CN114966408A - Power battery online parameter identification method, device and equipment and manned aircraft - Google Patents
Power battery online parameter identification method, device and equipment and manned aircraft Download PDFInfo
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Abstract
The application relates to a power battery online parameter identification method, a power battery online parameter identification device, power battery online parameter identification equipment and a manned aircraft. The power battery online parameter identification method comprises the following steps: establishing a second-order RC equivalent circuit model corresponding to the battery; identifying a first time scale parameter of a first-order circuit stage of the second-order RC equivalent circuit model by adopting a first preset algorithm; identifying a second time scale parameter of a second-order circuit stage of the second-order RC equivalent circuit model by adopting a second preset algorithm; and inputting the first time scale parameter of the first-order circuit stage and the second time scale parameter of the second-order circuit stage obtained after identification into the second-order RC equivalent circuit model to obtain the updated first time scale parameter and second time scale parameter. The scheme provided by the application can improve the identification precision of the online parameter identification of the power battery and improve the calculation efficiency.
Description
Technical Field
The application relates to the technical field of power battery system management, in particular to a power battery online parameter identification method, a power battery online parameter identification device, power battery online parameter identification equipment and a manned aircraft.
Background
As a new power source, the power battery has been widely used in electric vehicles, unmanned aerial vehicles, and other manufacturing industries using green power energy. The power battery is generally formed by connecting a plurality of battery monomers in series, the structure enables the voltage of the power battery pack to be high, the structure to be complex, and in the using process, the performance of the power battery can be influenced due to the influence of various factors.
In the using process of the power battery, the power battery needs to be monitored in real time, so that the phenomena of overcharge, overdischarge, over-temperature and the like of the power battery in use are prevented, the performance of the battery is further reduced, and the service life and the use safety of the battery are influenced.
In the related art, the State of Charge (SOC), the State of Health (SOH), and the State of Power (SOP) of the Power battery are generally estimated to realize real-time monitoring of the State of the Power battery. The parameter identification plays a vital role in the state estimation of the power battery, and the accurate parameter identification of the power battery has important significance in the aspects of battery state estimation, whole machine endurance, whole machine performance experience, battery gradient use, active safety early warning of the battery and the like.
However, in the using process of the power battery, complex working conditions generally exist, so that parameters of the battery can change along with time, the characteristics of the battery have time-varying property, the traditional parameter identification cannot meet the time-varying property requirement of the battery, the phenomenon of numerical value divergence is easily generated when the complex working conditions occur, the identification precision is influenced, the calculated amount is large, the calculation efficiency is low, and the engineering application is not facilitated.
Disclosure of Invention
In order to solve or partially solve the problems in the related art, the application provides a power battery online parameter identification method, a device, equipment and a manned aircraft, which can improve the identification precision of power battery online parameter identification and improve the calculation efficiency.
The first aspect of the present application provides a method for identifying online parameters of a power battery, including:
establishing a second-order RC equivalent circuit model corresponding to the battery;
identifying a first time scale parameter of a first-order circuit stage of the second-order RC equivalent circuit model by adopting a first preset algorithm;
identifying a second time scale parameter of a second-order circuit stage of the second-order RC equivalent circuit model by adopting a second preset algorithm;
and inputting the first time scale parameter of the first-order circuit stage and the second time scale parameter of the second-order circuit stage obtained after identification into the second-order RC equivalent circuit model to obtain the updated first time scale parameter and second time scale parameter.
In an embodiment, the identifying, by using a first preset algorithm, a first time scale parameter of a first-order circuit stage of the second-order RC equivalent circuit model includes:
and identifying the first time scale parameter of the first-order circuit stage of the second-order RC equivalent circuit model by adopting a recursive least square method based on a variable forgetting factor.
In an embodiment, the identifying the first time scale parameter of the first-order circuit stage of the second-order RC equivalent circuit model by using a recursive least square method based on a variable forgetting factor includes:
initializing parameters of a second-order RC equivalent circuit model;
adaptively estimating a variable forgetting factor;
estimating a set parameter by using the variable forgetting factor and a recursive least square method;
and determining a first time scale parameter of a first-order circuit stage of the second-order RC equivalent circuit model by reverse extrapolation of the estimated setting parameter.
In an embodiment, before the identifying the first time scale parameter of the first-order circuit stage of the second-order RC equivalent circuit model by using the first preset algorithm, the method further includes:
and normalizing the data input into the second-order RC equivalent circuit model.
In an embodiment, the identifying, by using a second preset algorithm, a second time scale parameter of a second-order circuit stage of the second-order RC equivalent circuit model includes:
and identifying a second time scale parameter of a second-order circuit stage of the second-order RC equivalent circuit model by adopting an adaptive extended Kalman filtering algorithm.
In an embodiment, the identifying, by using an adaptive-based extended kalman filtering algorithm, a second time scale parameter of a second-order circuit stage of the second-order RC equivalent circuit model includes:
establishing a state space equation of the polarization resistor in a second-order RC equivalent circuit model;
estimating the error of the measured voltage and the predicted voltage;
estimating a state covariance and a Kalman gain of the state space equation;
updating state noise and measurement noise of the state space equation based on a sliding window;
updating the state covariance and the state variable according to the Kalman gain;
and determining a second time scale parameter of a second-order circuit stage of the second-order RC equivalent circuit model according to a preset parameter constraint condition.
The second aspect of the present application provides an online parameter identification device for power battery, including:
the model equivalent module is used for establishing a second-order RC equivalent circuit model corresponding to the battery;
the first parameter identification module is used for identifying a first time scale parameter of a first-order circuit stage of the second-order RC equivalent circuit model by adopting a first preset algorithm and inputting the first time scale parameter into the second-order RC equivalent circuit model for updating;
the second parameter identification module is used for identifying a second time scale parameter of a second-order circuit stage of the second-order RC equivalent circuit model by adopting a second preset algorithm, and inputting the second time scale parameter into the second-order RC equivalent circuit model for updating;
and the parameter determining module is used for inputting the first time scale parameter of the first-order circuit stage obtained after the first parameter identification module identifies and the second time scale parameter of the second-order circuit stage obtained after the second parameter identification module identifies into the second-order RC equivalent circuit model of the model equivalent module to obtain the updated first time scale parameter and second time scale parameter.
In an embodiment, the first parameter identification module identifies a first time scale parameter of a first-order circuit stage of the second-order RC equivalent circuit model by using a recursive least square method based on a variable forgetting factor; or the like, or, alternatively,
and the second parameter identification module identifies a second time scale parameter of a second-order circuit stage of the second-order RC equivalent circuit model by adopting a self-adaptive extended Kalman filtering algorithm.
The third aspect of the application provides a manned aircraft, which comprises the power battery online parameter identification device.
A fourth aspect of the present application provides a computing device comprising:
a processor; and
a memory having executable code stored thereon, which when executed by the processor, causes the processor to perform the method as described above.
A fifth aspect of the present application provides a computer readable storage medium having stored thereon executable code, which when executed by a processor, causes the processor to perform the method as described above.
The technical scheme provided by the application can comprise the following beneficial effects:
the second-order RC equivalent circuit model corresponding to the battery is established, the first-order circuit stage and the second-order circuit stage in the second-order RC equivalent circuit model are respectively subjected to parameter identification modes with different time scales, parameters with different time scales are separated, distributed calculation is carried out, the calculated amount can be reduced, the calculation efficiency is improved, the identification precision and stability in the parameter identification process are effectively improved, and engineering application is facilitated.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
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The foregoing and other objects, features and advantages of the application will be apparent from the following more particular descriptions of exemplary embodiments of the application as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the application.
Fig. 1 is a schematic flow chart of an online parameter identification method for a power battery according to an embodiment of the present disclosure;
FIG. 2 is a model diagram of a second order RC equivalent model shown in the embodiment of the present application;
fig. 3 is another schematic flow chart of a power battery online parameter identification method according to an embodiment of the present disclosure;
fig. 4 is a schematic diagram of an application framework for identifying parameters of a first-order circuit stage by using a recursive least square method based on a variable forgetting factor according to an embodiment of the present application;
fig. 5 is a schematic view of an application framework of the power battery online parameter identification method according to the embodiment of the application;
fig. 6 is a schematic structural diagram of an online parameter identification device for a power battery according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of a computing device according to an embodiment of the present application.
Detailed Description
Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While embodiments of the present application are illustrated in the accompanying 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. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It should be understood that although the terms "first," "second," "third," etc. may be used herein to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present application. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
In the technical scheme of parameter identification of the power battery in the related art, parameter identification cannot meet battery identification accuracy, is unstable, has large calculation amount and low calculation efficiency, and is not beneficial to engineering application. The application provides a power battery online parameter identification method which can improve identification precision and calculation efficiency of power battery online parameter identification.
The technical solutions of the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a power battery online parameter identification method according to an embodiment of the present application.
Referring to fig. 1, the method includes:
and S101, establishing a second-order RC equivalent circuit model corresponding to the battery.
The battery of the present application may be a power battery. The power battery is mainly used for providing a power source for tools, wherein the tools include vehicles, production machinery and the like, for example, but not limited to, motor train units, electric automobiles, electric motorcycles, electric manned vehicles and the like.
The second-order RC equivalent circuit model corresponding to the battery may be referred to as a second-order RC equivalent model for short. A first order RC equivalent model generally has one RC loop, while a second order RC equivalent model has two series RC loops, wherein the second order RC equivalent model has a first order circuit stage and a second order circuit stage. Compared with a first-order RC equivalent model, the steady-state characteristic and the transient-state characteristic of the battery can be better considered by adopting the second-order RC equivalent model, and the charging and discharging behaviors of the battery under different temperatures and charging and discharging multiplying powers can be simulated with higher model precision. Therefore, in the embodiment of the application, a second-order RC equivalent circuit model corresponding to the battery is established.
S102, identifying a first time scale parameter of a first-order circuit stage of the second-order RC equivalent circuit model by adopting a first preset algorithm.
The step can adopt a recursive least square method based on variable forgetting factors to identify the first time scale parameter of the first-order circuit stage of the second-order RC equivalent circuit model.
The identification of the first time scale parameters of the first order circuit stage of the second order RC equivalent circuit model may be, for example, identification of the first time scale parameters of the first order circuit stage, such as R0, R1, tao 1.
And S103, identifying a second time scale parameter of a second-order circuit stage of the second-order RC equivalent circuit model by adopting a second preset algorithm.
The step can adopt an extended Kalman filtering algorithm based on self-adaption to identify a second time scale parameter of a second-order circuit stage of the second-order RC equivalent circuit model.
The second time scale parameter of the second order circuit stage of the second order RC equivalent circuit model is identified, for example, the second time scale parameter of R2, tao2 of the second order circuit stage is identified.
And S104, inputting the first time scale parameter of the first-order circuit stage and the second time scale parameter of the second-order circuit stage obtained after identification into a second-order RC equivalent circuit model, and obtaining the updated first time scale parameter and second time scale parameter.
After the first time scale parameter and the second time scale parameter are identified, the first time scale parameter of the first-order circuit stage and the second time scale parameter of the second-order circuit stage obtained after identification are input into the second-order RC equivalent circuit model again, the updated first time scale parameter and the updated second time scale parameter can be obtained, and therefore relevant parameters in the second-order RC equivalent circuit model are adjusted in a self-adaptive mode, and the identification accuracy of the second-order RC equivalent circuit model is improved continuously.
It should be noted that, in the present application, there may be no precedence between inputting the obtained first time scale parameter of the first-order circuit stage to the second-order RC equivalent circuit model and inputting the obtained second time scale parameter of the second-order circuit stage to the second-order RC equivalent circuit model. For example, the first time scale parameter may be immediately input to the second order RC equivalent circuit model after the identification of the first time scale parameter is completed, and the second time scale parameter may be immediately input to the second order RC equivalent circuit model after the identification of the second time scale parameter is completed. Through the processing, the timeliness of updating the second-order RC equivalent circuit model can be effectively ensured.
According to the embodiment, the second-order RC equivalent circuit model corresponding to the battery is established, the first-order circuit stage and the second-order circuit stage in the second-order RC equivalent circuit model are respectively subjected to parameter identification modes with different time scales, parameters with different time scales are separated, distributed calculation is carried out, the calculated amount can be reduced, the calculation efficiency is improved, the identification precision and stability in the parameter identification process are effectively improved, and the engineering application is facilitated.
Fig. 3 is another schematic flow chart of a power battery online parameter identification method according to an embodiment of the present application. Compared with the process shown in fig. 1, the process of identifying parameters of different time scales for the second-order RC equivalent circuit model is described in more detail, so that the adaptability and stability of the method to different working conditions are further improved.
Referring to fig. 3, the method includes:
s301, establishing a second-order RC equivalent circuit model corresponding to the battery, and performing discretization processing on the second-order RC equivalent circuit model.
The first-order RC equivalent model can describe the dynamic characteristics of the battery, but cannot accurately simulate the charging and discharging behaviors of the battery at different temperatures and charging and discharging rates. According to the embodiment of the application, a second-order RC equivalent model is adopted, the accuracy of the equivalent model is improved to the maximum by using relatively few parameters, and the second-order RC equivalent model can be seen in figure 2. The principle of parameter identification of the present application is shown in fig. 4, and the terminal voltage of the model is calculated by continuously estimating the terminal voltage according to the battery model through the input parameters
And correcting the parameters of the battery model in real time by the error of the measured voltage U (k) of the battery, and continuously repeating the operation to optimize the error in an unbiased variance minimization manner. The internal reaction process of the battery comprises a charge transfer process with a small time constant and a diffusion process with a large time constant, a first-order circuit stage in the second-order RC equivalent circuit model is a charge transfer link in the battery, and a second-order circuit stage is a diffusion link in the battery.
In the embodiment of the present invention, the parameters such as R0, R1, tao1, R2, tao2, etc. are identified online.
1) Mathematical modeling:
the method comprises the following steps of establishing a mathematical model of a second-order RC equivalent model through mathematical modeling according to a topological structure of the second-order RC equivalent model, wherein the mathematical model of the second-order RC equivalent model is characterized as follows:
U L (k)＝U ocv (k)+I(k)·R 0 +U 1 (k)+U 2 (k)
tao＝R·C
wherein, U L Is terminal voltage, U ocv Is open circuit voltage, U 1 Is a first order characteristic voltage, U 2 Is a second order characteristic voltage, I is a main circuit current, R 0 Is ohmic internal resistance, R 1 、R 2 For polarization internal resistance, tao is time constant, a is coefficient, tao 1 Being a first order time constant, tao 2 Is a second order time constant, C 1 Is a first order capacitance, C 2 Is a second order capacitance of which tao 1 、tao 2 Can respectively pass through R 1 *C 1 And R 2 *C 2 And (5) calculating.
2) Discretization expression based on least recursive two-multiplication:
in order to facilitate subsequent calculation, the second-order RC equivalent circuit model is subjected to discretization processing. The discretization processing refers to segmenting continuous data to enable the data to become a segment discretization interval, and meanwhile, the discretized features have strong robustness on abnormal data, so that the interference of the abnormal data on the model is effectively reduced, the stability of the model is enhanced, and the fast iteration of the second-order RC equivalent circuit model is facilitated.
The discretization of the data may be performed by an equidistant discretization method, an equal-frequency discretization method, a K-means model discretization method, a binary discretization method, a discretization method based on chi-square splitting, and the like, but is not limited thereto. The stability of the model can be effectively improved through discretization processing, and the interference of abnormal data to the mold core is prevented.
For example, the discretization expression based on the recursive least square method can be performed on the second-order RC equivalent circuit model, so that the discretization processing is more matched with the subsequent parameter identification process of the application, and specifically, the discretization expression based on the recursive least square method is performed on the second-order RC equivalent circuit model as follows:
θ(k)＝[U OCV (k),b 1 ,b 2 ,b 3 ,b 4 ,b 5 ] T
E(k)＝U OCV (k)-U L (k)
wherein y (k) is a predicted quantity of system output,
as input parameters of the system (mainly including measuring terminal voltage U) L Bus current I at present and historical time L ) Theta (k) is the parameter to be identified, E (k) is the open-circuit voltage U of the battery OCV And measuring terminal voltage U L The difference, T, represents transpose.
Wherein b1, b2, b3, b4, b5 refer to the coefficients of the polynomial and the parameter to be identified (ohmic internal resistance R) 0 Internal resistance to polarization R 1 、R 2 First time constant tao 1 Second time constant tao 2 ) The coefficients of the polynomial are strongly correlated, and the polynomial equation can be calculated by a fitting method.
Through the discretization expression, the parameters R0, R1 and R2 can be reversely deduced after b1, b2, b3, b4 and b5 are solved.
The backstepping calculation formula of the backstepping parameters R0, R1 and R2 is as follows:
where T refers to the time interval of sampling. By utilizing the formula, the values of the parameters R0, R1 and R2 can be calculated after the values of b1, b2, b3, b4 and b5 are substituted.
And S302, normalizing the data input into the second-order RC equivalent circuit model.
The step is used for carrying out normalization processing on input parameters such as current, voltage and the like, so that matrix singularity caused by overlarge feature difference in the identification process can be avoided.
In other words, in order to avoid influence on model operation due to matrix singularity caused by too large characteristic difference in the identification process, normalization processing can be performed on input parameters such as current and voltage, wherein the normalization processing can adopt the most-valued normalization processing or mean variance normalization processing, abnormal data are removed, the calculation efficiency is ensured, and therefore the steady state of the model operation is ensured.
And S303, identifying the first time scale parameter of the first-order circuit stage by adopting a recursive least square method based on a variable forgetting factor.
The recursive Least square method, also called RLS (recursive Least square) algorithm or Least square method, is an estimation method using the minimum sum of Squares of measurement errors as an optimal estimation criterion, but in practical application, the RLS algorithm may generate a phenomenon of data saturation, which causes the algorithm to fail, and therefore, the recursive Least square method with Forgetting factor, also called frls (recursive Least square) algorithm, is commonly used in the related art for parameter identification. However, the value of the forgetting factor in the FRLS algorithm is directly related to the stability of parameter identification and affects the model accuracy, and the FRLS algorithm with a fixed forgetting factor cannot adapt to the singular phenomenon of a numerical characteristic matrix generated by randomness in the working condition, and lacks the adaptability to the working condition.
The recursive least square method based on the variable forgetting factor is adopted, the self-adaptive variable forgetting factor is set, the variable forgetting factor and the recursive least square method are used for estimating set parameters, and therefore the parameter identification process is more accurate, the calculation speed of the algorithm is higher, the tracking capability is stronger, and better anti-noise capability is achieved.
Fig. 4 is a schematic diagram of an application framework for identifying parameters of a first-order circuit stage by using a recursive least square method based on a variable forgetting factor according to an embodiment of the present application.
This application adopts the recurrence least square method based on variable forgetting factor to discern the parameter of first order circuit stage, can include:
1) and initializing parameters of the second-order RC equivalent circuit model.
The method and the device perform parameter initialization on the discretized second-order RC equivalent circuit model. The initialized parameters comprise a system value theta (0) and a covariance P (0). In addition, a variable forgetting factor minimum value lambda is preset min Maximum value λ max A sliding window size M and a gain coefficient η.
The smaller the variable forgetting factor is, the stronger the algorithm tracking ability is, but the more sensitive to noise is, the larger the steady-state error is, the larger the variable forgetting factor is, the weaker the tracking ability is, but the less sensitive to noise is, and the smaller the estimation error of the parameter during convergence is. Therefore, the adaptive estimation variable forgetting factor is set, so that the recursive least square method has strong tracking capability, and better noise sensitivity is kept, and the identification precision of identifying the parameters of the first-order circuit stage is improved.
And in the process of initializing the parameters of the second-order RC equivalent circuit model, presetting a variable forgetting factor estimation initial value. Presetting variable forgetting factor minimum value lambda min Maximum value λ max In the practical application process, the minimum value and the maximum value of the variable forgetting factor can be taken according to multiple times of model simulation, for example, the maximum value and the minimum value of the variable forgetting factor which enables the algorithm to have both tracking capability and noise sensitivity are taken through 500 times of model simulation.
The sliding window size M is preset, wherein the value M can be set by adopting multiple times of model simulation, and can be set according to actual production requirements, and different error data can be quickly and effectively analyzed by utilizing error analysis with the sliding window.
2) The variable forgetting factor is adaptively estimated.
In the algorithm for adaptively estimating the variable forgetting factor in the present application, please refer to the following formula:
λ(k)＝λ min +(λ max -λ min )·2 Lk(k)
where err (k) is the estimation error of the least recursive quadratic algorithm at the kth time, lk (k) is the mean square error of the estimation error, and λ (k) is an adaptive variable forgetting factor.
In the estimation of the variable forgetting factor, the previous error analysis can be brought into the estimation process, so that the variable forgetting factor is adaptively updated.
3) And estimating the setting parameters by using a variable forgetting factor and a recursive least square method.
The variable forgetting factor is used in the recursive least square method, so that the identification process of the parameters of the first-order circuit stage is more accurate, and the estimation parameter formula is as follows:
where K (k) is the gain matrix of the algorithm, P (k) is the covariance matrix of the parameter to be identified,
is the optimized parameter matrix to be identified.
Through the process, the parameter identification of the first-order circuit stage is realized.
4) And restricting the identified parameters.
Since the time constants of the battery charge transfer process and the battery diffusion process are different and greatly different, the range of the battery charge transfer process and the battery diffusion process is restricted:
R 0 ≤R 0,max
R 1 ≤R 1,max
tao 1 ≤tao 1,max
5) the parameters identified in the first order circuit stage are deduced back from the estimated parameters.
And performing backstepping through the backstepping calculation formula by using the related estimated parameters obtained in the step of identifying the parameters of the first-order circuit stage by adopting a recursive least square method based on the variable forgetting factor, so as to obtain first time scale parameters R0, R1 and tao1 of the first-order circuit stage.
S304, identifying the second time scale parameter of the second-order circuit stage by adopting an adaptive extended Kalman filtering algorithm.
The Extended Kalman Filter (EKF) algorithm is implemented by expanding a nonlinear state equation and a measurement equation by a first-order taylor polynomial to implement local linearization and then using Kalman filtering.
By adopting the adaptive extended Kalman filtering algorithm, the noise parameters in the extended Kalman filtering algorithm can be continuously updated in the process of identifying the parameters in the second-order circuit stage, so that the noise parameters can be adaptively updated in the parameter identification process, for example, state noise and measurement noise are adaptively updated, and the influence of voltage noise and process noise in the parameter identification process on the parameter identification process is adjusted.
The method comprises the steps of analyzing and pre-estimating errors based on a sliding window, and then performing self-adaptive updating on state noise and measurement noise in the parameter identification process by utilizing the pre-estimated errors, so that the noise parameters can be updated in real time in the whole identification process, and the influence of the errors on the whole identification process is adjusted.
The resolving process of identifying the parameters of the second-order circuit stage based on the adaptive extended Kalman filtering algorithm is as follows:
1) establishing a state space equation
In the process of identifying the parameters of the second-order circuit stage, it can be considered that the polarization internal resistance R2 changes slowly, and the state space equation corresponding to the polarization internal resistance is established as follows:
wherein alpha is k Is state noise, beta k For measuring noise, V k To predict terminal voltage, R 2,k To polarize internal resistance, U 1,k Is a first order characteristic voltage, U 2,k And E is the polarization voltage of the circuit model.
2) Estimating the error of the measured voltage and the predicted voltage:
err(k)＝V mes -V k
wherein, V mes The measured voltage at the outer end.
3) Estimating state covariance and kalman gain:
wherein
For optimal estimation of covariance at time k, L k For Kalman gain, A is a state matrix, A T Is a transpose of A, C k Is a matrix of coefficients at time k, C k T Is C k The transposing of (1).
4) The state noise and the measurement noise are updated adaptively based on the sliding window by the previously obtained estimation values:
5) updating the state covariance and state variables according to the kalman gain:
R 2,k ＝R 2,k-1 +L k err(k)
6) parameter constraint
Because the power battery practical application operating mode is complicated, in order to avoid abnormal data to cause harmful effects, can also carry out the scope constraint to the parameter of second order circuit stage in this application.
R 2,min ≤R 2 ≤R 2,max
tao 2,min ≤tao 2 ≤tao 2,max
In the resolving process, the state noise and the measurement noise are updated adaptively continuously, so that the identification parameters of the second-order circuit stage are more stable and have stronger noise sensitivity.
S305, inputting the obtained first time scale parameters R0, R1 and tao1 of the first-order circuit stage into a second-order RC equivalent circuit model, and inputting the obtained second time scale parameters R2 and tao2 of the second-order circuit stage into the second-order RC equivalent circuit model to obtain the updated first time scale parameters and second time scale parameters.
In this step, the obtained first time scale parameter of the first order circuit stage may be input to the second order RC equivalent circuit model, and the obtained second time scale parameter of the second order circuit stage may be input to the second order RC equivalent circuit model.
After the obtained first time scale parameters R0, R1 and tao1 of the first-order circuit stage and the second time scale parameters R2 and tao2 of the second-order circuit stage are input into the second-order RC equivalent circuit model, the updated first time scale parameters and second time scale parameters can be obtained according to the operation of the second-order RC equivalent circuit model.
In addition, although the first time scale parameter and the second time scale parameter can be identified independently, in the present application, before the second time scale parameter is identified, if the parameter of the first order circuit stage is used, the parameter of the first order circuit stage can be obtained according to the requirement, for example, the latest parameters U0, U1 of the first order circuit stage are obtained before the second time scale parameter is identified.
It should be noted that the above-obtained first time scale parameter and second time scale parameter may be applied to SOC estimation. The first time scale parameter and the second time scale parameter obtained by applying the method are applied to SOC estimation, so that the SOC estimation precision can be improved, for example, the SOC estimation precision can be improved to be within 0.1%. The SOC estimation may be estimated by using a correlation function relationship or a mathematical model, which is not limited in this application.
It should be noted that, the method of the embodiment of the present application may be applied to parameter identification of SOH or SOP, in addition to parameter identification of SOC, and the principle is similar.
In summary, the present application provides a recursive least square method based on variable forgetting factors and an extended kalman filtering algorithm based on self-adaptation to respectively identify parameters with different time scales for two circuit stages of a second-order RC equivalent circuit model, so that both the two parameter identification processes have self-adaptability, the parameter identification method of the overall algorithm has strong adaptive capacity to work conditions, and effectively ensures the stability of the parameter identification process, and the application range of the present application is wide, for example, the present application can be applied to EV (pure electric) and HEV (hybrid electric) tools or devices, and realize multi-platform rapid transplantation, thereby ensuring engineering high precision, greatly reducing development cost and cycle, and improving development efficiency.
Corresponding to the embodiment of the application function implementation method, the application also provides a power battery online parameter identification device, a manned aircraft, computing equipment and a corresponding embodiment.
Fig. 6 is a schematic structural diagram of an online parameter identification device for a power battery according to an embodiment of the present application.
Referring to fig. 6, the power battery online parameter identification device 70 includes: a model equivalence module 71, a first parameter identification module 72, a second parameter identification module 73, and a parameter determination module 74.
And the model equivalent module 71 is used for establishing a second-order RC equivalent circuit model corresponding to the battery. Compared with a first-order RC equivalent model, the steady-state characteristic and the transient-state characteristic of the battery can be better considered by adopting the second-order RC equivalent model, and the charging and discharging behaviors of the battery at different temperatures and charging and discharging multiplying powers can be simulated by higher model precision. Therefore, the second-order RC equivalent circuit model corresponding to the battery is established.
The first parameter identification module 72 is configured to identify a first time scale parameter of a first-order circuit stage of the second-order RC equivalent circuit model by using a first preset algorithm, and input the first time scale parameter to the second-order RC equivalent circuit model for updating.
The second parameter identification module 73 is configured to identify a second time scale parameter of a second-order circuit stage of the second-order RC equivalent circuit model by using a second preset algorithm, and input the second time scale parameter to the second-order RC equivalent circuit model for updating.
And the parameter determining module 74 is configured to input the first time scale parameter of the first-order circuit stage obtained after the identification by the first parameter identifying module 72 and the second time scale parameter of the second-order circuit stage obtained after the identification by the second parameter identifying module 73 into the second-order RC equivalent circuit model of the model equivalent module, so as to obtain the updated first time scale parameter and the updated second time scale parameter.
The first parameter identification module 72 identifies the first time scale parameter of the first-order circuit stage of the second-order RC equivalent circuit model by using a recursive least square method based on a variable forgetting factor. For example, the R0, R1, tao1 parameters of the first order circuit stages are identified. Through setting the self-adaptive variable forgetting factor, the self-adaptive forgetting factor is brought into a recursive least square method to estimate parameters, so that the parameter identification process is more accurate, the calculation speed of the algorithm is higher, the tracking capability is stronger, and the better anti-noise capability is realized.
The second parameter identification module 73 identifies the second time scale parameter of the second-order circuit stage of the second-order RC equivalent circuit model by using an adaptive extended kalman filter algorithm. Such as identifying the R2, tao2 parameters of the second order circuit stage. By adopting the adaptive extended Kalman filtering algorithm, the noise parameter in the extended Kalman filtering algorithm can be continuously updated in the parameter identification process of the second-order circuit stage, so that the noise parameter can be adaptively updated in the parameter identification process, for example, state noise and measurement noise are adaptively updated, and the influence of voltage noise and process noise in the parameter identification process on the parameter identification process is adjusted.
According to the embodiment, the power battery online parameter identification device establishes the second-order RC equivalent circuit model corresponding to the battery, and separates the parameters of different time scales by adopting parameter identification modes of different time scales for the first-order circuit stage and the second-order circuit stage in the second-order RC equivalent circuit model, so that distributed calculation is performed, the calculation amount can be reduced, the calculation efficiency is improved, the identification precision and stability of the parameter identification process are effectively improved, and the power battery online parameter identification device is more beneficial to engineering application.
The embodiment of the application can also provide a manned aircraft, which comprises the power battery online parameter identification device shown in fig. 6.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Fig. 7 is a schematic structural diagram of a computing device according to an embodiment of the present application.
Referring to fig. 7, computing device 80 includes a memory 81 and a processor 82.
The Processor 82 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 81 may include various types of storage units such as a system memory, a Read Only Memory (ROM), and a permanent storage device. Wherein the ROM may store static data or instructions for the processor 82 or other modules of the computer. The persistent storage device may be a read-write storage device. The persistent storage may be a non-volatile storage device that does not lose stored instructions and data even after the computer is powered off. In some embodiments, the persistent storage device employs a mass storage device (e.g., magnetic or optical disk, flash memory) as the persistent storage device. In other embodiments, the permanent storage may be a removable storage device (e.g., floppy disk, optical drive). The system memory may be a read-write memory device or a volatile read-write memory device, such as a dynamic random access memory. The system memory may store instructions and data that some or all of the processors require at runtime. In addition, the memory 81 may comprise any combination of computer-readable storage media, including various types of semiconductor memory chips (e.g., DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), magnetic and/or optical disks, may also be employed. In some embodiments, memory 81 may include a removable storage device that is readable and/or writable, such as a Compact Disc (CD), a digital versatile disc read only (e.g., DVD-ROM, dual layer DVD-ROM), a Blu-ray disc read only, an ultra-dense disc, a flash memory card (e.g., SD card, min SD card, Micro-SD card, etc.), a magnetic floppy disk, or the like. Computer-readable storage media do not contain carrier waves or transitory electronic signals transmitted by wireless or wired means.
The memory 81 has stored thereon executable code which, when processed by the processor 82, may cause the processor 82 to perform some or all of the methods described above.
Furthermore, the method according to the present application may also be implemented as a computer program or computer program product comprising computer program code instructions for performing some or all of the steps of the above-described method of the present application.
Alternatively, the present application may also be embodied as a computer-readable storage medium (or non-transitory machine-readable storage medium or machine-readable storage medium) having executable code (or a computer program or computer instruction code) stored thereon, which, when executed by a processor of an electronic device (or server, etc.), causes the processor to perform part or all of the various steps of the above-described method according to the present application.
Having described embodiments of the present application, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (11)
1. A power battery online parameter identification method is characterized by comprising the following steps:
establishing a second-order RC equivalent circuit model corresponding to the battery;
identifying a first time scale parameter of a first-order circuit stage of the second-order RC equivalent circuit model by adopting a first preset algorithm;
identifying a second time scale parameter of a second-order circuit stage of the second-order RC equivalent circuit model by adopting a second preset algorithm;
and inputting the first time scale parameter of the first-order circuit stage and the second time scale parameter of the second-order circuit stage obtained after identification into the second-order RC equivalent circuit model to obtain the updated first time scale parameter and second time scale parameter.
2. The method of claim 1, wherein the identifying the first time scale parameter of the first order circuit stage of the second order RC equivalent circuit model using the first predetermined algorithm comprises:
and identifying the first time scale parameter of the first-order circuit stage of the second-order RC equivalent circuit model by adopting a recursive least square method based on a variable forgetting factor.
3. The method of claim 2, wherein the identifying the first time scale parameter of the first order circuit stage of the second order RC equivalent circuit model by using a recursive least square method based on a variable forgetting factor comprises:
initializing parameters of a second-order RC equivalent circuit model;
adaptively estimating a variable forgetting factor;
estimating a set parameter by using the variable forgetting factor and a recursive least square method;
and determining a first time scale parameter of a first-order circuit stage of the second-order RC equivalent circuit model by reverse extrapolation of the estimated setting parameter.
4. The method of claim 1, wherein before identifying the first time scale parameter of the first order circuit stage of the second order RC equivalent circuit model using the first predetermined algorithm, the method further comprises:
and carrying out normalization processing on the data input into the second-order RC equivalent circuit model.
5. The method of claim 1, wherein the identifying the second time scale parameter of the second-order circuit stage of the second-order RC equivalent circuit model by using a second predetermined algorithm comprises:
and identifying a second time scale parameter of a second-order circuit stage of the second-order RC equivalent circuit model by adopting an adaptive extended Kalman filtering algorithm.
6. The method of claim 5, wherein the identifying the second time scale parameter of the second-order circuit stage of the second-order RC equivalent circuit model using an adaptive-based extended Kalman filtering algorithm comprises:
establishing a state space equation of the polarization resistor in a second-order RC equivalent circuit model;
estimating the error of the measured voltage and the predicted voltage;
estimating a state covariance and a Kalman gain of the state space equation;
updating state noise and measurement noise of the state space equation based on a sliding window;
updating the state covariance and the state variable according to the Kalman gain;
and determining a second time scale parameter of a second-order circuit stage of the second-order RC equivalent circuit model according to a preset parameter constraint condition.
7. The utility model provides a power battery online parameter identification device which characterized in that includes:
the model equivalent module is used for establishing a second-order RC equivalent circuit model corresponding to the battery;
the first parameter identification module is used for identifying a first time scale parameter of a first-order circuit stage of the second-order RC equivalent circuit model by adopting a first preset algorithm and inputting the first time scale parameter into the second-order RC equivalent circuit model for updating;
the second parameter identification module is used for identifying a second time scale parameter of a second-order circuit stage of the second-order RC equivalent circuit model by adopting a second preset algorithm, and inputting the second time scale parameter into the second-order RC equivalent circuit model for updating;
and the parameter determining module is used for inputting the first time scale parameter of the first-order circuit stage obtained after the first parameter identification module identifies and the second time scale parameter of the second-order circuit stage obtained after the second parameter identification module identifies into the second-order RC equivalent circuit model of the model equivalent module to obtain the updated first time scale parameter and second time scale parameter.
8. The apparatus of claim 7, wherein:
the first parameter identification module identifies a first time scale parameter of a first-order circuit stage of the second-order RC equivalent circuit model by adopting a recursive least square method based on a variable forgetting factor; or the like, or a combination thereof,
and the second parameter identification module identifies a second time scale parameter of a second-order circuit stage of the second-order RC equivalent circuit model by adopting an adaptive extended Kalman filtering algorithm.
9. A manned vehicle comprising a power cell online parameter identification device according to claim 7 or 8.
10. A computing device, comprising:
a processor; and
a memory having executable code stored thereon, which when executed by the processor, causes the processor to perform the method of any one of claims 1-6.
11. A computer-readable storage medium having stored thereon executable code, which when executed by a processor, causes the processor to perform the method of any one of claims 1-6.
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