Initialization assignment method for SOC estimation of storage battery and SOC estimation method
By setting the initial value according to the resistance mean value and SOC relationship and optimizing the parameter assignment of the Kalman filter method, the problem of slow convergence speed of SOC estimation is solved, and more efficient SOC estimation is achieved.
Patent Information
- Application Number
- CN202510678165.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-26
- Publication Date
- 2025-08-12
AI Technical Summary
In the existing Kalman filtering method, the initial assignment method of the battery SOC leads to a slow convergence speed, affecting the SOC estimation efficiency.
The initial values of the ohmic internal resistance and polarization resistance are determined according to the resistance mean value in different SOC states. Combined with the relationship between the equivalent ideal voltage source and SOC inside the battery, the initial value of the SOC is set, and the fixed value is used as the initial values of the error covariance, state noise variance and observed noise variance. The initial values of each parameter are determined through interpolation calculation.
The convergence speed of the Kalman filtering algorithm is improved, the efficiency of SOC estimation is improved, and the number of iterations is shortened.
Smart Images
Figure CN120468692A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of energy storage management, and in particular to an initialization value assignment method for battery SOC estimation and an SOC estimation method. Background Art
[0002] State of Charge (SOC) refers to the ratio of a battery's remaining capacity after a period of use or long-term storage to its fully charged capacity, often expressed as a percentage. In a battery management system, the accuracy of SOC estimation directly impacts the battery's service life and performance. Highly accurate SOC estimation can prevent overcharging and discharging, extending the battery's service life.
[0003] SOC estimation can be achieved through the ampere-hour integration method, Kalman filter method, etc. Among them, the ampere-hour integration method estimates SOC by integrating current. It is suitable for situations where the current acquisition accuracy is sufficient, but its robustness is weak and needs to be combined with a correction strategy. The Kalman filter method estimates SOC through recursion. Specifically, the internal circuit of the battery can be simplified to a second-order RC equivalent circuit model, such as Figure 1 As shown, where U oc represents the equivalent ideal voltage source within the battery, which has a nonlinear relationship with the SOC. R0 represents the ohmic internal resistance, R1 and R2 are polarization resistors, C1 and C2 are polarization capacitors, and UT represents the terminal voltage of the battery. Discharge current is defined as positive and charge current as negative. Based on Kirchhoff's laws, the system equation and observation equation can be derived. When using the Kalman filter method, it is necessary to assign values to the parameters R0, R1, R2, C1, C2, and the initial SOC state in the battery's second-order RC equation. Then, through multiple rounds of iteration, the error converges to obtain an accurate estimate of the battery's SOC. The initial SOC value assigned to the battery significantly affects the algorithm's convergence speed. In conventional Kalman filtering, each parameter is initialized to a fixed value, resulting in a slow overall convergence speed. Summary of the Invention
[0004] In order to solve some or all of the problems in the prior art, the present invention provides a method for initializing and assigning a value for estimating the battery SOC in a first aspect, comprising:
[0005] Determining an initial value of the ohmic internal resistance, an initial value of the first polarization resistance, and an initial value of the second polarization resistance in a second-order RC equivalent circuit model of the battery according to average values of the respective resistances at different SOC states;
[0006] Determining an initial value of the battery SOC based on a relationship between an equivalent ideal voltage source within the battery and the SOC;
[0007] Determining initial values of the first polarization capacitor and the second polarization capacitor according to the initial SOC value of the battery; and
[0008] Preset fixed values are used as the initial values of the error covariance, state noise variance, and observation noise variance.
[0009] Furthermore, the initial value of the terminal voltage of the battery is equal to the sum of the average value of the ohmic internal resistance, the average value of the first polarization resistance and the average value of the second polarization resistance multiplied by the initial current value, plus the terminal voltage of the battery at the initial moment, wherein the initial current value is the battery current sampling value at the initial moment, and the terminal voltage at the initial moment is the battery voltage sampling value at the initial moment.
[0010] Furthermore, based on the relationship between the equivalent ideal voltage source inside the battery and the SOC, an initial value of the battery SOC is determined by an interpolation calculation method.
[0011] Furthermore, based on the relationship between the first polarization capacitance, the second polarization capacitance and the SOC inside the battery, the initial values of the first polarization capacitance and the second polarization capacitance inside the battery are determined by an interpolation calculation method.
[0012] Furthermore, the initial value of the error covariance is 0.1; and / or
[0013] The initial value of the state noise variance is [1e-6 0 0; 0 1e-6 0; 0 0 1e-6]; and / or
[0014] The initial value of the observation noise variance is 5e-6.
[0015] Based on the aforementioned initialization assignment method, a second aspect of the present invention provides a battery SOC estimation method, which estimates the SOC based on a second-order RC equivalent circuit model of the battery and includes:
[0016] Initialization assignment is performed through the initialization assignment method as described above;
[0017] Based on the initial value, a priori prediction is made according to the state equation and the error covariance is updated;
[0018] Calculate the Kalman gain based on the updated error covariance;
[0019] Calculating an optimal estimate based on the Kalman gain and updating an error covariance state; and
[0020] Multiple rounds of iteration are performed until the error covariance is less than a preset value.
[0021] Furthermore, the initial value includes an initial state quantity and an initial value of the error covariance, wherein the initial state quantity in I k-1 is the initial value of current, R1 k-1 、R2 k-1 are the initial values of the first and second polarization resistances respectively.
[0022] Furthermore, the a priori prediction is achieved according to the following formula:
[0023]
[0024] in:
[0025] T s The unit duration is 1 second, C1 k-1 is the initial value of the first polarization capacitor, and C2 k-1 is the initial value of the second polarization capacitance; it is obtained by interpolation calculation based on the battery SOC value at the initial moment.
[0026] Q n is the rated capacity of the battery, in Ah;
[0027] The error covariance is updated according to the following formula:
[0028]
[0029] in,
[0030] P k-1 is the initial value of the error covariance, Q k-1 is the initial value of the state noise variance.
[0031] Furthermore, the Kalman gain K k Calculated according to the following formula:
[0032]
[0033] in,
[0034] is the ideal source voltage U of the battery oc Partial derivatives with respect to SOC; and
[0035] R k-1 is the initial value of the observation noise variance.
[0036] Furthermore, the optimal estimate Calculated according to the following formula:
[0037]
[0038] in,
[0039] is the ideal source voltage U equivalent to the battery oc Partial derivative with respect to SOC; D k =-R0 k , R0 k is the ohmic internal resistance of the battery, UT k is the battery terminal voltage.
[0040] Furthermore, the error covariance state P k The updated formula is as follows:
[0041]
[0042] in,
[0043] E is the identity matrix.
[0044] The present invention provides an initialization assignment method and an SOC estimation method for battery SOC estimation. According to the relationship between the parameters of the second-order RC equivalent circuit model of the battery and the SOC, the initial values of the parameters are set, which can effectively improve the convergence speed of the Kalman filter algorithm and thus improve the efficiency of SOC estimation. BRIEF DESCRIPTION OF THE DRAWINGS
[0045] To further illustrate the above and other advantages and features of various embodiments of the present invention, a more detailed description of various embodiments of the present invention will be presented with reference to the accompanying drawings. It will be understood that these drawings depict only typical embodiments of the present invention and are not to be considered as limiting the scope thereof. In the drawings, for clarity, identical or corresponding components will be represented by the same or similar reference numerals.
[0046] Figure 1 A schematic diagram showing the structure of a second-order RC equivalent circuit model of a battery;
[0047] Figure 2 A schematic flow chart showing a method for estimating battery SOC according to an embodiment of the present invention; and
[0048] Figure 3 A schematic diagram showing a comparison of estimation errors between an initialization value assignment method for battery SOC estimation according to an embodiment of the present invention and a conventional Kalman filter algorithm is shown. DETAILED DESCRIPTION
[0049] In the following description, the present invention is described with reference to various embodiments. However, those skilled in the art will recognize that the embodiments may be implemented without one or more of the specific details or with other alternative and / or additional methods or components. In other cases, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring the inventive aspects of the present invention. Similarly, specific numbers and configurations are set forth for illustrative purposes in order to provide a comprehensive understanding of the embodiments of the present invention. However, the present invention is not limited to these specific details.
[0050] In this specification, reference to "one embodiment" or "the embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. The appearances of the phrase "in one embodiment" in various places in this specification are not necessarily all referring to the same embodiment.
[0051] It should be noted that the embodiments of the present invention describe the method steps in a specific order, but this is only for the purpose of illustrating the specific embodiment and does not limit the order of the steps. On the contrary, in different embodiments of the present invention, the order of the steps can be adjusted according to actual needs.
[0052] The second-order RC equivalent circuit model of the battery is as follows: Figure 1 As shown, the direction of the discharge current is defined as positive and the charging current is defined as negative. According to Kirchhoff's law, the system equation and observation equation can be obtained, where the system equation is:
[0053]
[0054] The observation equation is:
[0055] U T =U OC (SOC)-R0I T -U1-U2,
[0056] Among them, U oc Represents the equivalent ideal voltage source inside the battery, which has a nonlinear relationship with SOC. R0 represents the ohmic internal resistance, R1 and R2 are the first and second polarization resistors, C1 and C2 are the first and second polarization capacitors, U1 and U2 are the voltage values across the first and second polarization resistors, I T Indicates the battery current, U T represents the terminal voltage of the battery, Q n Represents the rated capacity of the battery. Each battery parameter has a nonlinear relationship with the battery's SOC. Table 1 shows the relationship between various parameters and the SOC of a 5Ah battery at an ambient temperature of 15°C.
[0057]
[0058] Table 1
[0059] For nonlinear systems, in order to use the extended Kalman filter (EKF) method to estimate the SOC of the battery, the system state equation is discretized to obtain the following formula:
[0060]
[0061] Where T s The unit time length is 1 second.
[0062] Discretize the observation equation of the system and obtain the following formula:
[0063] UT k =UOC(SOC) k ―R0 k *I k ―U1 k ―U2 k ,
[0064] Let x = [U1, U2, SOC], y = U T , and linearize the above equation, it can be further written as:
[0065]
[0066] in,
[0067] D=―R0 k .
[0068] At this point, the Kalman filter operation can be performed, that is, the parameters R0, R1, R2, C1, C2 and the initial SOC state in the second-order RC equation of the battery are assigned values, and then through multiple rounds of iteration, the error converges and finally an accurate estimate of the battery's SOC is obtained. In the conventional Kalman filter algorithm, the initial values of R0, R1, R2, C1, and C2 are all set to 0, and the initial state quantity is Although this method can ultimately produce a relatively accurate SOC estimate, its overall convergence speed is slow and requires a large number of iterations. To improve convergence speed, the present invention provides an initialization and value assignment method for battery SOC estimation and an SOC estimation method. Based on the relationship between the parameters of the battery's second-order RC equivalent circuit model and the SOC, the initial values of each parameter are set. This method has been proven to effectively improve the convergence speed of the Kalman filter algorithm.
[0069] The technical solution of the present invention is further described below in conjunction with the accompanying drawings of the embodiments.
[0070] Figure 2 FIG. 1 is a flow chart showing a method for estimating battery SOC according to an embodiment of the present invention. Figure 2 As shown, a battery SOC estimation method includes:
[0071] First, in step 201, the values are initialized. As mentioned above, in an embodiment of the present invention, the initial values of the various parameters are set according to the relationship between the various parameters and the SOC. Specifically, in one embodiment of the present invention, the initial value of the terminal voltage of the battery is determined mainly based on the average value of each resistor under different SOC states, wherein the resistor includes the ohmic internal resistance, the first polarization resistor and the second polarization resistor, and the initial value of the battery SOC is determined based on the relationship between the equivalent ideal voltage source inside the battery and the SOC, while the error covariance, state noise variance and observation noise variance use fixed values as initial values. Based on the initial values of the parameters, the initial state quantity and the initial value of the error covariance can be obtained, wherein the initial state quantity in I k-1 is the initial current value, which is the battery current sampling value at the initial moment, R1 k-1 、R2 k-1 are the initial values of the first and second polarization resistances, which are equal to the average values of the first and second polarization resistances at different SOC states. k-1 According to U oc The relationship between θ and SOC is calculated by interpolation. In addition, in one embodiment of the present invention, the initial value of the error covariance is 0.1. In one embodiment of the present invention, the initial value of the state noise variance is [1e-6 0 0; 0 1e-6 0; 0 0 1e-6]. In one embodiment of the present invention, the initial value of the observation noise variance is 5e-6;
[0072] Next, in step 202, a priori prediction is performed. Based on the initial state quantity, a priori prediction is performed according to the state equation. In one embodiment of the present invention, the priori prediction is implemented according to the following formula:
[0073]
[0074] in:
[0075] T s The unit duration is 1 second, C1 k-1 is the initial value of the first polarization capacitor, and C2 k-1 is the initial value of the second polarization capacitance; it is obtained by interpolation calculation based on the battery SOC value at the initial moment.
[0076] Q n is the rated capacity of the battery, in Ah;
[0077] Next, in step 203, the error covariance is updated. Based on the initial state quantity, the error covariance is updated. In one embodiment of the present invention, the error covariance is updated according to the following formula:
[0078]
[0079] in,
[0080] P k-1 is the initial value of the error covariance, Q k-1 is the initial value of the state noise variance;
[0081] Next, in step 204, the Kalman gain is calculated. The Kalman gain is calculated based on the updated error covariance. In one embodiment of the present invention, the Kalman gain K k Calculated according to the following formula:
[0082]
[0083] in,
[0084] is the ideal source voltage U of the battery oc Partial derivatives with respect to SOC; and
[0085] R k-1 is the initial value of the observation noise variance;
[0086] Next, in step 205, the optimal estimate is calculated. Based on the Kalman gain, the optimal estimate is calculated. In one embodiment of the present invention, the optimal estimate Calculated according to the following formula:
[0087]
[0088] in,
[0089] is the ideal source voltage U equivalent to the battery oc Partial derivative with respect to SOC; D k =-R0 k , R0 k is the ohmic internal resistance of the battery, UT k is the battery terminal voltage.
[0090] Finally, in step 206, the error covariance state is updated. The error covariance state is updated based on the Kalman gain and the error covariance. In one embodiment of the present invention, the error covariance state P k The updated formula is as follows:
[0091]
[0092] in,
[0093] E is the identity matrix.
[0094] Based on the optimal estimated value, steps 202 to 206 are repeated for multiple iterations until the error covariance state is less than a preset value, that is, the algorithm converges, and an accurate estimated value of the battery SOC is obtained.
[0095] In order to verify the SOC estimation method proposed in the present invention, different charging currents are used to charge the single battery, and the following results are obtained through testing: Figure 3 The results shown in the figure show that when using the conventional EKF algorithm, the SOC steady-state error only stabilizes after about 2000 iterations. However, when using the initial assignment optimization EKF algorithm of the present invention, the SOC steady-state error stabilizes after only about 50 iterations.
[0096] The battery SOC estimation method can be applied to on-orbit satellites to achieve monitoring and rapid measurement of satellite battery SOC.
[0097] Based on the battery SOC estimation method described above, the present invention further provides an electronic device for estimating battery SOC, comprising a memory and a processor, wherein the memory is configured to store a computer program that, when executed by the processor, executes the battery SOC estimation method described above. The electronic device can be used, for example, in an in-orbit satellite to monitor and rapidly measure the SOC of the satellite's batteries.
[0098] The present invention further provides a computer-readable storage medium for estimating battery SOC, which stores a computer program that, when executed on a processor, performs the battery SOC estimation method described above. The computer-readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device, such as an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. Specifically, the computer-readable storage medium includes, but is not limited to, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof.
[0099] In an embodiment of the present invention, the computer program can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer program from the network and forwards it for storage in the computer-readable storage medium in the respective computing / processing device.
[0100] In an embodiment of the present invention, the computer program may be an assembly instruction, an instruction set architecture (ISA) instruction, a machine instruction, a machine-dependent instruction, a microcode, a firmware instruction, a state setting data, or a source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The computer program may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, the computer program may be executed by utilizing the state information of the computer program to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA).
[0101] Although various embodiments of the present invention have been described above, it should be understood that they are presented by way of example only and not limitation. It will be apparent to those skilled in the relevant art that various combinations, modifications, and variations may be made thereto without departing from the spirit and scope of the present invention. Therefore, the breadth and scope of the present invention disclosed herein should not be limited by the exemplary embodiments disclosed above, but should be defined solely in accordance with the appended claims and their equivalents.
Claims
1. A method for initializing and assigning values for estimating battery SOC, characterized in that: Including steps: Determining an initial value of the ohmic internal resistance, an initial value of the first polarization resistance, and an initial value of the second polarization resistance in a second-order RC equivalent circuit model of the battery according to average values of the respective resistances at different SOC states; Determining an initial value of the battery SOC based on a relationship between an equivalent ideal voltage source within the battery and the SOC; Determining initial values of the first polarization capacitor and the second polarization capacitor according to the initial value of the battery SOC; as well as Preset fixed values are used as the initial values of the error covariance, state noise variance, and observation noise variance.
2. The initialization assignment method according to claim 1, wherein: The initial value of the equivalent ideal voltage source inside the battery is equal to the sum of the average value of the ohmic internal resistance, the average value of the first polarization resistance, and the average value of the second polarization resistance multiplied by the battery current at the initial moment, plus the voltage sampling value across the battery at the initial moment, wherein the battery current is positive when the battery is discharging and the battery current is negative when the battery is charging.
3. The initialization assignment method according to claim 1, wherein: Based on the relationship between the internal equivalent ideal voltage source of the battery and the SOC, an initial value of the battery SOC is determined by an interpolation calculation method.
4. The initialization assignment method according to claim 1, wherein: Based on the relationship between the first polarization capacitance, the second polarization capacitance and the SOC inside the battery, the initial values of the first polarization capacitance and the second polarization capacitance inside the battery are determined by an interpolation calculation method.
5. The initialization assignment method according to claim 1, wherein: The initial value of the error covariance is 0.1; and / or the initial value of the state noise variance is [1e-6 00; 0 1e-6 0; 0 0 1e-6]; and / or the initial value of the observation noise variance is 5e-6.
6. A battery SOC estimation method, characterized in that: Based on the second-order RC equivalent circuit model of the battery, the SOC is estimated by using a Kalman filter algorithm. The battery SOC estimation method includes the following steps: By using the initialization assignment method according to any one of claims 1 to 5, initial values of the ohmic internal resistance, the first polarization resistance, the second polarization resistance, the first polarization capacitance, the second polarization capacitance, the SOC, the error covariance, the state noise variance, and the observation noise variance are set; Based on the initial value, perform a priori prediction according to the state equation and update the error covariance; Calculate the Kalman gain based on the updated error covariance; Based on the Kalman gain, an optimal estimate is calculated and an error covariance state is updated; as well as Based on the optimal estimated value, a priori prediction is performed again and the error covariance state is updated, and this process is repeated for multiple rounds until the error covariance state is less than a preset value.
7. The battery SOC estimation method according to claim 6, wherein: The initial value includes the initial state quantity and the initial value of the error covariance, wherein the initial state quantity in I k-1 is the initial current value, obtained by sampling the battery current at the initial moment; R1 k-1 、R2 k-1 are the initial values of the first and second polarization resistances respectively.
8. The battery SOC estimation method according to claim 7, wherein: The a priori prediction is achieved according to the following formula: in: T s The unit duration is 1 second, C1 k-1 is the initial value of the first polarization capacitor, and C2 k-1 is the initial value of the second polarized capacitor, and the initial values of the first and second polarized capacitors are obtained by interpolation calculation according to the battery SOC value at the initial moment; Q n is the rated capacity of the battery, in Ah; and The error covariance is updated according to the following formula: in, P k-1 is the initial value of the error covariance, Q k-1 is the initial value of the state noise variance.
9. The battery SOC estimation method according to claim 6, wherein: The battery observation equation is: OUT k =C k *x k +D k *IN k ; It is composed of UT=―U1―U2+U OC (SOC)―R0*I is obtained after discretization, where is the ideal source voltage U equivalent to the battery oc Partial derivative with respect to SOC; D k =-R0 k , R0 k is the ohmic internal resistance of the battery.
10. The battery SOC estimation method according to claim 8, wherein: The Kalman gain K k Calculated according to the following formula: Among them, R k-1 is the initial value of the observation noise variance.
11. The battery SOC estimation method according to claim 9, wherein: The optimal estimate Calculated according to the following formula:
12. The battery SOC estimation method according to claim 9, wherein: The error covariance state P k The updated formula is as follows: in, E is the identity matrix.