CN118962469B - Battery cell characteristic analysis method, device and equipment - Google Patents
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Abstract
本申请涉及电池管理技术领域，具体涉及一种电芯特性分析方法、装置、设备和计算机存储介质。该电芯特性分析方法，通过采用自适应算法在线识别电池的等效电路模型参数，动态更新等效电路模型参数，使得获取的模型的初始参数能够精准反应电池的真实状态，通过获取初始参数与预估参数之间的残差值，并根据该残差值通过LM算法对性能参数进行更新迭代，进一步提高了电芯特性分析的准确度，使得预测得到的性能参数能更加准确的反应电芯的真实状态。
The present application relates to the field of battery management technology, and specifically to a method, device, equipment and computer storage medium for analyzing battery characteristics. The battery characteristics analysis method uses an adaptive algorithm to identify the equivalent circuit model parameters of the battery online, dynamically updates the equivalent circuit model parameters, so that the initial parameters of the model obtained can accurately reflect the actual state of the battery, and further improves the accuracy of the battery characteristics analysis by obtaining the residual value between the initial parameters and the estimated parameters, and iterating the performance parameters through the LM algorithm according to the residual value, so that the predicted performance parameters can more accurately reflect the actual state of the battery.
Description
Technical Field
The embodiment of the application relates to the technical field of battery management, in particular to a method, a device, equipment and a computer storage medium for analyzing battery cell characteristics.
Background
With the rapid development of electric vehicles, energy storage systems, and the like, management and monitoring of battery performance have become increasingly important. Existing battery management systems typically estimate battery status via a predetermined model to determine battery characteristics.
The inventor finds that most of the existing battery models are based on ideal experimental conditions, cannot accurately reflect battery performance changes in actual use environments, many battery parameters are preset, the capability of self-adaptive adjustment according to actual use conditions is lacking, the problem that the battery performance changes along with time and the use environments is difficult to deal with is solved, and large errors exist in estimation results. The existing battery management system often cannot monitor and update these changes in real time, affecting accurate estimation of the battery state.
Disclosure of Invention
In view of the above problems, embodiments of the present application provide a method, an apparatus, a device, and a computer storage medium for analyzing a battery cell characteristic, which are used to solve the above technical problems in the prior art.
According to an aspect of an embodiment of the present application, there is provided a method for analyzing characteristics of a battery cell, the method including:
s1, discharging the battery cell with a first discharge current I to obtain a first voltage value V1;
S2, acquiring a second voltage value V2 when the discharge of the battery cell is finished;
S3, obtaining a third voltage value V3 after t1 seconds after the discharge of the battery cell is finished;
S4, obtaining a stable voltage value V4 after discharge is finished;
S5, determining a polarization resistance R1 of the battery cell according to the stable voltage value V4, the second voltage value V2 and the first discharge current I, and determining a time constant tau of the battery cell according to the stable voltage value V4, the third voltage value V3 and the second voltage value V2;
s6, determining performance parameters, damping factors and damping factor update coefficients of the battery cell according to the first discharge current I, the polarization resistor R1 and the time constant tau;
S7, acquiring a first real-time voltage value of the battery cell, and predicting the voltage value of the battery cell according to the discharge response function to generate a first voltage prediction record;
s8, updating the performance parameters through an LM algorithm according to the first residual value and the damping factor;
S9, acquiring a second real-time voltage value of the battery cell, determining a second voltage prediction record of the battery cell according to the updated performance parameter and the discharge response function, and determining a second residual value according to the second real-time voltage value and the second voltage prediction record;
S10, if the square value of the second residual value is smaller than or equal to the square value of the first residual value, the damping factor is reduced according to the damping factor updating coefficient, and if the square value of the second residual value is larger than the square value of the first residual value, the damping factor is amplified according to the damping factor updating coefficient;
And S11, repeatedly executing the steps S7 to S10 to iteratively update the performance parameters of the battery cell according to the processed damping factor and the updated performance parameters until the performance parameters meet preset convergence conditions.
Preferably, the determining the discharge response function corresponding to the battery cell according to the performance parameter includes:
determining a discharge response function by the following formula :
;
Wherein, b=ir1,。
Preferably, the updating the performance parameter by the LM algorithm according to the first residual value and the damping factor includes:
calculating the partial derivative of the first residual value on the performance parameter to form a jacobian matrix;
Generating a Hessian matrix and a gradient vector according to the jacobian matrix;
And updating the performance parameters according to the Hessian matrix, the gradient vector and the damping factor.
Preferably, said calculating a partial derivative of said first residual value with respect to said performance parameter forms a jacobian matrix, comprising:
The jacobian matrix is determined by the following formula :
;
Wherein, ,b=IR1,。
Preferably, updating the performance parameter according to the Hessian matrix, the gradient vector and the damping factor comprises:
Updating the performance parameters by the following formula:
;
Wherein the method comprises the steps of Hessian matrix for the current performance parameter vectorGradient vectorR, wherein r is a first residual value,Is a damping factor;
current performance parameters: ;
Updated performance parameters: ;
the matrix gradient operation is used for obtaining:
;
the updated performance parameters are:
;
。
Preferably, the update coefficient of the damping factor according to the damping factor updates the damping factor Performing a reduction process, including:
;
the amplifying the damping factor according to the damping factor updating coefficient comprises the following steps:
;
Wherein, The coefficients are updated for the damping factor,Is a positive integer greater than 1.
Preferably, the preset convergence condition is that the first residual value and the second residual value are smaller than a preset residual threshold value, or that the difference between the performance parameter and the updated performance parameter is smaller than a preset amplitude threshold value.
According to an aspect of the embodiment of the present application, there is also provided a device for analyzing characteristics of a battery cell, including:
The parameter acquisition module is used for discharging the battery cell with a first discharge current I to acquire a first voltage value V1, acquiring a second voltage value V2 when the discharge of the battery cell is finished, acquiring a third voltage value V3 after t1 seconds of the discharge of the battery cell is finished, acquiring a stable voltage value V4 after the discharge is finished, determining a polarization resistance R1 of the battery cell according to the stable voltage value V4, the second voltage value V2 and the first discharge current I, determining a time constant tau of the battery cell according to the stable voltage value V4, the third voltage value V3 and the second voltage value V2, determining a performance parameter, a damping factor and a damping factor updating coefficient of the battery cell according to the first discharge current I, the polarization resistance R1 and the time constant tau, and determining a discharge response function corresponding to the battery cell according to the performance parameter;
The first residual value determining module is used for obtaining a first real-time voltage value of the battery cell, predicting the voltage value of the battery cell according to the discharge response function and generating a first voltage prediction record;
The performance parameter updating module is used for updating the performance parameters through an LM algorithm according to the first residual value and the damping factor;
The second residual value determining module is used for obtaining a second real-time voltage value of the battery cell, determining a second voltage prediction record of the battery cell according to the updated performance parameter and the discharge response function, and determining a second residual value according to the second real-time voltage value and the second voltage prediction record;
And the damping factor updating module is used for judging whether the performance parameter meets a preset convergence condition, if not, reducing the damping factor according to the damping factor updating coefficient when the square value of the second residual value is smaller than the square value of the first residual value, and amplifying the damping factor according to the damping factor updating coefficient when the square value of the second residual value is larger than the square value of the first residual value.
According to a third aspect of the embodiment of the present application, there is also provided a battery cell characteristic analysis apparatus, including a processor, a memory, a communication interface, and a communication bus, where the processor, the memory, and the communication interface complete communication with each other through the communication bus;
The memory is configured to store at least one executable instruction, where the executable instruction causes the processor to perform the method for analyzing a characteristic of a battery cell according to any one of the above embodiments.
According to a fourth aspect of the embodiments of the present application, there is also provided a computer-readable storage medium having stored therein at least one executable instruction that, when executed on a cell characteristic analysis device, causes the cell characteristic analysis device to perform the cell characteristic analysis method according to any one of the above embodiments.
As can be seen from the foregoing, the embodiments of the present application provide a method, an apparatus, and a device for analyzing a battery cell characteristic, which dynamically update parameters of an equivalent circuit model of a battery by adopting an adaptive algorithm to identify the parameters of the equivalent circuit model on line, so that initial parameters of an obtained model can accurately reflect a real state of the battery, and simultaneously, by obtaining a residual value between the initial parameters and estimated parameters, and updating and iterating performance parameters according to the residual value by an LM algorithm, accuracy of the battery cell characteristic analysis is further improved, so that the predicted performance parameters can more accurately reflect the real state of the battery cell. Further, in the process of analyzing the battery cell characteristics, the method for updating the damping factor is determined by judging the square value of the first residual value and the square value of the second residual value, so that the process of predicting the battery cell performance parameters by adopting the prediction model is more targeted, and the prediction model can have higher convergence rate and better calculation stability.
The foregoing description is only an overview of the technical solutions of the embodiments of the present application, and may be implemented according to the content of the specification, so that the technical means of the embodiments of the present application can be more clearly understood, and the following specific embodiments of the present application are given for clarity and understanding.
Drawings
The drawings are only for purposes of illustrating embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
fig. 1 is a schematic flow chart of a method for analyzing characteristics of a battery cell according to an embodiment of the present application;
Fig. 2 is a schematic diagram of an equivalent circuit of a battery cell according to an embodiment of the present application;
FIG. 3 is a schematic diagram of voltage response at a cell excitation current according to an embodiment of the present application;
FIG. 4 is a functional block diagram of a device for analyzing characteristics of a battery cell according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a device for analyzing characteristics of a battery cell according to an embodiment of the present application.
Detailed Description
Exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein.
The battery core is a core part of the battery, and the battery core performance analysis plays a very important role in battery management, so that the battery can be ensured to run safely, the service life of the battery can be prolonged, the performance and efficiency of the battery can be optimized, and real-time monitoring, fault prediction and the like can be realized through the battery core performance analysis. Cell performance analysis typically includes analyzing the capacity, voltage, current, impedance, etc. of the battery to determine the state of health of the cell, so that the battery management system can manage the battery based on the state of health of the cell.
The inventor finds that the existing battery cell parameter identification technology often adopts a static model method, and cannot adapt to the parameter change of a battery cell in the actual use process, such as aging effect, temperature change and the like, so that the accuracy of a parameter identification result is insufficient. Moreover, these methods have a relatively large dependence on the initial model parameters, and incorrect initial parameter settings may lead to inaccurate estimation results.
In view of this, an embodiment of the present application provides a method, an apparatus, and a device for analyzing a battery cell characteristic, where the method for analyzing a battery cell characteristic provided by the embodiment of the present application uses an adaptive algorithm to identify parameters of an equivalent circuit model of a battery online, and dynamically update the parameters of the equivalent circuit model, so that initial parameters of an obtained model can accurately reflect a real state of the battery, and simultaneously, by obtaining a residual value between the initial parameters and an estimated parameter, and updating and iterating a performance parameter according to the residual value by an LM algorithm, accuracy of the battery cell characteristic analysis is further improved, so that the predicted performance parameter can more accurately reflect the real state of the battery cell. Further, in the process of analyzing the battery cell characteristics, the method for updating the damping factor is determined by judging the square value of the first residual value and the square value of the second residual value, so that the process of predicting the battery cell performance parameters by adopting the prediction model is more targeted, and the prediction model can have higher convergence rate and better calculation stability.
As shown in fig. 1, a flow chart of a method for analyzing characteristics of a battery cell according to an embodiment of the present application is provided, and the method includes the following steps:
s1, discharging the battery cell with a first discharge current I to obtain a first voltage value V1;
S2, acquiring a second voltage value V2 when the discharge of the battery cell is finished;
S3, obtaining a third voltage value V3 after t1 seconds after the discharge of the battery cell is finished;
S4, obtaining a stable voltage value V4 after discharge is finished;
S5, determining a polarization resistance R1 of the battery cell according to the stable voltage value V4, the second voltage value V2 and the first discharge current I, and determining a time constant tau of the battery cell according to the stable voltage value V4, the third voltage value V3 and the second voltage value V2;
in steps S1-S5, a process of acquiring initial parameters of the battery cell is shown. In the embodiment of the application, in order to accurately acquire the initial parameters of the battery cell, the parameters of the equivalent circuit model of the battery are identified on line by adopting an adaptive algorithm, as shown in fig. 2, which is a first-order RC equivalent circuit of the battery cell shown in the embodiment of the application, wherein R0 is the internal resistance of the battery cell, R1 is the polarization resistance, and a C1 capacitor represents the capacitance of the battery cell. In the first-order RC equivalent circuit, when the battery cell is charged, the capacitor is charged, and the voltage is gradually increased until the capacitor is fully charged. The current in the circuit will then gradually decrease because the capacitor no longer sinks current. Therefore, the current in the circuit is larger at the beginning of capacitor charging, the capacitor charging is gradually reduced to half current, and finally the current is almost zero when the capacitor is fully charged. The voltage within the capacitor varies over time. In the first-order RC equivalent circuit, the time constant τ represents the constant of the time course of the transient reaction, and means the time required for the physical quantity to decay from the maximum value to 1/e of the maximum value. The first-order RC equivalent circuit can be used for describing the charging and discharging processes of the lithium battery and analyzing the dynamic response and steady-state response of the lithium battery.
Fig. 3 shows a response curve of voltages in a first-order RC equivalent circuit, and shows a change situation of voltages at each stage in a process of discharging a battery cell with a first discharge current I.
In fig. 2 and 3, V is a detection voltage, and I is a first discharge current, i.e., an excitation current. When the battery cell is discharged by the first discharge current I, the voltage of the battery cell is obviously reduced immediately after the discharge is started, and after the discharge lasts for N seconds, N is preferably greater than 100 seconds, so that the capacitor C can be ensured to be fully charged, the recorded voltage V1 is a first voltage value, namely the first voltage V1 is the voltage before the discharge of the battery cell is ended, and as can be seen from fig. 3, at this time, V1 is the lowest voltage.
With continued reference to fig. 3, after the discharge current is finished, the voltage V2 at this time is recorded because the capacitor C is in a fully charged state and the voltage is in a state of rising back. After t1 seconds from the end of the cell discharge, t1 is preferably 100 seconds, and the third voltage V3 is recorded. When the cell discharge is in a stable state after the end, and the detection voltage is not changed greatly, a stable voltage value V4 is recorded.
According to the first-order RC equivalent circuit of FIG. 2, when the first discharge current I disappears, the voltage variation full response function is:
(equation one);
(formula II);
(equation three);
(equation four);
from formulas three and four, we can get:
,
I.e. time constant The method comprises the following steps:
(equation five);
Where t1 is the discharge end time.
The polarization resistance R1 is:
(formula six);
When the battery system runs the HPPC mixed pulse power characteristic test working condition, after each excitation pulse is finished, voltage and current data are obtained according to the steps, and all parameters required by the first-order RC equivalent model can be obtained.
According to the embodiment of the application, the time constant of the battery cell is identified on line by analyzing the response curve of the voltage, and in this way, the model parameters can be dynamically adjusted according to the real-time data, the time constant is dynamically updated, and the identification efficiency of the performance parameters is improved.
S6, determining performance parameters, damping factors and damping factor update coefficients of the battery cell according to the first discharge current I, the polarization resistor R1 and the time constant tau, and determining a discharge response function corresponding to the battery cell according to the performance parameters;
In the steps S1-S5, after determining the polarization resistor R1 and the time constant τ according to the first-order RC equivalent circuit, it is further necessary to determine the performance parameters, the damping factor, and the damping factor update coefficient of the battery cell according to the polarization resistor R1 and the time constant τ.
In the embodiment of the present application, in order to facilitate the use of the above-mentioned cell performance parameters in the subsequent model, the polarization resistor R1 and the time constant τ need to be converted to obtain the performance parameters a and b, which can be known according to the formula one:
RC discharge response function The method comprises the following steps:
(equation seven);
Wherein, ,T is the sampling time;
according to formula seven, the discharge response function The transformation is as follows:
(equation eight);
the damping factor is an empirical value obtained by estimating the performance parameter measured by the first-order RC equivalent circuit, and since the damping factor is updated in the subsequent prediction model, the initial damping factor can be obtained by empirical estimation at this stage, and the accuracy of the subsequent estimation of the performance parameter is not affected. The damping factor update coefficient For a subsequent update of the damping factor, a predetermined coefficient, the determination of which has an influence on the number of iterations of the predictive model, preferably,Is a positive integer greater than 1. Preferably, the initial damping factorSet to 10, the damping factor updates the coefficientSet to 10.
S7, acquiring a first real-time voltage value of the battery cell, and predicting the voltage value of the battery cell according to the discharge response function to generate a first voltage prediction record;
In the above step, after the initial performance parameters of the battery cells are determined by the first-order RC equivalent circuit model, according to the above formula one, the data record of the first real-time voltage value with an interval of 1 second is obtained after the end of charging and discharging, as shown in the following table 1, the theoretical parameters τ=10, r1=0.0005, i=100 a, b=ir1=0.02, and the actual voltage values obtained by a= -1/τ= -0.01 are:
TABLE 1
Meanwhile, predicting the voltage value of the battery cell according to a formula eight to generate a first voltage prediction record: . From the first real-time voltage value and the first voltage prediction record in Table 1, a first residual value between the first real-time voltage value and the first voltage prediction record can be obtained For each data point i, a residual between the predicted value and the actual recorded value of the discharge response function is calculated according to the following formula nine.
(Equation nine);
Wherein, The voltage change values at the respective time points shown in Table 1。
S8, updating the performance parameters through an LM algorithm according to the first residual value and the damping factor;
The LM algorithm is an optimization algorithm for the nonlinear least squares problem. It is possible to quickly and accurately find the optimal parameters that minimize the loss function by combining the advantages of the gauss-newton method and the gradient descent method. The LM algorithm controls the behavior between gaussian-newton and gradient descent through a damping factor λ during the iteration process. When the damping factor is greater than a certain value, the LM algorithm is similar to Gaussian-Newton method, and when the damping factor is less than a certain value, the LM algorithm tends to gradient descent method. According to the embodiment of the application, the LM algorithm is introduced into the analysis of the characteristics of the battery cells, so that the characteristics of the LM algorithm can be fully utilized, the analysis of the characteristics of the battery cells can be quickly converged, and the stability of calculation can be maintained.
As can be seen from the above step S7, the first residual value isDamping factor isCalculating the partial derivatives of the first residual values on the performance parameters to form a jacobian matrix, and calculating the partial derivatives of the model on the performance parameters a and b for each data point t to obtain the following jacobian matrix:
Wherein ;
Generating a Hessian matrix and gradient vectors according to the jacobian matrix to obtain the Hessian matrixAnd gradient vectorR, wherein r is the first residual value in the above steps.
Updating the performance parameters according to the Hessian matrix, the gradient vector and the damping factor by the following formula ten:
(equation ten);
Wherein the method comprises the steps of Hessian matrix for the current performance parameter vectorGradient vectorR, wherein r is a first residual value,Is a damping factor;
current performance parameters: ;
Updated performance parameters: ;
the matrix gradient operation is used for obtaining:
;
the updated performance parameters are:
;
。
S9, acquiring a second real-time voltage value of the battery cell, determining a second voltage prediction record of the battery cell according to the updated performance parameter and the discharge response function, and determining a second residual value according to the second real-time voltage value and the second voltage prediction record;
after the updated performance parameters are obtained, a second voltage prediction record is generated in a mode in the step S7, and a residual error determined according to the updated performance parameters is generated through a formula nine according to the acquired second real-time voltage value Wherein。
S10, if the square value of the second residual value is smaller than or equal to the square value of the first residual value, the damping factor is reduced according to the damping factor updating coefficient, and if the square value of the second residual value is larger than the square value of the first residual value, the damping factor is amplified according to the damping factor updating coefficient;
as shown in fig. 1, determining whether the square value of the second residual value is greater than the square value of the first residual value, if the square value of the second residual value is less than or equal to the square value of the first residual value, indicating that the error is decreasing, executing step S101, i.e. updating the coefficient according to the damping factor The damping factor is setThe reduction treatment is performed so as to approach newton's method. Preferably, in an embodiment of the present application, the following will be providedI.e. the damping factor is reducedMultiple to reduce the step size of the iteration.
When the square value of the second residual value is greater than the square value of the first residual value, the error is larger, step S102 is executed, i.e. the coefficient is updated according to the damping factorThe damping factor is setAnd (5) performing amplification treatment to approach the gradient descent method. Preferably, in an embodiment of the present application, the following will be providedI.e. amplifying the damping factorMultiple times to increase the step size of the iteration.
According to the embodiment of the application, by introducing a strategy for dynamically adjusting the damping factor lambda according to the residual value, efficient and stable parameter estimation is realized in nonlinear least square optimization, and compared with the traditional method, the LM algorithm provided by the embodiment of the application provides better convergence speed and calculation stability.
And S11, repeatedly executing the steps S7 to S10 to iteratively update the performance parameters of the battery cell according to the processed damping factor and the updated performance parameters until the performance parameters meet preset convergence conditions.
With continued reference to fig. 1, whether the updated performance parameter meets a preset convergence condition is determined, where the preset convergence condition may be that the first residual value and the second residual value are smaller than a preset residual threshold, and when the first residual value or the second residual value is smaller than the preset residual threshold, it is indicated that the actual voltage value and the predicted voltage value are relatively close, and if the difference between the actual voltage value and the predicted voltage value is not large, step S12 may be executed, that is, the cell characteristic analysis flow is ended, and updating of the performance parameter is stopped. Otherwise, continuing to execute the steps S7 to S10, and continuing to iteratively update the performance parameters of the battery cell.
Optionally, the preset convergence condition may be that a difference between the performance parameter and the updated performance parameter is smaller than a preset amplitude threshold, and when the difference between the performance parameter and the updated performance parameter is smaller than the preset amplitude threshold, it is indicated that the performance parameter is not changed greatly, and step S12 may be executed, that is, the process of analyzing the cell characteristics is ended, and updating of the performance parameter is stopped. Otherwise, continuing to execute the steps S7 to S10, and continuing to iteratively update the performance parameters of the battery cell.
As shown in table 2 below, the performance parameters after multiple iterations are performed based on the data of table 1:
TABLE 2
As can be seen from table 2, after 6 iterations, the predicted performance parameter a= -0.1010, b=0.0499, according to,The solution gave a predicted time constant of τ=9.9 and polarization resistance of r1= 0.0004999. In table 1, theoretical parameter τ=10 and r1=0.0005, and compared with the theoretical parameter τ=10, the predicted performance parameter is very close to the theoretical performance parameter, which also proves the accuracy of the cell characteristic analysis method provided by the embodiment of the application.
In summary, according to the method for analyzing the characteristics of the battery cell provided by the embodiment of the application, the parameters of the equivalent circuit model are dynamically updated by adopting the parameters of the first-order RC equivalent circuit model, so that the obtained initial parameters of the model can accurately reflect the real state of the battery, and meanwhile, the accuracy of analyzing the characteristics of the battery cell is further improved by obtaining the residual value between the initial parameters and the estimated parameters and updating and iterating the performance parameters through the LM algorithm according to the residual value, so that the predicted performance parameters can more accurately reflect the real state of the battery cell. Further, in the process of analyzing the battery cell characteristics, the method for updating the damping factor is determined by judging the square value of the first residual value and the square value of the second residual value, so that the process of predicting the battery cell performance parameters by adopting the prediction model is more targeted, and the prediction model can have higher convergence rate and better calculation stability.
According to another aspect of the embodiment of the present application, as shown in fig. 4, an embodiment of the present application further provides a cell characteristic analysis apparatus 100, configured to perform the cell characteristic analysis method set forth in any of the foregoing embodiments, where the cell characteristic analysis apparatus 100 includes a parameter obtaining module 101, a first residual value determining module 102, a performance parameter updating module 103, a second residual value determining module 104, and a damping factor updating module 105.
The parameter obtaining module 101 is configured to obtain a first voltage value V1 by discharging the battery cell with a first discharge current I, obtain a second voltage value V2 when the discharge of the battery cell is completed, obtain a third voltage value V3 after t1 seconds of the discharge of the battery cell is completed, obtain a stable voltage value V4 after the discharge is completed, determine a polarization resistance R1 of the battery cell according to the stable voltage value V4, the second voltage value V2 and the first discharge current I, determine a time constant τ of the battery cell according to the stable voltage value V4, the third voltage value V3 and the second voltage value V2, determine a performance parameter, a damping factor and a damping factor update coefficient of the battery cell according to the first discharge current I, the polarization resistance R1 and the time constant τ, and determine a discharge response function corresponding to the battery cell according to the performance parameter;
The first residual value determining module 102 is configured to obtain a first real-time voltage value of the battery cell, predict the voltage value of the battery cell according to the discharge response function, and generate a first voltage prediction record;
The performance parameter updating module 103 is configured to update the performance parameter through an LM algorithm according to the first residual value and the damping factor;
The second residual value determining module 104 is configured to obtain a second real-time voltage value of the battery cell, determine a second voltage prediction record of the battery cell according to the updated performance parameter and the discharge response function, and determine a second residual value according to the third voltage value and the second voltage prediction record;
The damping factor updating module 105 is configured to determine whether the performance parameter meets a preset convergence condition, if the performance parameter does not meet the preset convergence condition, reduce the damping factor according to the damping factor updating coefficient when the square value of the second residual value is smaller than the square value of the first residual value, and amplify the damping factor according to the damping factor updating coefficient when the square value of the second residual value is greater than the square value of the first residual value.
The embodiment of the application also provides a battery cell characteristic analysis device, and particularly referring to fig. 5, fig. 5 shows a schematic structural diagram of the battery cell characteristic analysis device.
As shown in fig. 5, the object detection determination device may include a processor 602, a memory 606, a communication interface 604, and a communication bus 608.
Wherein the processor 602, the memory 606, and the communication interface 604 perform communication with each other via a communication bus 608. The memory 606 is used for storing at least one program 610, and the program 610 causes the processor 602 to perform the relevant steps in the embodiment of the method for analyzing the characteristics of a battery cell as described above.
In particular, program 610 may include program code comprising computer-executable instructions.
The processor 602 may be a central processing unit CPU, or an Application-specific integrated Circuit ASIC (Application SPECIFIC INTEGRATED Circuit), or one or more integrated circuits configured to implement embodiments of the present application. The one or more processors included in the cell characteristic analysis device may be the same type of processor, such as one or more CPUs, or may be different types of processors, such as one or more CPUs and one or more ASICs.
Memory 606 for storing program 610. The memory 606 may comprise high-speed RAM memory or may further comprise non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 610 may be specifically invoked by the processor 602 to cause the cell characteristic analysis device to perform the above-described cell characteristic analysis method embodiment to provide any operation method, and specifically includes the following operations:
s1, discharging the battery cell with a first discharge current I to obtain a first voltage value V1;
S2, acquiring a second voltage value V2 when the discharge of the battery cell is finished;
S3, obtaining a third voltage value V3 after t1 seconds after the discharge of the battery cell is finished;
S4, obtaining a stable voltage value V4 after discharge is finished;
S5, determining a polarization resistance R1 of the battery cell according to the stable voltage value V4, the second voltage value V2 and the first discharge current I, and determining a time constant tau of the battery cell according to the stable voltage value V4, the third voltage value V3 and the second voltage value V2;
s6, determining performance parameters, damping factors and damping factor update coefficients of the battery cell according to the first discharge current I, the polarization resistor R1 and the time constant tau;
S7, acquiring a first real-time voltage value of the battery cell, and predicting the voltage value of the battery cell according to the discharge response function to generate a first voltage prediction record;
s8, updating the performance parameters through an LM algorithm according to the first residual value and the damping factor;
S9, acquiring a second real-time voltage value of the battery cell, determining a second voltage prediction record of the battery cell according to the updated performance parameter and the discharge response function, and determining a second residual value according to the second real-time voltage value and the second voltage prediction record;
S10, if the square value of the second residual value is smaller than or equal to the square value of the first residual value, the damping factor is reduced according to the damping factor updating coefficient, and if the square value of the second residual value is larger than the square value of the first residual value, the damping factor is amplified according to the damping factor updating coefficient;
And S11, repeatedly executing the steps S7 to S10 to iteratively update the performance parameters of the battery cell according to the processed damping factor and the updated performance parameters until the performance parameters meet preset convergence conditions.
The embodiment of the application also provides a computer readable storage medium, and executable instructions are stored in the storage medium, and when the executable instructions run on the cell characteristic analysis equipment, the cell characteristic analysis equipment executes the cell characteristic analysis method provided in any embodiment.
The embodiment of the application also provides a battery cell characteristic analysis program, which is used for executing the battery cell characteristic analysis method provided by the embodiment.
The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general-purpose systems may also be used with the teachings herein. The required structure for a construction of such a system is apparent from the description above. In addition, embodiments of the present application are not directed to any particular programming language. It will be appreciated that the teachings of the present application described herein may be implemented in a variety of programming languages, and the above description of specific languages is provided for disclosure of enablement and best mode of the present application.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the above description of exemplary embodiments of the application, various features of the embodiments of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component, and they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all of the features disclosed in this specification (including the accompanying abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including the accompanying abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the application. The steps in the above embodiments should not be construed as limiting the order of execution unless specifically stated.
Claims (10)
1. A method of analyzing characteristics of a cell, comprising:
s1, discharging the battery cell with a first discharge current I to obtain a first voltage value V1;
S2, acquiring a second voltage value V2 when the discharge of the battery cell is finished;
S3, obtaining a third voltage value V3 after t1 seconds after the discharge of the battery cell is finished;
S4, obtaining a stable voltage value V4 after discharge is finished;
S5, determining a polarization resistance R1 of the battery cell according to the stable voltage value V4, the second voltage value V2 and the first discharge current I, and determining a time constant tau of the battery cell according to the stable voltage value V4, the third voltage value V3 and the second voltage value V2;
s6, determining performance parameters, damping factors and damping factor update coefficients of the battery cell according to the first discharge current I, the polarization resistor R1 and the time constant tau;
S7, acquiring a first real-time voltage value of the battery cell, and predicting the voltage value of the battery cell according to the discharge response function to generate a first voltage prediction record;
s8, updating the performance parameters through an LM algorithm according to the first residual value and the damping factor;
S9, acquiring a second real-time voltage value of the battery cell, determining a second voltage prediction record of the battery cell according to the updated performance parameter and the discharge response function, and determining a second residual value according to the second real-time voltage value and the second voltage prediction record;
S10, if the square value of the second residual value is smaller than or equal to the square value of the first residual value, the damping factor is reduced according to the damping factor updating coefficient, and if the square value of the second residual value is larger than the square value of the first residual value, the damping factor is amplified according to the damping factor updating coefficient;
And S11, repeatedly executing the steps S7 to S10 to iteratively update the performance parameters of the battery cell according to the processed damping factor and the updated performance parameters until the performance parameters meet preset convergence conditions.
2. The method of claim 1, wherein determining a discharge response function corresponding to the cell according to the performance parameter comprises:
determining a discharge response function by the following formula :
;
Wherein, b=ir1,T is the sampling time.
3. The method of claim 1, wherein updating the performance parameter by the LM algorithm based on the first residual value and the damping factor comprises:
calculating the partial derivative of the first residual value on the performance parameter to form a jacobian matrix;
Generating a Hessian matrix and a gradient vector according to the jacobian matrix;
And updating the performance parameters according to the Hessian matrix, the gradient vector and the damping factor.
4. The method of claim 3, wherein said calculating partial derivatives of said first residual values with respect to said performance parameters to form a jacobian matrix comprises:
The jacobian matrix is determined by the following formula :
;
Wherein, ,b=IR1,T is the sampling time.
5. The method of claim 4, wherein updating the performance parameters based on the Hessian matrix, the gradient vector, and the damping factor comprises:
Updating the performance parameters by the following formula:
;
Wherein the method comprises the steps of Hessian matrix for the current performance parameter vectorGradient vectorR, wherein r is a first residual value,Is a damping factor;
current performance parameters: ;
Updated performance parameters: ;
the matrix gradient operation is used for obtaining:
;
the updated performance parameters are:
;
。
6. the method for analyzing battery characteristics according to claim 5, wherein,
The damping factor is updated according to the damping factor updating coefficientPerforming a reduction process, including:
;
the amplifying the damping factor according to the damping factor updating coefficient comprises the following steps:
;
Wherein, The coefficients are updated for the damping factor,Is a positive integer greater than 1.
7. The method of claim 1, wherein the predetermined convergence condition is that the first residual value and the second residual value are smaller than a predetermined residual threshold, or that a difference between the performance parameter and the updated performance parameter is smaller than a predetermined amplitude threshold.
8. A cell characteristic analysis device, comprising:
The parameter acquisition module is used for discharging the battery cell with a first discharge current I to acquire a first voltage value V1, acquiring a second voltage value V2 when the discharge of the battery cell is finished, acquiring a third voltage value V3 after t1 seconds of the discharge of the battery cell is finished, acquiring a stable voltage value V4 after the discharge is finished, determining a polarization resistance R1 of the battery cell according to the stable voltage value V4, the second voltage value V2 and the first discharge current I, determining a time constant tau of the battery cell according to the stable voltage value V4, the third voltage value V3 and the second voltage value V2, determining a performance parameter, a damping factor and a damping factor updating coefficient of the battery cell according to the first discharge current I, the polarization resistance R1 and the time constant tau, and determining a discharge response function corresponding to the battery cell according to the performance parameter;
The first residual value determining module is used for obtaining a first real-time voltage value of the battery cell, predicting the voltage value of the battery cell according to the discharge response function and generating a first voltage prediction record;
The performance parameter updating module is used for updating the performance parameters through an LM algorithm according to the first residual value and the damping factor;
The second residual value determining module is used for obtaining a second real-time voltage value of the battery cell, determining a second voltage prediction record of the battery cell according to the updated performance parameter and the discharge response function, and determining a second residual value according to the second real-time voltage value and the second voltage prediction record;
And the damping factor updating module is used for judging whether the performance parameter meets a preset convergence condition, if not, reducing the damping factor according to the damping factor updating coefficient when the square value of the second residual value is smaller than the square value of the first residual value, and amplifying the damping factor according to the damping factor updating coefficient when the square value of the second residual value is larger than the square value of the first residual value.
9. The battery cell characteristic analysis equipment is characterized by comprising a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface are communicated with each other through the communication bus;
The memory is configured to store at least one executable instruction that causes the processor to perform the cell characteristic analysis method according to any one of claims 1-7.
10. A computer readable storage medium, characterized in that at least one executable instruction is stored in the storage medium, which executable instruction, when run on a cell characteristic analysis device, causes the cell characteristic analysis device to perform the cell characteristic analysis method according to any one of claims 1-7.
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