value can be updated based on the Kalman gain, and the state estimation covariance can also be calculated. In the noise adaptive estimation process of state estimation, process noise and observation noise can be estimated online, thereby updating the state prediction error, process noise covariance, system output error, and observation noise covariance. The fading factor can be updated based on the process noise covariance and observation noise covariance.

[0245] The above process iterates continuously, and each iteration yields the latest state estimate, i.e., the state of charge of the battery cell. Through this process, the parameter identification process and the state estimation process can be considered as an organic whole, with each serving as an input to the other and continuously iterating to obtain more accurate estimation results.

[0246] The above methods describe how to determine the state of charge (SOC) of a single battery cell. Based on this, the SOC of a battery composed of multiple cells can be further determined. Please refer to [link / reference]. Figure 9 One embodiment of a method for determining the state of charge of a battery, as described in this application, may include:

[0247] Step S901: Determine the state of charge of each cell in the battery.

[0248] The state of charge of each battery cell can be determined using the aforementioned method for determining the state of charge of a battery cell, which will not be elaborated further in this embodiment.

[0249] Step S902: Determine the state of charge of the battery based on the state of charge of each cell.

[0250] Existing technologies generally require comprehensive consideration of the state of charge of each individual cell when determining the state of charge of a battery, which consumes a large amount of computing and storage resources.

[0251] In one specific implementation of this application, to address the issue of limited computing and storage resources, the maximum and minimum states of charge (SOCs) of each cell can be selected, and the battery's SOC is determined based on these values. This process effectively reduces the consumption of computing and storage resources by determining the battery's SOC solely based on the maximum and minimum SOCs.

[0252] The mapping relationship between the maximum and minimum state of charge (SOC) values ​​and the battery's SOC can be flexibly set according to actual conditions. As an example, the battery's SOC can be determined based on the following mapping relationship:

[0253]

[0254] Among them, SOC min The minimum state of charge (SOC) max The maximum state of charge (SOC) pack This refers to the state of charge of the battery.

[0255] Through the above process, the state of charge (SOC) of each cell can be determined based on the aforementioned method for determining the SOC of a cell, resulting in a more accurate SOC of each cell. Based on this, the SOC of the battery can be determined, resulting in a more accurate SOC of the battery.

[0256] To verify the above-mentioned state of charge determination method under real-world vehicle conditions, tests can be conducted at initial battery temperatures of 29 degrees Celsius and 5 degrees Celsius, respectively, to simulate the application scenarios of a battery without a thermal management system in summer and winter. Typical test conditions are as follows: Figure 10 The dynamic operating conditions shown include typical voltage and current curves.

[0257] By comparing the method for determining the state of charge in this application (denoted as Method 1) with the prior art method for determining the state of charge (denoted as Method 2), we can obtain the following results: Figure 11The test results are shown. It can be seen that: (1) The battery has a significant temperature rise during discharge: the temperature reaches 41 degrees Celsius in the later stage of discharge when the initial temperature is 29 degrees Celsius; and the temperature reaches 22 degrees Celsius in the later stage of discharge when the initial temperature is 5 degrees Celsius. (2) When tested at room temperature (29 degrees Celsius), the SOC error of both methods can be kept within 2%, which is good. (3) When tested at low temperature (5 degrees Celsius), the SOC error of method 2 reaches 6%, while the SOC error of method 1 is still kept within 2%, and the estimation result is significantly smoother, resulting in a better user experience. The above results verify the superiority of the state of charge determination method in the embodiments of this application in the application scenarios of power batteries with wide operating temperature range and strong uncertainty of operating conditions.

[0258] In summary, the embodiments of this application can perform parameter identification and state estimation based on the target equivalent circuit model. Since the polarization voltage in the target equivalent circuit model is related to the operating state, it can be adaptively adjusted as the operating state changes. It has strong adaptability even in scenarios with large changes in operating state, and can obtain more accurate state of charge estimation results.

[0259] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0260] A method for determining the state of charge of a battery cell, corresponding to the above embodiment, Figure 12 This diagram illustrates a structural diagram of an embodiment of a battery cell state-of-charge determination device provided in this application.

[0261] In this embodiment, a battery cell state-of-charge determination device may include:

[0262] The acquisition module 1201 is used to acquire the operating data of the battery cell, wherein the operating data of the battery cell includes at least voltage and current;

[0263] The parameter identification module 1202 is used to identify the parameters of the target equivalent circuit model based on the operating data of the battery cell, and to construct the state space model of the battery cell in the current operating state based on the parameter identification results; wherein, the target equivalent circuit model is constructed on the basis of the basic equivalent circuit model;

[0264] The joint estimation module 1203 is used to jointly estimate the polarization voltage correction parameters and the state of charge of the battery cell based on the battery cell's operating data and state-space model, thereby obtaining the battery cell's state of charge. The polarization voltage correction parameters are used to correct the polarization voltage in the basic equivalent circuit model to obtain the target polarization voltage of the state-space model under different operating states. The target polarization voltage is used to estimate the polarization voltage correction parameters and the battery cell's state of charge.

[0265] The aforementioned device can correct the polarization voltage in the basic equivalent circuit model, thereby obtaining a target polarization voltage that can adapt to changes in operating state. Based on this, parameter identification and state estimation can be performed, which has strong adaptability even in scenarios with large changes in operating state, and can obtain more accurate state of charge estimation results.

[0266] In one specific implementation of this application embodiment, the joint estimation module may include:

[0267] The output prediction submodule is used to predict the output based on the state-space model and the target polarization voltage at the previous moment, and obtain the output prediction data at the current moment.

[0268] The prediction residual determination submodule is used to determine the prediction residual between the cell's operating data and the current output prediction data;

[0269] The state estimation submodule is used to estimate the correction parameters of the polarization voltage at the current moment and the state of charge of the cell at the current moment based on the prediction residual, so as to obtain the correction parameters of the polarization voltage at the current moment and the state of charge of the cell at the current moment.

[0270] Using the above-mentioned device, output prediction and residual calculation are performed based on the target polarization voltage that can adapt to changes in operating state. The obtained prediction residual can also adapt to changes in operating state. Based on the prediction residual, state estimation can be performed to obtain more accurate estimation results.

[0271] In one specific implementation of this application embodiment, the state estimation submodule may include:

[0272] Gain value determination unit, used to determine the gain value of the correction parameter of polarization voltage and the gain value of the state of charge of the cell based on the prediction residual;

[0273] The state prediction unit is used to predict the polarization voltage correction parameters and the state of charge of the battery cell at the current moment based on the battery cell's operating data, the polarization voltage correction parameters at the previous moment, and the battery cell's state of charge at the previous moment, so as to obtain the predicted values ​​of the polarization voltage correction parameters and the battery cell's state of charge at the current moment.

[0274] The state estimation unit is used to determine the correction parameters of the polarization voltage at the current moment based on the predicted value of the correction parameters of the polarization voltage at the current moment and the gain value of the correction parameters of the polarization voltage at the current moment; and to determine the state of charge of the cell at the current moment based on the predicted value of the state of charge of the cell at the current moment and the gain value of the state of charge of the cell at the current moment.

[0275] Using the aforementioned device, the gain value is calculated based on the prediction residual that can adapt to changes in operating state. The obtained gain value can also adapt to changes in operating state. Based on this gain value, state estimation can be performed to obtain more accurate estimation results.

[0276] In one specific implementation of this application embodiment, the state of charge determination device may further include:

[0277] The basic equivalent circuit model determination module is used to determine the basic equivalent circuit model;

[0278] The polarization voltage correction module is used to correct the polarization voltage in the basic equivalent circuit model based on a preset polarization correction sub-model to obtain the target polarization voltage; wherein, the polarization correction sub-model is used to characterize the mapping relationship between the polarization voltage and the target polarization voltage.

[0279] The target equivalent circuit model construction module is used to modify the polarization voltage in the basic equivalent circuit model to the target polarization voltage in order to construct the target equivalent circuit model.

[0280] Using the above-mentioned device, a target equivalent circuit model can be constructed based on the basic equivalent circuit model. Since the polarization voltage is modified to a target polarization voltage that can adapt to changes in operating state, the constructed target equivalent circuit model has strong adaptability even in scenarios with large changes in operating state. Based on this target equivalent circuit model, state estimation can be performed to obtain more accurate estimation results.

[0281] In one specific implementation of this application, the correction parameter for the polarization voltage may include: a linear correction parameter, a quadratic transformation correction parameter, or a higher-order transformation correction parameter.

[0282] With the above-mentioned device, the setting of the polarization voltage correction parameters is more flexible and diverse, so that appropriate correction parameters can be selected according to the actual situation, and the complexity and accuracy of the calculation can be more flexibly controlled, thus having a wider range of applicability.

[0283] In one specific implementation of this application embodiment, when the polarization voltage correction parameter is a linear correction parameter, the polarization voltage correction module may include:

[0284] The linear correction submodule is used to perform linear transformation correction on the polarization voltage based on the linear correction parameters to obtain the target corrected voltage.

[0285] In one specific implementation of this application embodiment, the linear correction submodule may include:

[0286] The proportional transformation unit is used to proportionally transform the polarization voltage based on the proportional parameter in the linear correction parameter to obtain the proportional correction voltage.

[0287] The bias superposition unit is used to superimpose the bias parameters in the linear correction parameters to obtain the target correction voltage by bias superposition of the proportional correction voltage.

[0288] The above-mentioned device can be used to correct polarization voltage based on linear correction parameters. Since the linear correction parameters only involve relatively simple parameters such as proportional parameters and bias parameters, they do not add too much computational complexity and can be better suited for application scenarios with limited computing and storage resources.

[0289] In one specific implementation of this application, the parameter identification module can be specifically used to: determine the terminal voltage error, terminal voltage error change rate, and current change rate of the battery cell based on the battery cell's operating data; perform online fuzzy inference on the terminal voltage error, terminal voltage error change rate, and current change rate using a fuzzy controller to obtain a forgetting factor; use a recursive least squares algorithm carrying the forgetting factor to perform online parameter identification on the target equivalent circuit model to obtain the model parameters of the target equivalent circuit model; and determine the parameters of the state space model based on the model parameters of the target equivalent circuit model.

[0290] With the above-mentioned device, a recursive least squares algorithm carrying a forgetting factor can be used for parameter identification. The forgetting factor is dynamically updated through a fuzzy controller, which can improve the adaptability of the forgetting factor under changes in the running state, thereby improving the accuracy and robustness of the model parameter estimation results.

[0291] In one specific implementation of this application, the parameter identification module and the joint estimation module run iteratively to each other, with the result of the parameter identification submodule serving as the input to the state estimation submodule, and the result of the state estimation submodule serving as the input to the parameter identification submodule.

[0292] The above-mentioned device allows the parameter identification process and the state estimation process to be treated as an organic whole, with each serving as an input to the other and iterating continuously to obtain more accurate estimation results.

[0293] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0294] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0295] Figure 13 A schematic block diagram of an electronic device provided in an embodiment of this application is shown. For ease of explanation, only the parts related to the embodiment of this application are shown.

[0296] like Figure 13 As shown, the electronic device 13 of this embodiment includes a processor 130, a memory 131, and a computer program 132 stored in the memory 131 and executable on the processor 130. When the processor 130 executes the computer program 132, it implements the steps in the various embodiments of the state of charge determination method described above. Alternatively, when the processor 130 executes the computer program 132, it implements the functions of each module / unit in the various device embodiments described above.

[0297] For example, computer program 132 may be divided into one or more modules / units, one or more of which are stored in memory 131 and executed by processor 130 to complete this application. One or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 132 in electronic device 13.

[0298] Those skilled in the art will understand that Figure 13 This is merely an example of electronic device 13 and does not constitute a limitation on electronic device 13. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 13 may also include input / output devices, network access devices, buses, etc.

[0299] The processor 130 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0300] The memory 131 can be an internal storage unit of the electronic device 13, such as a hard disk or RAM. The memory 131 can also be an external storage device of the electronic device 13, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 131 can include both internal and external storage units of the electronic device 13. The memory 131 is used to store computer programs and other programs and data required by the electronic device 13. The memory 131 can also be used to temporarily store data that has been output or will be output.

[0301] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0302] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0303] Those skilled in the art will recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0304] In the embodiments provided in this application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0305] The units described as separate components may or may not be physically separate. The 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 the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0306] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0307] If an integrated module / unit 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, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable storage medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable storage medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0308] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for determining the state of charge of a battery cell, characterized in that, include: Acquire the operating data of the battery cell, wherein the operating data of the battery cell includes at least voltage and current; Based on the operating data of the battery cell, the parameters of the target equivalent circuit model are identified, and a state space model of the battery cell in the current operating state is constructed according to the parameter identification results; wherein, the target equivalent circuit model is constructed on the basis of the basic equivalent circuit model; Based on the cell's operating data and the state-space model, the correction parameters of the polarization voltage and the cell's state of charge are jointly estimated to obtain the cell's state of charge. The polarization voltage correction parameter is used to correct the polarization voltage in the basic equivalent circuit model to obtain the target polarization voltage of the state space model under different operating states; the target polarization voltage is used to estimate the polarization voltage correction parameter and the state of charge of the battery cell.

2. The method for determining the state of charge according to claim 1, characterized in that, The method of jointly estimating the polarization voltage correction parameter and the state of charge of the battery cell based on the cell's operating data and the state-space model to obtain the cell's state of charge includes: Based on the state-space model and the target polarization voltage at the previous moment, output prediction is performed to obtain the output prediction data at the current moment. Determine the prediction residual between the cell's operating data and the current moment's output prediction data; Based on the predicted residual, the correction parameters of the polarization voltage at the current moment and the state of charge of the battery cell at the current moment are estimated to obtain the correction parameters of the polarization voltage at the current moment and the state of charge of the battery cell at the current moment.

3. The method for determining the state of charge according to claim 2, characterized in that, The step of estimating the correction parameters of the polarization voltage at the current moment and the state of charge of the battery cell at the current moment based on the predicted residual, to obtain the correction parameters of the polarization voltage at the current moment and the state of charge of the battery cell at the current moment, includes: The gain value of the correction parameter of the polarization voltage and the gain value of the state of charge of the cell are determined based on the predicted residual. Based on the cell's operating data, the correction parameter of the polarization voltage at the previous moment, and the cell's state of charge at the previous moment, the correction parameter of the polarization voltage at the current moment and the cell's state of charge at the current moment are predicted to obtain the predicted value of the correction parameter of the polarization voltage at the current moment and the predicted value of the cell's state of charge at the current moment. Based on the predicted value of the correction parameter of the polarization voltage at the current moment and the gain value of the correction parameter of the polarization voltage, the correction parameter of the polarization voltage at the current moment is determined. The current state of charge (SOC) of the battery cell is determined based on the predicted SOC and the gain value of the SOC at the current moment.

4. The method for determining the state of charge according to any one of claims 1 to 3, characterized in that, The process of constructing the target equivalent circuit model includes: Determine the basic equivalent circuit model; Based on a preset polarization correction sub-model, the polarization voltage in the basic equivalent circuit model is corrected to obtain the target polarization voltage; wherein, the polarization correction sub-model is used to characterize the mapping relationship between the polarization voltage and the target polarization voltage; Based on the basic equivalent circuit model, the polarization voltage in the basic equivalent circuit model is corrected to the target polarization voltage to construct the target equivalent circuit model.

5. The method for determining the state of charge according to any one of claims 1 to 4, characterized in that, The correction parameters for the polarization voltage include: linear correction parameters, quadratic transformation correction parameters, or higher-order transformation correction parameters.

6. The method for determining the state of charge according to claim 5, characterized in that, When the correction parameter for the polarization voltage is the linear correction parameter, the correction process for the polarization voltage includes: Based on the linear correction parameters, the polarization voltage is linearly transformed and corrected to obtain the target corrected voltage.

7. The method for determining the state of charge according to claim 6, characterized in that, The step of performing a linear transformation correction on the polarization voltage based on the linear correction parameter to obtain the target corrected voltage includes: Based on the proportional parameter in the linear correction parameters, the polarization voltage is proportionally transformed to obtain the proportionally corrected voltage; Based on the bias parameter in the linear correction parameter, the proportional correction voltage is biased and superimposed to obtain the target correction voltage.

8. The method for determining the state of charge according to any one of claims 1 to 7, characterized in that, The parameter identification of the target equivalent circuit model based on the operating data of the battery cell includes: The terminal voltage error, the rate of change of terminal voltage error, and the rate of change of current of the battery cell are determined based on the operating data of the battery cell. The forgetting factor is obtained by performing online fuzzy inference on the terminal voltage error, the rate of change of the terminal voltage error, and the rate of change of the current using a fuzzy controller. The recursive least squares algorithm with a forgetting factor is used to perform online parameter identification on the target equivalent circuit model to obtain the model parameters of the target equivalent circuit model. Based on the model parameters of the target equivalent circuit model, the parameters of the state space model are determined.

9. The method for determining the state of charge according to any one of claims 1 to 8, characterized in that, The parameter identification process and the state estimation process run iteratively to each other. The result of the parameter identification process is used as the input of the state estimation process, and the result of the state estimation process is used as the input of the parameter identification process.

10. A method for determining the state of charge of a battery, characterized in that, include: Based on the state of charge determination method as described in any one of claims 1 to 9, the state of charge of each cell in the battery is determined respectively; The state of charge (SOC) of the battery is determined based on the SOC of each individual cell.

11. The method for determining the state of charge according to claim 10, characterized in that, Determining the state of charge (SOC) of the battery based on the SOC of each individual cell includes: Select the maximum and minimum state of charge values ​​from the states of charge of each cell; The state of charge (SOC) of the battery is determined based on the maximum SOC and the minimum SOC.

12. A device for determining the state of charge of a battery cell, characterized in that, include: An acquisition module is used to acquire the operating data of the battery cell, wherein the operating data of the battery cell includes at least voltage and current; The parameter identification module is used to identify the parameters of the target equivalent circuit model based on the operating data of the battery cell, and to construct the state space model of the battery cell in the current operating state based on the parameter identification results; wherein, the target equivalent circuit model is constructed on the basis of the basic equivalent circuit model; The joint estimation module is used to jointly estimate the correction parameters of the polarization voltage and the state of charge of the battery cell based on the operating data of the battery cell and the state-space model, so as to obtain the state of charge of the battery cell. The polarization voltage correction parameter is used to correct the polarization voltage in the basic equivalent circuit model to obtain the target polarization voltage of the state space model under different operating states; the target polarization voltage is used to estimate the polarization voltage correction parameter and the state of charge of the battery cell.

13. The state of charge determination device according to claim 12, characterized in that, The joint estimation module includes: The output prediction submodule is used to predict the output based on the state space model and the target polarization voltage at the previous moment, so as to obtain the output prediction data at the current moment. The prediction residual determination submodule is used to determine the prediction residual between the operating data of the battery cell and the output prediction data at the current moment; The state estimation submodule is used to estimate the correction parameters of the polarization voltage at the current time and the state of charge of the battery cell at the current time based on the prediction residual, so as to obtain the correction parameters of the polarization voltage at the current time and the state of charge of the battery cell at the current time.

14. The state of charge determination device according to claim 13, characterized in that, The state estimation submodule includes: A gain value determination unit is used to determine the gain value of the correction parameter of the polarization voltage and the gain value of the state of charge of the battery cell based on the prediction residual. The state prediction unit is used to predict the correction parameters of the polarization voltage and the state of charge of the battery cell at the current moment based on the operating data of the battery cell, the correction parameters of the polarization voltage at the previous moment and the state of charge of the battery cell at the previous moment, so as to obtain the predicted value of the correction parameters of the polarization voltage and the predicted value of the state of charge of the battery cell at the current moment. The state estimation unit is used to determine the correction parameter of the polarization voltage at the current moment based on the predicted value of the correction parameter of the polarization voltage at the current moment and the gain value of the correction parameter of the polarization voltage at the current moment; and to determine the state of charge of the battery cell at the current moment based on the predicted value of the state of charge of the battery cell at the current moment and the gain value of the state of charge of the battery cell at the current moment.

15. The state of charge determination apparatus according to any one of claims 12 to 14, characterized in that, The state of charge determination device further includes: A basic equivalent circuit model determination module is used to determine the basic equivalent circuit model; A polarization voltage correction module is used to correct the polarization voltage in the basic equivalent circuit model based on a preset polarization correction sub-model to obtain a target polarization voltage; wherein, the polarization correction sub-model is used to characterize the mapping relationship between the polarization voltage and the target polarization voltage; The target equivalent circuit model construction module is used to modify the polarization voltage in the basic equivalent circuit model to the target polarization voltage based on the basic equivalent circuit model, so as to construct the target equivalent circuit model.

16. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method for determining the state of charge as described in any one of claims 1 to 11.

17. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for determining the state of charge as described in any one of claims 1 to 11.

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