Electric quantity estimation method and voltameter chip
By adjusting the parameters about crystal oscillator deviation in the state variable covariance matrix in the battery meter chip, the problem that the crystal oscillator clock deviation affects the battery estimation accuracy is solved, and a more accurate battery estimation is achieved.
Patent Information
- Application Number
- CN202411676481.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-21
- Publication Date
- 2025-03-25
AI Technical Summary
The clock signal provided by the crystal oscillator in the battery meter chip is affected by factors such as temperature changes, power supply fluctuations, process differences and aging, resulting in clock deviations, affecting the accurate estimation of the battery remaining battery by the battery meter chip and reducing the accuracy of the battery estimation.
By adjusting the parameters about crystal oscillator deviation in the state variable covariance matrix, the clock deviation is corrected and the battery estimation accuracy is improved. The specific method includes establishing a Kalman equation containing crystal oscillator deviation according to the battery RC equivalent model, and updating and adjusting the crystal oscillator deviation parameters in the covariance matrix of the state variable during the extended Kalman filtering process.
By correcting the clock deviation, the accuracy of the power estimation is improved and the error of the battery residual power estimation of the battery meter chip is reduced.
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Figure CN119689299A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of battery management technology, and in particular to a power estimation method and a power meter chip. Background Art
[0002] A fuel gauge is a device used to monitor and display the remaining power of a battery. The fuel gauge estimates the remaining power of a battery by measuring the voltage and current of the battery and combining the battery's historical discharge behavior and temperature effects. A fuel gauge chip is a product form of a fuel gauge that can provide important information such as the remaining power, health status, and charging and discharging of the battery through high-precision sampling and complex algorithms, thereby extending battery life and improving device performance.
[0003] The crystal oscillator is the clock source in the fuel gauge chip, providing a clock signal for the fuel gauge chip to estimate the remaining power. However, due to factors such as temperature changes, power supply fluctuations, process differences, and aging, the clock signal provided by the crystal oscillator to the fuel gauge chip deviates from the expected value. This deviation affects the fuel gauge chip's accurate estimation of the remaining battery power and reduces the accuracy of power estimation. Summary of the invention
[0004] In view of this, the present application provides a power estimation method and a power meter chip, which achieves the purpose of correcting clock deviation by adjusting the parameters related to crystal oscillator deviation in the state variable covariance matrix, thereby achieving the effect of improving the accuracy of power estimation.
[0005] In a first aspect, an embodiment of the present invention provides a method for estimating power, the method comprising:
[0006] The Kalman equation including the crystal oscillator deviation is established based on the battery RC equivalent model;
[0007] Performing an extended Kalman filter on the Kalman equation, wherein the process of the extended Kalman filter includes:
[0008] After the state variable covariance matrix is updated, the parameters related to the crystal oscillator deviation in the state variable covariance matrix are adjusted.
[0009] In some embodiments, the state variable covariance matrix includes the variance of the crystal oscillator deviation;
[0010] After the state variable covariance matrix is updated, the parameters related to the crystal oscillator deviation in the state variable covariance matrix are adjusted, including:
[0011] After the variance of the crystal oscillator deviation is adjusted using the variance adjustment value, it is re-substituted into the updated state variable covariance matrix.
[0012] In some embodiments, the variance calibration value used in each calibration is a fixed value.
[0013] In some embodiments, establishing a Kalman equation including a crystal oscillator deviation according to a battery RC equivalent model includes:
[0014] The state equation is: Q k =Q k-1 +I k (t k -Δt k );
[0015] The state update equation is:
[0016] The observation equation is: in,
[0017] The observed variable at the kth moment is: battery voltage V k 、Battery current I k ; The state variable at the kth moment is: battery power Q k , battery capacitor voltage V′ k , crystal deviation Δt k ; Battery parameters are: resistance R0, R1 and capacitor C1; t k Step the crystal clock;
[0018] Represents state variables y represents the observed variable u represents the control variable u k =I k ;
[0019] Represents the state transfer matrix:
[0020] Represents the control matrix:
[0021] Represents the measurement matrix:
[0022] represent and The connection matrix between them; k(Q k ) represents the difference between Q and k The slope value related to the value.
[0023] In some embodiments, before performing extended Kalman filtering on the Kalman equation, the method further includes linearizing the Kalman equation, including:
[0024] Q k The variation range [0, Q max ] is divided into N subintervals, where the starting and ending points of the nth subinterval are and Q max The maximum battery capacity;
[0025] In satisfying In the subinterval of , the linearized equation can be expressed as:
[0026]
[0027] Among them, V ocv is the open circuit voltage of the battery, which is a known value determined by measurement; is a known value, l k Represents in the sub-interval The slope of change obtained by first-order linearization of the function f(·).
[0028] In some embodiments, based on the linearization, an extended Kalman filter is performed, including:
[0029] Initialize the state variable Q k , V′ k and Δt k , where the crystal deviation Δt k The initialization value of is 0;
[0030] Based on the estimated value Q k and the maximum battery capacity Q max , calculate soc k =Q k / Q max ;
[0031] Through the soc k Look up the table to get the battery parameters R0, R1 and C1;
[0032] Based on the state update equation, we get the prior estimate of the state variable Q k+1,pri , V′ k+1，pri and Δt k+1,pri ;
[0033] Update the state variable covariance matrix;
[0034] Adjust the state variable covariance matrix, including: Let cov{Δt k}=cov{Δt k}+D t , cov{Δt k} represents the variance of the crystal oscillator deviation, D t represents the variance adjustment value;
[0035] Calculate the constructed value of the battery voltage based on the observation equation Based on the observed value V k and The error between them updates the prior estimate of the state variable and obtains the extended Kalman filter estimation result Q k+1 , V′ k+1 and Δt k+1 ;
[0036] Let Q k =Q k+1 , V′ k =V′ k+1 and Δt k =Δt k+1 , repeat the above calculation soc k And the process of performing extended Kalman filtering.
[0037] In a second aspect, an embodiment of the present invention provides a fuel meter chip, comprising: at least one processor; and at least one memory communicatively connected to the processor, wherein: the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the method described in the first aspect or any one of the first aspects.
[0038] The power estimation method and power meter chip provided in this application have at least the following technical effects:
[0039] In the process of extending Kalman filtering, the embodiment of the present application corrects the clock deviation of the power meter chip by adjusting the parameters of the crystal oscillator deviation in the state variable covariance matrix, thereby achieving the effect of improving the accuracy of power estimation. BRIEF DESCRIPTION OF THE DRAWINGS
[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0041] Figure 1 A schematic diagram of a flow chart of a power estimation method provided by an embodiment of the present invention;
[0042] Figure 2 A schematic diagram of a process flow of an extended Kalman filter provided by an embodiment of the present invention;
[0043] Figure 3 A schematic diagram of a battery RC equivalent model provided by an embodiment of the present invention;
[0044] Figure 4 A schematic diagram of a flow chart of a power estimation method provided by an embodiment of the present invention;
[0045] Figure 5 A schematic diagram of the structure of an electric meter chip provided in an embodiment of the present invention. DETAILED DESCRIPTION
[0046] In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.
[0047] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.
[0048] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.
[0049] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.
[0050] Kalman filtering is an efficient recursive filter that predicts the next state of the system based on the current state and uses observed data to correct the prediction to achieve a more accurate estimate. The Kalman filtering process is divided into two steps: prediction and update. The prediction step predicts the state and error covariance based on the model, and the update step adjusts the prediction based on the latest measurement. The advantage of this method is that it can effectively integrate past information and current observations to reduce the uncertainty of the estimate.
[0051] Kalman filter assumes that the system dynamics and observations are linear and the noise is Gaussian, which limits its application scope. Extended Kalman filter is a generalization of Kalman filter, which is used to deal with the state estimation problem of nonlinear systems. It approximates the nonlinear effect by linearizing the nonlinear model at each moment.
[0052] An embodiment of the present invention provides a power estimation method. The method performs power estimation by establishing a Kalman model and an extended Kalman filter to achieve the purpose of correcting the clock deviation in a power estimation chip.
[0053] like Figure 1 As shown, the power estimation method of the embodiment of the present invention includes the steps of establishing a Kalman model and extending the Kalman filter. Figure 2 As shown, the extended Kalman filter specifically includes: initialization, state prediction, crystal oscillator error calibration and state update.
[0054] Figure 3 A schematic diagram of a battery RC equivalent model provided by an embodiment of the present invention. Figure 3 In the battery RC equivalent model shown, V ocv is the open circuit voltage, V ocv There is a nonlinear relationship with SOC, R0 is the battery internal resistance, R1 is the battery polarization internal resistance, and C1 is the equivalent capacitance.
[0055] Combination Figure 3 The battery RC equivalent model shown in FIG. 1 and the power estimation method of the embodiment of the present invention are as follows: Figure 4 As shown, including:
[0056] Step 101: Establish a Kalman equation including crystal oscillator deviation according to the battery RC equivalent model.
[0057] Step 102, performing extended Kalman filtering on the Kalman equation, wherein the process of extended Kalman filtering includes: after updating the state variable covariance matrix, adjusting the parameters related to the crystal oscillator deviation in the state variable covariance matrix.
[0058] Among them, the matrix elements in the state variable covariance matrix include the variance of the crystal oscillator deviation. After updating the state variable covariance matrix, adjusting the parameters about the crystal oscillator deviation in the state variable covariance matrix includes: adjusting the crystal oscillator deviation using the variance adjustment value, and then substituting it into the updated state variable covariance matrix. Among them, the variance adjustment value used for adjusting the parameters about the crystal oscillator deviation in the state variable covariance matrix each time is a fixed value. Alternatively, the variance adjustment value used for adjusting the parameters about the crystal oscillator deviation in the state variable covariance matrix each time is a dynamic adjustment value, that is, the variance adjustment value used for each adjustment can be the same value or a different value, and can be adjusted as needed.
[0059] In the embodiment of the present invention, the parameters related to the crystal oscillator deviation in the state variable covariance matrix are adjusted by using the variance adjustment value to correct the clock deviation of the chip, thereby achieving the effect of improving the power estimation accuracy.
[0060] Combined with the above battery RC equivalent model, the observed variable at the kth moment is defined as: battery voltage V k 、Battery current I k ; The state variable at the kth moment is: battery power Q k , battery capacitor voltage V′ k, crystal deviation Δt k ; Battery parameters are: resistance R0, R1 and capacitor C1; t k Step the crystal clock.
[0061] Combined with the battery RC equivalent model, the Kalman equation including the crystal oscillator deviation is established, including:
[0062] The state equation is: Q k =Q k-1 +I k (t k -Δt k ).
[0063] The state update equation is:
[0064] The observation equation is:
[0065] In the above Kalman equation, Represents state variables y represents the observed variable u represents the control variable u k =I k .
[0066] Represents the state transfer matrix:
[0067] Represents the control matrix:
[0068] Represents the measurement matrix:
[0069] represent and The connection matrix between them; k(Q k ) represents the difference between Q and k The slope value related to the value.
[0070] Battery open circuit voltage V ocv and battery level Q k There is a nonlinear constraint relationship V ocv =f(Q k ). Linearize the Kalman equation, including: k The variation range [0, Q max ] is divided into N subintervals, where the starting and ending points of the nth subinterval are and Q max The maximum battery charge. In the subinterval of , the linearized equation can be expressed as:
[0071]
[0072] Among them, V ocv is the open circuit voltage of the battery, which is a known value determined by measurement; is a known value, l k Represents in the sub-interval The slope of change obtained by first-order linearization of the function f(·).
[0073] Based on the above linearization, an extended Kalman filter is performed, including:
[0074] (a) Initialize the state variable Q k , V′ k and Δt k , where the crystal deviation Δt k The initialization value is 0.
[0075] (b) Based on the estimated value Q k and the maximum battery capacity Q max , calculate soc k =Q k / Q max . Through the soc k Look up the table to get the battery parameters R0, R1 and C1.
[0076] (c) Based on the state update equation, we get the prior estimate of the state variable Q k+1,pri , V′ k+1，pri and Δt k+1,pri ; Update the state variable covariance matrix.
[0077] (d) Adjust the state variable covariance matrix, which includes: Let cov[Δt k}=cov[Δt k}+D t , cov[Δt k} represents the variance of the crystal oscillator deviation, D t Represents the variance correction value.
[0078] (e) Calculate the constructed value of the battery voltage based on the observation equation Based on the observed value V k and The error between them updates the prior estimate of the state variable and obtains the extended Kalman filter estimation result Q k+1 , V′ k+1 and Δt k+1 .
[0079] (f) Let Q k =Qk+1 , V′ k =V′ k+1 and Δt k =Δt k+1 , repeat the above steps (b) to (e).
[0080] In the embodiment of the present invention, the variance adjustment value for adjusting the crystal oscillator deviation is added to the state variable covariance matrix to correct the chip clock deviation and improve the power estimation accuracy. In some examples, the power estimation error of the SOC can be reduced from 0.15 to below 0.03.
[0081] Corresponding to the above-mentioned power estimation method, an embodiment of the present invention provides a power meter chip. Figure 5 As shown, the fuel gauge chip includes: a processor 201, a memory 202 and a communication unit 203. These components communicate through one or more buses. Those skilled in the art can understand that the structure of the image forming device shown in the figure does not constitute a limitation on the embodiment of the present application. It can be a bus structure or a star structure, and can also include more or less components than shown in the figure, or combine certain components, or arrange components differently.
[0082] The communication unit 203 is used to establish a communication channel so that the image forming device can communicate with other devices, receive user data sent by other devices or send user data to other devices.
[0083] The processor 201 is the control center of the fuel meter chip. It uses various interfaces and lines to connect various parts of the entire fuel meter chip. It runs or executes software programs, instructions, and / or modules stored in the memory 202, and calls data stored in the memory to perform various functions of the fuel meter chip and / or process data. The processor 201 can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 201 may include a central processing unit (CPU), a microcontroller Uni (MCU), etc.
[0084] The memory 202 is used to store the execution instructions of the processor 201. The memory 202 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.
[0085] When the execution instructions in the memory 202 are executed by the processor 201 , the power meter chip 200 is enabled to execute the power estimation method in the embodiment of the present invention.
[0086] In a specific implementation, the present application further provides a computer storage medium, wherein the computer storage medium may store a program, and when the program is executed, the program may include some or all of the steps in each embodiment of the power estimation method provided in the present application. The storage medium may be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.
[0087] Those skilled in the art can clearly understand that the technology in the embodiments of the present application can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a disk, an optical disk, etc., including several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application or some parts of the embodiments.
[0088] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the device embodiment and the terminal embodiment, since they are basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.
Claims
1. A method for estimating power, characterized in that: The method comprises: The Kalman equation including the crystal oscillator deviation is established based on the battery RC equivalent model; Performing an extended Kalman filter on the Kalman equation, wherein the process of the extended Kalman filter includes: After the state variable covariance matrix is updated, the parameters related to the crystal oscillator deviation in the state variable covariance matrix are adjusted.
2. The method according to claim 1, characterized in that The state variable covariance matrix includes the variance of the crystal oscillator deviation; After the state variable covariance matrix is updated, the parameters related to the crystal oscillator deviation in the state variable covariance matrix are adjusted, including: After the variance of the crystal oscillator deviation is adjusted using the variance adjustment value, it is re-substituted into the updated state variable covariance matrix.
3. The method according to claim 2, characterized in that The variance adjustment value used in each adjustment is a fixed value; or the variance adjustment value used in each adjustment is a dynamically adjusted value.
4. The method according to claim 1, characterized in that: The Kalman equation including the crystal oscillator deviation is established according to the battery RC equivalent model, including: The state equation is: Q k =Q k-1 +I k (t k -Δt k ); The state update equation is: The observation equation is: in, The observed variable at the kth moment is: battery voltage V k 、Battery current I k ; The state variable at the kth moment is: battery power Q k , battery capacitor voltage V k ′ , crystal deviation Δt k ; Battery parameters are: resistance R0, R1 and capacitor C1; t k Step the crystal clock; Represents state variables y represents the observed variable u represents the control variable u k =I k ; Represents the state transfer matrix: Represents the control matrix: Represents the measurement matrix: represent and The connection matrix between them; k(Q k ) represents the difference between Q and k The slope value related to the value.
5. The method according to claim 4, characterized in that Before performing extended Kalman filtering on the Kalman equation, the method further includes linearizing the Kalman equation, including: Q k The variation range [0, Q max ] is divided into N subintervals, where the starting and ending points of the nth subinterval are and Q max The maximum battery capacity; In satisfying In the subinterval of , the linearized equation can be expressed as: Among them, V ocv is the open circuit voltage of the battery, which is a known value determined by measurement; is a known value, l k Represents in the sub-interval The slope of change obtained by first-order linearization of the function f(·).
6. The method according to claim 5, characterized in that On the basis of the linearization, an extended Kalman filter is performed, including: Initialize the state variable Q k 、V k ′ and Δt k , where the crystal deviation Δt k The initialization value of is 0; Based on the estimated value Q k and the maximum battery capacity Q max , calculate soc k =Q k / Q max ; Through the soc k Look up the table to get the battery parameters R0, R1 and C1; Based on the state update equation, we get the prior estimate of the state variable Q k+1,pri 、V k ′ +1,pri and Δt k+1,pri ; Update the state variable covariance matrix; Adjust the state variable covariance matrix, including: Let cov{Δt k }=cov{Δt k }+D t , cov{Δt k } represents the variance of the crystal oscillator deviation, D r represents the variance adjustment value; Calculate the constructed value of the battery voltage based on the observation equation Based on the observed value V k and The error between them updates the prior estimate of the state variable and obtains the extended Kalman filter estimation result Q k+1 、V k ′ +1 and Δt k+1 ; Let Q k =Q k+1 、V k ′ =V k ′ +1 and Δt k =Δt k+1 , repeat the above calculation soc k And the process of performing extended Kalman filtering.
7. A power meter chip, characterized in that: include: at least one processor; and at least one memory communicatively connected to the processor, wherein: the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the method according to any one of claims 1 to 6.