Method and device for estimating SOC (State of Charge) of power battery
By using the adaptive traceless information filtered SOC estimation algorithm in the power battery SOC estimation, using the equivalent circuit model and the Levenberg-Marquardt operator to optimize the error covariance, the problems of low accuracy and poor stability in the prior art are solved, and real-time and accurate estimation of the power battery SOC is achieved.
Patent Information
- Application Number
- CN202411424340.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-12
- Publication Date
- 2025-03-07
AI Technical Summary
The existing SOC estimation methods for power batteries have problems of low accuracy and poor stability, especially when power batteries estimate SOC in real time during operation, it is difficult to ensure accuracy.
Adaptive traceless information filtering SOC estimation algorithm is used to establish the equivalent circuit model of the power battery, and the state equation and observation equation are constructed, and the state prediction error covariance is optimized by using the Levenberg-Marquardt operator. The noise variance is measured through the adaptive filter processing, and the SOC is estimated in real time.
It improves the accuracy and stability of SOC estimation of power batteries, and can estimate SOC in real time and accurately during the operation of power batteries, enhancing the power and safety of electric vehicles.
Smart Images
Figure CN119575213A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of battery technology, and in particular to a method and device for estimating the SOC of a power battery. Background Art
[0002] In recent years, the national government and major automobile companies have been vigorously promoting the development of electric vehicles. As the energy source of electric vehicles, the performance of power batteries plays a vital role in the overall performance and quality of electric vehicles.
[0003] The estimation of power battery SOC (state of charge) is one of the core algorithms in the power battery management process. Accurate estimation of power battery SOC is not only conducive to improving the utilization rate of power batteries and extending the service life of power batteries, but also has important significance for improving the power and safety of electric vehicles.
[0004] At present, the commonly used power battery SOC estimation methods mainly include: ampere-hour integration method, open circuit voltage method, neural network method, etc. However, these methods have certain defects to a greater or lesser extent. For example, the ampere-hour integration method has high requirements for the accuracy of the initial SOC value, and there is an error accumulation phenomenon; the open circuit voltage method can only be accurately estimated when the power battery is stationary for a long time, and cannot be estimated in real time during the operation of the car; the neural network method requires a large amount of data for training, has high requirements for the hardware configuration of the computer, and is greatly affected by the training samples and training methods.
[0005] To this end, we hope to propose a power battery SOC estimation method that is easy to implement while ensuring the estimation accuracy and stability of the power battery SOC. Summary of the invention
[0006] The present application provides a method and device for estimating the SOC of a power battery, so as to ensure the estimation accuracy and stability of the SOC of the power battery while being easy to implement.
[0007] In a first aspect, the present application provides a method for estimating the SOC of a power battery, the method comprising:
[0008] Establishing an equivalent circuit model of the power battery, and constructing a state equation and an observation equation of the power battery according to the equivalent circuit model;
[0009] An adaptive untraceable information filtering SOC estimation algorithm is constructed, and the state estimation value and error covariance at any time are determined by the adaptive untraceable information filtering SOC estimation algorithm; wherein, in the adaptive untraceable information filtering SOC estimation algorithm,
[0010] The prediction error covariance is processed by the Levenberg-Marquardt operator, and the measurement noise variance is processed by an adaptive filter;
[0011] In the state equation and the observation equation, the SOC value of the power battery at any time is solved according to the state estimation value and the error covariance at any time.
[0012] In a feasible embodiment of the present application, the equivalent circuit model is a Thevenin equivalent circuit model, and the state equation and the observation equation of the power battery are constructed according to the Thevenin equivalent circuit model. The state equation and the observation equation of the power battery are:
[0013]
[0014] in, is the state variable at time k, z k is the observation vector at time k, SOC k is the SOC value of the power battery at time k, U 1,k is the polarization voltage value of the power battery at time k, η is the discharge efficiency of the power battery, Δt is the sampling time, Q N is the rated capacity of the power battery, R1, C1, and R0 are the polarization resistance, polarization capacitance, and ohmic internal resistance of the power battery, respectively. k is the current measurement value of the power battery at time k, U oc,k is the open circuit voltage of the power battery at time k, ω k is the system process noise at time k, υ k is the observation noise at time k.
[0015] In a feasible embodiment of the present application, the adaptive unscented information filtering SOC estimation algorithm comprises the following steps:
[0016] Step 1: Initialize the state variables and the estimated error covariance corresponding to the state variables;
[0017] Step 2: Calculate the Sigma point and the weight coefficient corresponding to the Sigma point;
[0018] Step 3: Calculate the state prediction value and the state prediction error covariance optimization value; wherein the state prediction error covariance optimization value is the value obtained by processing the state prediction error covariance through the Levenberg-Marquardt operator;
[0019] Step 4: Update the Sigma point according to the state prediction error covariance optimization value;
[0020] Step 5: Calculate the information matrix prediction value and information vector prediction value of the state variable;
[0021] Step 6: Calculate the measurement prediction value and the cross-covariance;
[0022] Step 7: Calculate the adaptive measurement noise variance; wherein the adaptive measurement noise variance is obtained by processing the measurement noise variance through an adaptive filter;
[0023] Step 8: Calculate the measurement information state and the measurement information matrix;
[0024] Step 9: Update the information matrix and the information vector according to the measurement information state and the measurement information matrix;
[0025] Step 10: Update the state variables and error covariance according to the updated information matrix and information vector.
[0026] In a feasible embodiment of the present application, in step 2, the sampling strategy of the Sigma point is:
[0027]
[0028] Among them, χ i,k-1 is the Sigma point set, n is the dimension of the state variable;
[0029] In step 2, the weight coefficient corresponding to the Sigma point is:
[0030]
[0031] in, is the mean square error weight coefficient corresponding to the Sigma point, is the covariance weight coefficient corresponding to the Sigma point, α(α∈[0,1]) is the distribution state of the sampling point, β is the parameter for merging prior information, λ is the proportional coefficient, λ=α 2 (n+ε)-n, ε is the proportional factor.
[0032] In a feasible embodiment of the present application, in step 3, the state prediction value is calculated as follows:
[0033] χ i,k|k-1 =f(χ i|k-1 );
[0034]
[0035] in, is the state prediction value;
[0036] In step 3, the method of calculating the state prediction error covariance optimization value is:
[0037]
[0038] in, is the state prediction error covariance, is the optimized value of the state prediction error covariance, which is obtained by processing the state prediction error covariance with the Levenberg-Marquardt operator. I is the unit matrix, μ is the optimization parameter, and Q k is the process noise variance at time k.
[0039] In a feasible embodiment of the present application, in step 5, the information matrix prediction value Y of the state variable k|k-1 for:
[0040]
[0041] In step 5, the information vector prediction value y of the state variable k|k-1 for:
[0042]
[0043] In a feasible embodiment of the present application, the calculation method of the measurement prediction value and the cross-covariance is:
[0044] Z i,k|k-1 =h(χ i,k|k-1 );
[0045]
[0046] Among them, χ i,k|k-1 is the updated Sigma point set, is the measured predicted value, is the mutual covariance.
[0047] In a feasible embodiment of the present application, in step 7, the adaptive measurement noise variance is calculated as follows:
[0048]
[0049] Among them, z k is the observed value, e k is the residual between the observed value and the measured predicted value, R k is the measurement noise variance, is the adaptive measurement noise variance.
[0050] In a feasible embodiment of the present application, in step 8, the measurement information state i k for:
[0051]
[0052] In step 8, the information matrix I is measured. k for:
[0053]
[0054] In a second aspect, the present application provides a device for estimating SOC of a power battery, the device comprising:
[0055] A construction module, used to establish an equivalent circuit model of the power battery, and to construct a state equation and an observation equation of the power battery according to the equivalent circuit model;
[0056] A determination module, used to construct an adaptive untraceable information filtering SOC estimation algorithm, and determine the state estimation value and error covariance at any time through the adaptive untraceable information filtering SOC estimation algorithm; wherein, in the adaptive untraceable information filtering SOC estimation algorithm, the prediction error covariance is processed by the Levenberg-Marquardt operator, and the measurement noise variance is processed by the adaptive filter;
[0057] A calculation module is used to solve the SOC value of the power battery at any time according to the state estimation value and the error covariance at any time in the state equation and the observation equation.
[0058] The above technical solution provided by the embodiment of the present application has at least the following advantages compared with the prior art:
[0059] 1. Different from the existing method of estimating battery SOC through Kalman filtering algorithm, the Levenberg-Marquardt operator is introduced to optimize the state prediction error covariance, which ensures the information correlation between information transmission in the estimation process and improves the convergence and stability of the algorithm in the state prediction process.
[0060] 2. Aiming at the problem of filtering divergence caused by the uncertainty of noise statistical characteristics in practical applications, an adaptive filter is introduced to process the measurement noise variance, thereby improving the accuracy of the estimation process.
[0061] The technical solution provided in this application is easy to implement while ensuring the estimation accuracy and stability of the power battery SOC. BRIEF DESCRIPTION OF THE DRAWINGS
[0062] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.
[0063] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.
[0064] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.
[0065] Figure 1 A schematic diagram of a flow chart of a method for estimating the SOC of a power battery provided in an embodiment of the present application;
[0066] Figure 2 A schematic flow chart of an adaptive unscented information filtering SOC estimation algorithm in a power battery SOC estimation method provided in an embodiment of the present application;
[0067] Figure 3 A schematic diagram of the structure of a power battery SOC estimation device provided in an embodiment of the present application;
[0068] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0069] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.
[0070] The disclosure below provides many different embodiments or examples to implement different structures of the present invention. In order to simplify the disclosure of the present invention, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the present invention. In addition, the present invention can repeat reference numbers and / or letters in different examples. This repetition is for the purpose of simplification and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed.
[0071] In order to solve the technical problems in the prior art that the estimation accuracy and stability of the power battery SOC are low, the present application provides a power battery SOC estimation method and device, which are easy to implement while ensuring the estimation accuracy and stability of the power battery SOC.
[0072] Figure 1 A schematic diagram of a method for estimating the SOC of a power battery provided in an embodiment of the present application is provided. Figure 1 The present invention provides a method for estimating the SOC of a power battery, which specifically includes the following steps:
[0073] S101: Establishing an equivalent circuit model of the power battery, and constructing a state equation and an observation equation of the power battery according to the equivalent circuit model;
[0074] S102: constructing an adaptive untraceable information filtering SOC estimation algorithm, and determining a state estimation value and an error covariance at any time by using the adaptive untraceable information filtering SOC estimation algorithm; wherein, in the adaptive untraceable information filtering SOC estimation algorithm, the prediction error covariance is processed by a Levenberg-Marquardt operator, and the measurement noise variance is processed by an adaptive filter;
[0075] S103: Solve the SOC value of the power battery at any time according to the state estimation value and error covariance at any time in the state equation and the observation equation.
[0076] Specifically, in S101, relevant battery parameters of the power battery are obtained, an equivalent circuit model of the power battery is constructed based on the relevant battery parameters of the power battery, and a state equation and an observation equation of the power battery are established according to the equivalent circuit model of the power battery.
[0077] In a feasible embodiment of the present application, a Thevenin equivalent circuit model of the power battery is established. In this embodiment, the state equation and observation equation of the power battery established based on the Thevenin equivalent circuit model are described as follows:
[0078]
[0079] in, is the state variable at time k, z k is the observation vector at time k, SOC k is the SOC value of the power battery at time k, U 1,k is the polarization voltage value of the power battery at time k, η is the discharge efficiency of the power battery, Δt is the sampling time, Q N is the rated capacity of the power battery, R1, C1, and R0 are the polarization resistance, polarization capacitance, and ohmic internal resistance of the power battery, respectively.k is the current measurement value of the power battery at time k, U oc,k is the open circuit voltage of the power battery at time k, ω k is the system process noise at time k, υ k is the observation noise at time k.
[0080] Specifically, in S102, an adaptive untraceable information filtering SOC estimation algorithm is constructed, and the state estimation value and error covariance of the current battery at any time are calculated by the adaptive untraceable information filtering SOC estimation algorithm.
[0081] In the technical solution provided in the present application, the basic principle of the adaptive untraceable information filtering SOC estimation algorithm is implemented based on the Kalman filtering algorithm. The difference between the technical solution provided in the present application and the Kalman filtering algorithm in the prior art is that when solving the prediction error covariance, the prediction error covariance is processed by the Levenberg-Marquardt operator, and when solving the measurement noise variance, the measurement noise variance is processed by an adaptive filter.
[0082] Specifically, in S103, the state estimation value and error covariance of the power battery at any time are determined by an adaptive untraceable information filtering SOC estimation algorithm. For time k, the state estimation value and error covariance at time k-1 are respectively substituted into the state equation and observation equation of the power battery, and the measurement value of the power battery at time k is measured at the same time, thereby estimating the SOC value of the power battery at time k.
[0083] The main improvement of the technical solution provided by the present application lies in the adaptive untraceable information filtering SOC estimation algorithm, which is different from the method of estimating battery SOC through the Kalman filtering algorithm in the prior art. The Levenberg-Marquardt operator is introduced to optimize the state prediction error covariance, which ensures the information correlation between information transmission in the estimation process while improving the convergence and stability of the algorithm in the state prediction process; in order to solve the filtering divergence problem caused by the uncertainty of noise statistical characteristics in practical applications, an adaptive filter is introduced to process the measurement noise variance, thereby improving the accuracy of the estimation process.
[0084] The technical solution provided in this application is easy to implement while ensuring the estimation accuracy and stability of the power battery SOC.
[0085] Figure 2 A schematic diagram of a flow chart of an adaptive unscented information filtering SOC estimation algorithm in a power battery SOC estimation method provided in an embodiment of the present application, referring to Figure 2 In S102, the adaptive untraceable information filtering SOC estimation algorithm specifically includes the following steps:
[0086] Step 1: Initialize the state variables and the estimated error covariance corresponding to the state variables;
[0087] Step 2: Calculate the Sigma point and the weight coefficient corresponding to the Sigma point;
[0088] Step 3: Calculate the state prediction value and the state prediction error covariance optimization value; wherein the state prediction error covariance optimization value is the value obtained by processing the state prediction error covariance through the Levenberg-Marquardt operator;
[0089] Step 4: Update the Sigma point according to the state prediction error covariance optimization value;
[0090] Step 5: Calculate the information matrix prediction value and information vector prediction value of the state variable;
[0091] Step 6: Calculate the measurement prediction value and the cross-covariance;
[0092] Step 7: Calculate the adaptive measurement noise variance; wherein the adaptive measurement noise variance is obtained by processing the measurement noise variance through an adaptive filter;
[0093] Step 8: Calculate the measurement information state and the measurement information matrix;
[0094] Step 9: Update the information matrix and the information vector according to the measurement information state and the measurement information matrix;
[0095] Step 10: Update the state variables and error covariance according to the updated information matrix and information vector.
[0096] Specifically, the implementation principle of the adaptive untraceable information filtering SOC estimation algorithm constructed in S102 is: according to step 1, the initial value of the state variable and the initial value of the estimated error covariance are set; then, the corrected state estimate and error covariance are obtained through steps 2-10; finally, the corrected state estimate and error covariance of step 10 are used as the initial values of the next moment, and the iterative calculation of steps S2-S10 is repeated to determine the state estimate and error covariance at any moment.
[0097] The specific steps of the adaptive unscented information filtering SOC estimation algorithm and the main algorithms involved in each step are as follows:
[0098] Step 1: Initialize the state variables and the estimated error covariance corresponding to the state variables. The initialization process is shown in the following formula (1).
[0099]
[0100] in, To initialize the state variables, is the estimated error covariance corresponding to the initialized state variables.
[0101] Step 2: Calculate the Sigma points and the weight coefficients corresponding to the Sigma points. The Sigma point set is shown in formula (2), and the weight coefficients corresponding to the Sigma points are shown in formula (3).
[0102]
[0103] In formula (2), χ i,k-1 is a Sigma point set, n is the dimension of the state variable, and in a preferred embodiment of the present application, n=2.
[0104] Among them, in formula (3), among them, is the mean square error weight coefficient corresponding to the Sigma point, is the covariance weight coefficient corresponding to the Sigma point, α(α∈[0,1]) is the distribution state of the sampling point, β is the parameter for merging prior information, λ is the proportional coefficient, λ=α 2 (n+ε)-n, ε is a proportional factor. In a preferred embodiment of the present application, ε is 0 or 3-n, and β is 2.
[0105] Step 3: Calculate the state prediction value and the optimized value of the state prediction error covariance; wherein the optimized value of the state prediction error covariance is the value obtained by processing the state prediction error covariance by the Levenberg-Marquardt operator. The state prediction value is calculated as shown in the following formula (4) and formula (5), and the optimized value of the state prediction error covariance is calculated as shown in the following formula (6) and formula (7).
[0106] χ i,k|k-1 =f(χ i,k-1 )(4);
[0107]
[0108] in, is the state prediction value, is the state prediction error covariance, is the optimized value of the state prediction error covariance, which is obtained by processing the state prediction error covariance with the Levenberg-Marquardt operator. I is the unit matrix, μ is the optimization parameter, and Q k is the process noise variance at time k. In a preferred embodiment of the present application, μ=0.21.
[0109] Step 4: Update the Sigma point according to the optimized value of the state prediction error covariance. The updating process is shown in equation (8).
[0110]
[0111] in, is the updated Sigma point set.
[0112] Step 5: Calculate the information matrix prediction value and the information vector prediction value of the state variable. The calculation method of the information matrix prediction value of the state variable is shown in the following formula (9), and the calculation method of the information vector prediction value of the state variable is shown in the following formula (10).
[0113]
[0114] Among them, Y k|k-1 is the information matrix prediction value of the state variable, y k|k-1 is the information vector prediction value of the state variable.
[0115] Step 6: Calculate the measurement prediction value and the cross-covariance. The measurement prediction value is calculated as shown in the following equation (11) and the following equation (12), and the cross-covariance is calculated as shown in the following equation (13).
[0116]
[0117] in, is the measured predicted value, is the mutual covariance.
[0118] Step 7: Calculate the adaptive measurement noise variance. The adaptive measurement noise variance is obtained by processing the measurement noise variance through an adaptive filter. The adaptive measurement noise variance is calculated as shown in the following equations (14) and (15).
[0119]
[0120] Among them, z k is the observed value, e k is the residual between the observed value and the measured predicted value, R k is the measurement noise variance, is the adaptive measurement noise variance.
[0121] Step 8: Calculate the measurement information state and the measurement information matrix. The measurement information state is calculated as shown in equation (16), and the measurement information matrix is calculated as shown in equation (17).
[0122]
[0123] Among them, ik is the measurement information status, I k is the measurement information matrix.
[0124] Step 9: Update the information matrix and the information vector according to the measurement information state and the measurement information matrix. The information matrix is updated as shown in equation (18), and the information vector is updated as shown in equation (19).
[0125] Y k =Y k|k-1 +I k (18);
[0126] y k =y k|k-1 +i k (19);
[0127] Among them, Y k is the information matrix at time k after the update, y k The information vector at time k after the update.
[0128] Step 10: Update the state variables and error covariance according to the updated information matrix and information vector. The state variables are updated as shown in equation (20), and the error covariance is updated as shown in equation (21).
[0129]
[0130] Based on the above description of the steps of the adaptive untraceable information filtering SOC estimation algorithm, the adaptive untraceable information filtering SOC estimation algorithm provided in the present application can be divided into an initialization process, a prediction process and an update process. After completing the initialization process, the prediction process and the update process are continuously cycled to realize the prediction of the state estimation value and error covariance at any time.
[0131] Figure 3 A schematic diagram of a power battery SOC estimation device provided in an embodiment of the present application, referring to Figure 3 , corresponding to the above method embodiment, the embodiment of the present application further provides a power battery SOC estimation device, the device specifically comprising:
[0132] A construction module 301 is used to establish an equivalent circuit model of the power battery, and to construct a state equation and an observation equation of the power battery according to the equivalent circuit model;
[0133] A determination module 302 is used to construct an adaptive untraceable information filtering SOC estimation algorithm, and determine the state estimation value and error covariance at any time through the adaptive untraceable information filtering SOC estimation algorithm; wherein, in the adaptive untraceable information filtering SOC estimation algorithm, the prediction error covariance is processed by the Levenberg-Marquardt operator, and the measurement noise variance is processed by the adaptive filter;
[0134] The calculation module 303 is used to solve the SOC value of the power battery at any time according to the state estimation value and the error covariance at any time in the state equation and the observation equation.
[0135] like Figure 4 As shown, an embodiment of the present application provides an electronic device, including a processor 401, a communication interface 402, a memory 403 and a communication bus 404, wherein the processor 401, the communication interface 402, and the memory 403 communicate with each other through the communication bus 404.
[0136] Memory 403, used for storing computer programs;
[0137] In one embodiment of the present application, the processor 401 is used to implement a power battery SOC estimation method provided by any one of the aforementioned method embodiments when executing a program stored in the memory 403 .
[0138] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of a method for estimating the SOC of a power battery provided in any of the aforementioned method embodiments are implemented.
[0139] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0140] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a general hardware platform, and of course, by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the relevant technology can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.
[0141] It should be understood that the terms used herein are only for the purpose of describing specific example embodiments and are not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms "one", "an" and "said" as used herein may also be meant to include plural forms. The terms "include", "comprise", "contain", and "have" are inclusive, and therefore specify the existence of stated features, steps, operations, elements and / or parts, but do not exclude the existence or addition of one or more other features, steps, operations, elements, parts, and / or combinations thereof. The method steps, processes, and operations described herein are not interpreted as necessarily requiring them to be performed in the specific order described or illustrated, unless the execution order is clearly indicated. It should also be understood that additional or alternative steps may be used.
[0142] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.
Claims
1. A method for estimating the SOC of a power battery, characterized in that: The method comprises: Establishing an equivalent circuit model of the power battery, and constructing a state equation and an observation equation of the power battery according to the equivalent circuit model; Constructing an adaptive untraceable information filtering SOC estimation algorithm, and determining the state estimation value and error covariance at any time by the adaptive untraceable information filtering SOC estimation algorithm; wherein, in the adaptive untraceable information filtering SOC estimation algorithm, the prediction error covariance is processed by the Levenberg-Marquardt operator, and the measurement noise variance is processed by the adaptive filter; In the state equation and the observation equation, the SOC value of the power battery at any time is solved according to the state estimation value and the error covariance at any time.
2. The method according to claim 1, characterized in that The equivalent circuit model is a Thevenin equivalent circuit model. The state equation and the observation equation of the power battery are constructed according to the Thevenin equivalent circuit model. The state equation and the observation equation of the power battery are: in, is the state variable at time k, z k is the observation vector at time k, SOC k is the SOC value of the power battery at time k, U 1,k is the polarization voltage value of the power battery at time k, η is the discharge efficiency of the power battery, Δt is the sampling time, Q N is the rated capacity of the power battery, R1, C1, and R0 are the polarization resistance, polarization capacitance, and ohmic internal resistance of the power battery, respectively. k is the current measurement value of the power battery at time k, U oc,k is the open circuit voltage of the power battery at time k, ω k is the system process noise at time k, υ k is the observation noise at time k.
3. The method according to claim 1, characterized in that The adaptive untraceable information filtering SOC estimation algorithm comprises the following steps: Step 1: Initialize the state variables and the estimated error covariance corresponding to the state variables; Step 2: Calculate the Sigma point and the weight coefficient corresponding to the Sigma point; Step 3: Calculate the state prediction value and the state prediction error covariance optimization value; wherein the state prediction error covariance optimization value is the value obtained by processing the state prediction error covariance through the Levenberg-Marquardt operator; Step 4: Update the Sigma point according to the state prediction error covariance optimization value; Step 5: Calculate the information matrix prediction value and information vector prediction value of the state variable; Step 6: Calculate the measurement prediction value and the cross-covariance; Step 7: Calculate the adaptive measurement noise variance; wherein the adaptive measurement noise variance is obtained by processing the measurement noise variance through an adaptive filter; Step 8: Calculate the measurement information state and the measurement information matrix; Step 9: Update the information matrix and the information vector according to the measurement information state and the measurement information matrix; Step 10: Update the state variables and error covariance according to the updated information matrix and information vector.
4. The method according to claim 3, characterized in that: In step 2, the sampling strategy of Sigma points is: Among them, χ i,k-1 is the Sigma point set, n is the dimension of the state variable; In step 2, the weight coefficient corresponding to the Sigma point is: in, is the mean square error weight coefficient corresponding to the Sigma point, is the covariance weight coefficient corresponding to the Sigma point, α(α∈[0,1]) is the distribution state of the sampling point, β is the parameter for merging prior information, λ is the proportional coefficient, λ=α 2 (n+ε)-n, ε is the proportional factor.
5. The method according to claim 4, characterized in that: In step 3, the state prediction value is calculated as follows: x i,k|k-1 =f(x i|k-1 ); in, is the state prediction value; In step 3, the method of calculating the state prediction error covariance optimization value is: in, is the state prediction error covariance, is the optimized value of the state prediction error covariance, which is obtained by processing the state prediction error covariance with the Levenberg-Marquardt operator. I is the unit matrix, μ is the optimization parameter, and Q k is the process noise variance at time k.
6. The method according to claim 5, characterized in that: In step 5, the information matrix prediction value Y of the state variable k|k-1 for: In step 5, the information vector prediction value y of the state variable k|k-1 for:
7. The method according to claim 4, characterized in that In step 6, the calculation method of the measurement prediction value and the cross-covariance is: Z i,k|k-1 =h(χ i,k|k-1 ); Among them, χ i,k|k-1 is the updated Sigma point set, is the measured predicted value, is the mutual covariance.
8. The method according to claim 7, characterized in that In step 7, the adaptive measurement noise variance is calculated as follows: Among them, z k is the observed value, e k is the residual between the observed value and the measured predicted value, R k is the measurement noise variance, is the adaptive measurement noise variance.
9. The method according to claim 8, characterized in that: In step 8, the information state i is measured. k for: In step 8, the information matrix I is measured. k for:
10. A power battery SOC estimation device, characterized in that: The device comprises: A construction module, used to establish an equivalent circuit model of the power battery, and to construct a state equation and an observation equation of the power battery according to the equivalent circuit model; A determination module, used to construct an adaptive untraceable information filtering SOC estimation algorithm, and determine the state estimation value and error covariance at any time through the adaptive untraceable information filtering SOC estimation algorithm; wherein, in the adaptive untraceable information filtering SOC estimation algorithm, the prediction error covariance is processed by the Levenberg-Marquardt operator, and the measurement noise variance is processed by the adaptive filter; A calculation module is used to solve the SOC value of the power battery at any time according to the state estimation value and the error covariance at any time in the state equation and the observation equation.