CN111693870A - Online identification method and device for high-order PNGV model parameters, storage medium and electronic equipment - Google Patents
Online identification method and device for high-order PNGV model parameters, storage medium and electronic equipment Download PDFInfo
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Abstract
The embodiment of the invention provides a method and a device for identifying parameters of a high-order PNGV model, a storage medium and electronic equipment, and solves the technical problems that in the prior art, the high-order PNGV model cannot quickly identify the parameters of the model in real time, the identification accuracy of the parameters of the model is reduced, and the estimation accuracy of the model on the state of a battery is reduced. According to the method for identifying the parameters of the high-order PNGV model, provided by the embodiment of the invention, the equivalent parameters of the high-order PNGV model are calculated in real time according to the terminal voltage and the loop current which are continuously and repeatedly acquired, and the model parameters of the high-order PNGV model are calculated in real time according to the equivalent parameters of the high-order PNGV model, so that the real-time model parameters of the high-order PNGV model can be identified in real time, the influence on the model parameters caused by the change of the parameters along with factors such as temperature, charging and discharging current, aging degree and the like in the use process of the battery is reduced, the identification accuracy of the model parameters is improved, and the state estimation.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of battery management, in particular to a method and a device for online identification of high-order PNGV model parameters, a storage medium and electronic equipment.
[ background of the invention ]
The battery is a complex electrochemical system, the Charge and discharge State of the battery is a nonlinear time-varying process, and the battery management system can be constructed to accurately monitor the Charge and discharge State of the battery, such as detecting the voltage, temperature, current, and the like of the battery, and estimating the SOC (State of Charge), SOP (State of Power), SOH (State of life), and the like of the battery. The battery management system can avoid overcharging and over-discharging of the battery, guides a user to reasonably use the battery and protect the battery, firstly constructs a battery equivalent model which is a bridge connecting external characteristics and internal states of the battery, predicts the working state of the battery by using the battery equivalent model, analyzes possible accidents caused by the battery, and avoids the occurrence of battery safety accidents.
The battery equivalent model commonly used in the prior art comprises an electrochemical model and an equivalent circuit model, wherein the equivalent circuit model simulates the output characteristics of a battery by using a circuit composed of circuit components based on the working performance characteristics of the battery, and sequentially simulates certain characteristics of the battery (the performance parameters of the battery comprise capacity, residual capacity, discharge rate, working temperature, efficiency, service life and the like), and the battery equivalent circuit model commonly used in the prior art comprises: the high-order PNGV model, the Rint model and the Thevenin model have higher precision than other models and are widely applied to battery characteristic simulation and battery state estimation. The model parameters corresponding to different battery equivalent circuit models are also different.
A battery equivalent circuit model with better estimation accuracy generally goes through the following three stages: the method comprises the steps of constructing a battery equivalent circuit model, identifying parameters of the battery equivalent model (wherein the parameter identification of the battery equivalent model is mainly to perform identification experiments on specific parameters of a battery), and performing simulation verification on the battery equivalent circuit model. In the process of simulation verification, when the error between the actually measured battery voltage and the voltage calculated by the battery equivalent circuit model is smaller than the error value acceptable under the experimental condition, the accuracy of the battery equivalent circuit model is considered to be better.
However, in the prior art, the identification of the high-order PNGV model parameters mainly depends on the offline test, but the parameters change along with factors such as temperature, charging and discharging current, aging degree and the like in the use process of the battery, so the offline test cannot identify the model parameters in real time, the identification accuracy of the model parameters is reduced, and the state estimation precision of the model on the battery is reduced.
[ summary of the invention ]
In view of this, embodiments of the present invention provide an online identification method and apparatus for high-order PNGV model parameters, a storage medium, and an electronic device, where by online real-time identification of the high-order PNGV model parameters, accuracy of identifying the model parameters is improved, and accuracy of estimating a state of a battery by a model is improved; the method solves the technical problems that the high-order PNGV model in the prior art cannot rapidly identify the model parameters on line in real time, the identification accuracy of the model parameters is reduced, and the state estimation precision of the model to the battery is reduced.
In one aspect, an embodiment of the present invention provides an online identification method for a high-order PNGV model parameter, where the high-order PNGV model is an equivalent circuit model of a battery, and the method includes:
constructing an initial equivalent parameter matrix and an initial covariance matrix;
sampling the terminal voltage and the loop current of the battery according to a preset time interval to obtain a plurality of terminal voltages and a plurality of loop currents; and
updating the initial equivalent parameter matrix according to the terminal voltages and the loop currents, generating equivalent parameters of the high-order PNGV model, and calculating model parameters of the high-order PNGV model according to the equivalent parameters;
wherein the plurality of model parameters comprises a first polarization capacitance value Cp1First polarization internal resistance value Rp1A second value of the polarization capacitance Cp2Second polarization internal resistance Rp2Equivalent capacitance value CbVoltage value U of voltage sourceemfAnd an ohmic internal resistance value R.
In an embodiment of the present invention, the obtaining the terminal voltages and the loop currents includes:
sampling the terminal voltage and the loop current of the battery for the ith time according to the preset time interval to generate the terminal voltage and the loop current during the sampling for the ith time;
the updating the initial equivalent parameter matrix according to the terminal voltages and the loop currents to generate equivalent parameters of the high-order PNGV model, and calculating model parameters of the high-order PNGV model according to the equivalent parameters, including:
updating an initial state matrix according to the terminal voltage and the loop current in the ith sampling to generate a state matrix in the ith sampling;
calculating a gain matrix during the ith sampling according to a preset forgetting factor, an initial covariance matrix and the state matrix during the ith sampling;
calculating an equivalent parameter matrix during the ith sampling according to the gain matrix during the ith sampling, the initial equivalent parameter matrix, the state matrix during the ith sampling and the terminal voltage during the ith sampling;
generating a plurality of equivalent parameters of the high-order PNGV model according to the equivalent parameter matrix during the ith sampling;
calculating a plurality of model parameters of the high-order PNGV model according to the equivalent parameters;
calculating a covariance matrix during the ith sampling according to the preset forgetting factor, the initial covariance matrix, the gain matrix during the ith sampling and the state matrix during the ith sampling;
1 is added to the sampling times;
judging whether the sampling frequency after the 1 adding processing is larger than a preset threshold value or not; and
when the sampling frequency after the 1 adding processing is smaller than or equal to the preset threshold, sampling the terminal voltage and the loop current of the battery for the (i +1) th time according to the preset time interval, and generating the terminal voltage and the loop current in the (i +1) th sampling; taking the state matrix during the ith sampling as an initial state matrix, updating the state matrix during the ith sampling according to the terminal voltage and the loop current during the (i +1) th sampling, and generating the state matrix during the (i +1) th sampling; and taking the equivalent parameter matrix during the ith sampling as an initial equivalent matrix, taking the covariance matrix during the ith sampling as an initial covariance matrix, and calculating the gain matrix during the (i +1) th sampling according to the preset forgetting factor, the covariance matrix during the ith sampling and the state matrix during the (i +1) th sampling.
In an embodiment of the present invention, the calculating a plurality of model parameters of the high-order PNGV model according to the equivalent parameters includes:
calculating the equivalent parameters through a system of equations (I) to generate a plurality of model parameters of the high-order PNGV model, wherein the system of equations (I) is as follows:
wherein α, β, gamma, lambda, η, omega, mu are eight equivalent parameters, Cp1Is a first polarization capacitance value, Rp1Is a first polarization internal resistance value, Cp2Is the second polarized capacitance value, Rp2Is the second polarization internal resistance value, CbIs equivalent capacitance value, UemfIs the voltage value of the voltage source, R is the ohmic internal resistance value, a1、a1、b1、b1C is a transition parameter, TsIs the sampling period.
In an embodiment of the present invention, the calculating a gain matrix at the ith sampling according to a preset forgetting factor, an initial covariance matrix, and the state matrix at the ith sampling includes:
calculating the preset forgetting factor, the initial covariance matrix and the state matrix at the ith sampling through a formula (I), and generating a gain matrix at the ith sampling, wherein the formula (I) is as follows:
wherein G isiIs the gain matrix at the ith sampling, Pi-1In order to be the initial covariance matrix,
and rho is a preset forgetting factor which is a state matrix at the ith sampling.
In an embodiment of the present invention, the calculating an equivalent parameter matrix at the ith sampling according to the gain matrix at the ith sampling, the initial equivalent parameter matrix, and the state matrix at the ith sampling includes:
calculating the gain matrix, the initial equivalent parameter matrix and the state matrix during the ith sampling through a formula (II), and generating the equivalent parameter matrix during the ith sampling, wherein the formula (II) is as follows:
wherein, thetai-1For an initial equivalent parameter matrix, θiIs an equivalent parameter matrix at the ith sampling, GiThe gain matrix at the time of the ith sample,
is the state matrix at the ith sampling, yiIs the terminal voltage at the ith sampling.
In an embodiment of the present invention, the calculating a covariance matrix at an ith sampling according to the preset forgetting factor, the initial covariance matrix, the gain matrix at the ith sampling, and the state matrix at the ith sampling includes:
calculating the preset forgetting factor, the initial covariance matrix, the gain matrix during the ith sampling and the state matrix during the ith sampling through a formula (III), and generating the covariance matrix during the ith sampling, wherein the formula (III) is as follows:
wherein, PiIs the covariance matrix at the ith sample, Pi-1In order to be the initial covariance matrix,
is the state matrix at the ith sampling, GiThe matrix is a gain matrix at the ith sampling, II is a unit matrix, and rho is a preset forgetting factor.
In an embodiment of the present invention, the constructing the initial equivalent parameter matrix and the initial covariance matrix includes:
setting the initial equivalent parameter matrix to be 0, and generating an initial covariance matrix according to a preset matrix and a preset coefficient; or
Sampling the terminal voltage and the loop current of the battery for m times to generate the terminal voltage and the loop current at the time of the m-th sampling; updating a state matrix at the mth sampling according to the terminal voltage and the loop current at the mth sampling; and calculating an initial equivalent parameter matrix and an initial covariance matrix according to the state matrix during the mth sampling, the terminal voltage during the mth sampling and the loop current.
As a second aspect of the present invention, an embodiment of the present invention provides an online identification apparatus for high-order PNGV model parameters, where the high-order PNGV model is an equivalent circuit model of a battery, and the online identification apparatus for high-order PNGV model parameters includes:
the sampling module is used for sampling the terminal voltage and the loop current of the battery according to a preset time interval to obtain a plurality of terminal voltages and a plurality of loop currents;
the matrix construction module is used for constructing an initial equivalent parameter matrix and an initial covariance matrix;
the parameter calculation module is used for updating the initial equivalent parameter matrix according to the terminal voltages and the loop currents, generating equivalent parameters of the high-order PNGV model, and calculating model parameters of the high-order PNGV model according to the equivalent parameters;
wherein the plurality of model parameters comprises a first polarization capacitance value Cp1First polarization internal resistance value Rp1A second value of the polarization capacitance Cp2Second polarization internal resistance Rp2Equivalent capacitance value CbVoltage value U of voltage sourceemfAnd an ohmic internal resistance value R.
As a third aspect of the present invention, an embodiment of the present invention provides a computer-readable storage medium, where the storage medium stores a computer program for executing the above-mentioned online identification method for parameters of a high-order PNGV model.
As a fourth aspect of the present invention, an embodiment of the present invention provides an electronic apparatus, including:
a processor;
a memory for storing the processor-executable instructions;
the processor is used for executing the online identification method of the high-order PNGV model parameters.
According to the online identification method for the parameters of the high-order PNGV model, provided by the embodiment of the invention, the equivalent parameter matrix is constructed, the terminal voltage and the loop current of the battery are collected in real time, the equivalent parameter matrix is updated according to the terminal voltage and the loop circuit which are continuously collected for multiple times, the equivalent parameters of the high-order PNGV model during sub-sampling are generated, the model parameters of the high-order PNGV model during sub-sampling are calculated according to the equivalent parameters of the sub-high-order PNGV model, the parameters of the high-order PNGV model can be identified in real time and on line, the influence of the parameters on the model parameters along with the change of factors such as temperature, charge-discharge current and aging degree in the use process of the battery is reduced, the identification accuracy of the model parameters is improved, and the state estimation accuracy of the battery.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is an equivalent circuit diagram of a high-level PNGV model according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating an online identification method for high-order PNGV model parameters according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart illustrating a method for online identification of high-order PNGV model parameters according to another embodiment of the present invention;
FIG. 4 is a schematic structural diagram of an online identification apparatus for high-order PNGV model parameters according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an online identification apparatus for high-order PNGV model parameters according to another embodiment of the present invention.
[ detailed description ] embodiments
For better understanding of the technical solutions of the present invention, the following detailed descriptions of the embodiments of the present invention are provided with reference to the accompanying drawings.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention 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 be understood that the term "and/or" as used herein is merely one type of association that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
As described in the background art, the identification of the high-order PNGV model parameters in the prior art mainly depends on the offline test, but the parameters of the model parameters change with factors such as temperature, charge-discharge current, aging degree and the like in the use process of the battery, so that the offline test cannot identify the model parameters in real time, the identification accuracy of the model parameters is reduced, and the state estimation accuracy of the battery by the model is reduced.
The embodiment of the invention provides an online identification method of high-order PNGV model parameters, which is characterized in that an equivalent parameter matrix is constructed, the terminal voltage and the loop current of a battery are collected in real time, the equivalent parameter matrix is updated according to the terminal voltage and the loop circuit which are continuously collected for multiple times, equivalent parameters when the terminal voltage and the loop current of the battery are collected for the second time are generated, the model parameters of the high-order PNGV model when the battery is sampled for the second time are calculated according to the equivalent parameters of the high-order PNGV model when the battery is sampled for the second time, the real-time parameters of the high-order PNGV model can be identified in real time and online, the influence of the change of the parameters along with factors such as temperature, charging and discharging current, aging degree and the like on the model parameters in the use process of the battery is reduced, the identification accuracy of the.
The following describes the high-order PNGV model provided in the embodiment of the present invention and an online identification method of model parameters of the high-order PNGV model in detail with reference to the drawings.
Fig. 1 is a circuit structure diagram of a high-order PNGV model according to an embodiment of the present invention, and as shown in fig. 1, the high-order PNGV model includes: the battery voltage simulation system comprises a voltage source U1, an equivalent capacitor C1, an ohmic internal resistor R1, a first parallel structure and a second parallel structure, wherein the first parallel structure comprises a first polarized internal resistor R2 and a first polarized capacitor C2 which are connected in parallel, the second parallel structure comprises a second polarized internal resistor R3 and a second polarized capacitor C3 which are connected in parallel, one end of the ohmic internal resistor R1 is connected with the second parallel structure, one end of the voltage source U1 is connected with one end of the equivalent capacitor C1, and the voltage between the other end of the ohmic internal resistor R1 and the other end of the voltage source U1 is the terminal voltage value of the PNVG model and is the terminal voltage value of the analog battery. Wherein the content of the first and second substances,
the voltage value of the voltage source U1 is UemfThe device is used for simulating the voltage between the positive electrode and the negative electrode of the battery under the condition that no current passes through the battery;
the resistance value of the ohmic internal resistance R1 is R, and the resistance value is used for simulating the ohmic internal resistance inside the battery;
the resistance value of the first polarization internal resistance R2 is Rp1The simulation device is used for simulating the internal resistance caused by the electrochemical polarization phenomenon caused by the chemical reaction between the anode and the cathode in the charging or discharging process of the battery, and is caused by the retardation effect generated by the transmission of charges in the battery;
the capacitance value of the first polarization capacitor C2 is Cp1The device is used for simulating electrochemical polarization capacitance caused by polarization phenomenon caused by chemical reaction between the anode and the cathode in the charging or discharging process of the battery;
the resistance value of the second polarization internal resistance R3 is Rp2The device is used for simulating the internal resistance caused by concentration polarization phenomenon in the charging or discharging process of the battery;
the second polarization capacitor C3 has a capacitance value of Cp2The polarization concentration polarization capacitor is used for simulating the polarization concentration polarization capacitor formed by the concentration polarization phenomenon in the charging or discharging process of the battery;
the equivalent capacitance C1 has a capacitance value of CbAnd the voltage variation simulation module is used for simulating the voltage variation generated by the accumulation of the battery in the working process.
Because the high-order PNGV model simulates the output characteristics of the battery by using a circuit composed of circuit components based on the working performance characteristics of the battery, and certain characteristics of the battery are simulated in turn (the performance parameters of the battery comprise capacity, residual capacity, discharge rate, working temperature, efficiency, service life and the like), the parameter identification in the high-order PNGV model is to identify that the voltage value of the voltage source U1 is U1emfThe ohmic internal resistance R1 has a resistance value of R, and the first polarization internal resistance R2 has a resistance value of Rp1The equivalent capacitance C1 has a capacitance value of CbCapacitance value of the first polarization capacitor C2Is Cp1The capacitance value of the second polarization capacitor C3 is Cp2The resistance value of the second polarization internal resistance R3 is Rp2。
Calculating the seven model parameters of the high-order PNGV model, which can be identified by obtaining a terminal voltage value U and a loop current I of the battery (the equivalent circuit model of the battery is the high-order PNGV model), wherein a relationship between the terminal voltage U and the loop current of the battery and the seven model parameters of the high-order PNGV model can be represented by a formula (five), wherein the formula (five) is:
in the formula (V), UemfIs the voltage value of the voltage source U1, R is the resistance value of the ohmic internal resistance R1, Rp1Is a resistance value of the first polarization internal resistance R2, Up1Is the voltage value, C, across the first polarization internal resistance R2bIs the capacitance value of the equivalent capacitance C1, UbIs the voltage value, R, across the equivalent capacitor C1p2Is the resistance value of the second polarization internal resistance R3, Up2Is the voltage value, C, across the second polarization internal resistance R3p1Is the capacitance value of a first polarization capacitor C2, Cp2The capacitance value of the second polarization capacitor C3;
therefore, the parameter U in the formula (five) can be completed by experimental data (namely the terminal voltage U of the battery and the loop current)emf、R、Rp1、Rp2、Cb、Cp1And Cp2。
Therefore, an embodiment of the present invention provides a method for identifying parameters of a high-order PNGV model, which is used to identify seven model parameters U of the high-order PNGV model shown in fig. 1emf、R、Rp1、Rp2、Cb、Cp1And Cp2. As shown in fig. 2, the method for identifying parameters of the high-order PNGV model includes the following steps:
step S101: constructing an initial equivalent parameter matrix and an initial covariance matrix;
step S102: sampling the terminal voltage and the loop current of the battery according to a preset time interval to obtain a plurality of terminal voltages and a plurality of loop currents; and
step S103: updating an initial equivalent parameter matrix according to the terminal voltages and the loop currents to generate equivalent parameters of a high-order PNGV model, and calculating model parameters of the high-order PNGV model according to the equivalent parameters;
wherein the plurality of model parameters includes a first polarization capacitance value Cp1First polarization internal resistance value Rp1A second value of the polarization capacitance Cp2Second polarization internal resistance Rp2Equivalent capacitance value CbVoltage value U of voltage sourceemfAnd an ohmic internal resistance value R.
According to the method for identifying the parameters of the high-order PNGV model, the equivalent parameter matrix is constructed, the terminal voltage and the loop current of the battery are collected in real time, the equivalent parameter matrix is updated according to the terminal voltage and the loop circuit which are collected for multiple times continuously, the equivalent parameters of the high-order PNGV model during sub-sampling are generated, the model parameters of the high-order PNGV model during sub-sampling are calculated according to the equivalent parameters of the high-order PNGV model during sub-sampling, the real-time parameters of the high-order PNGV model can be identified in real time and on line, the influence of the parameters on the model parameters along with the change of factors such as temperature, charging and discharging current and aging degree in the using process of the battery is reduced, the identification accuracy of the model parameters is improved, and the state estimation accuracy of the model on the battery is further improved.
Specifically, in an embodiment of the present invention, as shown in fig. 3, step S101 includes the following steps:
step S1011: constructing an initial equivalent parameter matrix theta3And an initial covariance matrix P3；
The discrete state space can be derived from equation (five), i.e., equation (five) is converted into a difference equation, which can be represented by equation (six), where equation (six) is:
in the formula (VI), alpha, beta, gamma, lambda, eta, omega and mu are eight equivalent parameters, U (I) and I (I), U (I-1) and I (I-1), U (I-2) and I (I-2), U (I-3) and I (I-3) are respectively the terminal voltage and the loop current when the battery is sampled at the ith time, the terminal voltage and the loop current when the battery is sampled at the ith-1 time, the terminal voltage and the loop current when the battery is sampled at the ith-2 time, and the terminal voltage and the loop current when the battery is sampled at the ith-3 time in the working process of the battery; since the equation (five) is the relationship between the terminal voltage and current of the battery and the seven model parameters of the high-order PNGV model, the seven model parameters of the high-order PNGV model can be calculated by calculating eight equivalent parameters α, β, γ, λ, η, ω, μ.
There are various identification methods for identifying the eight equivalent parameters in the formula (six), and the embodiment of the invention adopts an RLS (recursive least squares) algorithm to identify the eight equivalent parameters in the formula (six);
however, the identification algorithm for identifying the eight equivalent parameters in the formula (six) is not limited in the embodiment of the present invention, that is, the identification method for identifying the eight equivalent parameters in the formula (six) includes, but is not limited to, least squares algorithm, particle filter, machine learning, and the like.
Thus, an equivalent parameter matrix θ ═ α β γ λ η μ ω is constructed]TAnd constructing a state matrix of the battery:
make the terminal voltage y of the battery at the ith samplingi＝UiThen, equation (six) can also be represented by equation (seven):
due to the state matrix
And yiCan be obtained by sampling, therefore, the theta can be obtained by the formula (seven)iThrough thetaiThe eight equivalent parameters α, β, gamma, lambda, η, omega, mu of the high-order PNGV model can be obtained.
Therefore, the state matrix can be obtained by respectively sampling the terminal voltage U and the current of the battery three times in succession, i.e., the state matrix is updated once every time sampling is performed. The equivalent parameter matrix θ is obtained by calculation, and therefore, when constructing the equivalent parameter matrix, an initial equivalent matrix needs to be determined, that is, the initial equivalent matrix is constructed. In order to better acquire the equivalent matrix of PNGV at each sampling, the embodiment of the invention introduces a covariance matrix P, so that an initial covariance matrix is constructed while an initial equivalent parameter matrix is constructed.
State matrix
The terminal voltage U and the current of the battery are respectively sampled and obtained by four times continuously, therefore, when the battery is sampled in the working process of the battery, firstly, the 1 st, the 2 nd, the 3 rd and the 4 th sampling of the PNGV are needed to obtain a state matrix of the battery at the 4 th sampling (i.e. i is equal to 4), and therefore, an equivalent parameter matrix theta is obtained when the 4 th sampling of the battery is calculated4Namely, the equivalent parameters are obtained by first calculation, so that theta can be directly used when an initial equivalent parameter matrix is constructed3As an initial equivalent parameter; when the initial covariance matrix is constructed in the same way, P can be directly combined3As an initial covariance matrix.
Optionally, the initial equivalent parameter matrix θ3It can be set to 0 directly or can be set to 0,
i.e. theta3＝[0 0 0 0 0 0 0 0]T
Initial covariance matrix P3＝σ2II, where II is an identity matrix, σ, of 8 × 82≥106。
Initial equivalent matrix theta3And an initial covariance matrix P3Although the model parameter can be determined arbitrarily, the arbitrary determination may cause the model parameter obtained when the battery is sampled for the previous M times to be greatly different from the actual parameter, and the error between the generated model parameter and the model actual parameter may be small after the battery is sampled for multiple times. Thus will theta3Can be set directly to 0, the initial covariance matrix P3＝σ2And II, although the difference between the model parameters acquired when the battery is sampled for the first few times and the actual model parameters can be larger, the error between the generated model parameters and the model actual parameters can be quickly smaller.
Optionally, in order to make the error between the generated model parameter and the model actual parameter smaller more quickly, the terminal voltage and the loop current of the battery may be sampled m times first, and the terminal voltage and the loop current at the time of the m-th sampling may be generated; obtaining a state matrix phi in the mth sampling according to the terminal voltage and the loop current in the mth samplingm(ii) a And according to the state matrix phi at the m-th samplingmTerminal voltage Y at m-th samplingmAnd calculating an initial equivalent parameter matrix theta by using the loop current3And an initial covariance matrix P3。
I.e. the initial equivalent parameter matrix theta3Calculated by the following formula (eight):
initial covariance matrix P3Calculated by the following formula (nine):
i.e. the initial equivalent parameter matrix theta3And an initial covariance matrix P3Setting an equivalent parameter matrix and a covariance matrix of a high-order PNGV model after m-time sampling as an initial equivalent parameter matrix and an initial covariance matrix, and enabling the initial equivalent parameter matrix theta to be3And an initial covariance matrix P3The error from the actual model parameters is small.
The step S1011 is to construct an initial equivalent parameter matrix θ3And an initial covariance matrix P3In the specific process of constructing the initial equivalent parameter matrix theta3And an initial covariance matrix P3Then, the battery may be sampled, and the model parameters of the high-order PNGV model are generated in real time, that is, step S102 is executed, as shown in fig. 3, where step S102 specifically includes the following steps:
step S1021: respectively sampling the terminal voltage and the loop current of the battery for the 1 st time, the 2 nd time, the 3 rd time and the 4 th time according to a preset time interval, and respectively acquiring four groups of terminal voltages U and loop currents I, namely U, at the 1 st time, the 2 nd time, the 3 rd time and the 4 th time of sampling4And I4、U3And I3、U2And I2、U1And I1；
Step S103 specifically includes the following steps:
step S1031: according to four groups of terminal voltage U and loop current I at the time of sampling at times 1, 2, 3 and 4, namely U4And I4、U3And I3、U2And I2、U1And I1(ii) a Generating the State matrix at the 4 th sample
Wherein the content of the first and second substances,
step S1032: according to a preset forgetting factor rho, an initial covariance matrix P3And the state matrix at the 4 th sampling
Computing the gain matrix G at the 4 th sample4；
Specifically, the initial covariance matrix P is calculated by the formula (ten)3And the state matrix at the 4 th sampling
Calculating to generate a gain matrix G at the 4 th sampling4Wherein the formula (ten) is:
wherein rho is a preset forgetting factor, and is a numerical value which is greater than 0 and less than 1; due to p3Having been acquired in the step S1011,
also in step S1031, the gain matrix G at the 4 th sampling time can be calculated by formula (ten)4。
By setting the forgetting factor, the gain can be more accurate when the gain is calculated, and further, the calculated equivalent parameter is more accurate, the calculated model parameter is closer to a real value, and the precision of the high-order PNGV model is higher.
Step S1033: according to the gain matrix G at the 4 th sampling4Initial equivalent parameter matrix theta3State matrix at 4 th sampling
Calculating an equivalent parameter matrix theta at the 4 th sampling4；
Specifically, the gain matrix G at the 4 th sampling is expressed by the formula (eleven)4Initial equivalent parameter matrix theta3State matrix at 4 th sampling
Calculating to generate equivalent parameter matrix theta at the 4 th sampling4Wherein the formula (eleven) is:
wherein, y4For terminal voltage at sample 4, gain matrix G4Initial equivalent parameter matrix theta3State matrix at 4 th sampling
All are obtained, therefore, the equivalent parameter matrix theta at the 4 th sampling can be obtained by the formula (eleven)4。
Step S1034: according to the equivalent parameter matrix theta at the time of the 4 th sampling4Generating equivalent parameters at the 4 th sampling, namely eight equivalent parameters α, β, gamma, lambda, η, omega,. mu.at the 4 th sampling;
step S1035: and calculating seven model parameters of the high-order PNGV model according to the eight equivalent parameters alpha, beta, gamma, lambda, eta, omega and mu at the time of the 4 th sampling.
Specifically, eight equivalent parameters (α, β, γ, λ, η, ω, μ) at the 4 th sampling are calculated through a system of equations (one), and seven model parameters of the high-order PNGV model are generated, wherein the system of equations (one) is as follows:
wherein α, β, gamma, lambda, η, omega, mu are eight equivalent parameters, Cp1Is a first polarization capacitance value, Rp1Is a first polarization internal resistance value, Cp2Is the second polarized capacitance value, Rp2Is the second polarization internal resistance value, CbIs equivalent capacitance value, UemfIs the voltage value of the voltage source, R is the ohmic internal resistance value, a1、a1、b1、b1C is a transition parameter, TsIs the sampling period.
Step S1036: according to a preset forgetting factor rho and an initial covariance matrix P3Gain matrix G at the 4 th sampling4And the state matrix at the 4 th sampling
Calculating covariance matrix P at 4 th sampling4；
Specifically, the initial covariance matrix P is calculated by the formula (twelve)3Gain matrix G at the 4 th sampling4State matrix at 4 th sampling
Calculating to generate covariance matrix P at 4 th sampling4Wherein the formula (twelve) is:
in the formula (twelve), II is an identity matrix.
In step S1036, the eight equivalent parameters α, β, γ, λ, η, ω, and μ at the 4 th sampling are known, and therefore, the model parameter at the 4 th sampling can be calculated through the equation set (one).
When the 4 th sampling is performed on the battery, and the parameters of the high-order PNGV model are calculated, the 5 th sampling and the 6 th sampling … … and the ith sampling … … can be performed on the battery, and the following steps are respectively performed:
step (I): sampling the terminal voltage and the loop current of the battery for the ith time according to a preset time interval, and generating the terminal voltage U at the sampling for the ith timeiAnd the loop current IiAnd according to the terminal voltage U at the ith samplingiAnd the loop current IiUpdating the initial state matrix to generate the state matrix at the ith sampling
Step (II): according to the presetForgetting factor p, initial covariance matrix, and state matrix at ith sample
Calculating gain matrix G at ith samplingi(ii) a The initial covariance matrix is the covariance matrix of the last sampling, i.e. the covariance matrix P of the (i-1) th samplingi-1；
Specifically, a preset forgetting factor rho and a covariance matrix P at the time of sampling the (i-1) th time are calculated by formula (I)i-1And the state matrix at the ith sampling
Calculating to generate a gain matrix G at the ith samplingiWherein the formula (one) is:
wherein G isiIs the gain matrix at the ith sampling, Pi-1Is the covariance matrix at the i-1 th sample,
is the state matrix at the ith sample.
Step (three): according to the gain matrix G at the ith sampling timeiInitial equivalent parameter matrix and state matrix at ith sampling
Calculating an equivalent parameter matrix theta at the ith samplingiThe initial equivalent parameter matrix at this time is the equivalent parameter matrix at the last sampling, i.e. the equivalent parameter matrix theta at the i-1 th samplingi-1；
Specifically, the gain matrix G at the ith sampling is obtained by the formula (II)iAnd the equivalent parameter matrix theta at the time of sampling of the (i-1) th timei-1State matrix at ith sampling
Calculating to generate an equivalent parameter matrix during the ith sampling, wherein a formula (two) is as follows:
wherein, thetai-1Is an equivalent parameter matrix at the i-1 th sampling, thetaiIs an equivalent parameter matrix at the ith sampling, GiIs the gain matrix at the ith sample,
is the state matrix at the ith sampling, yiIs terminal voltage U at the ith samplingi。
Step (IV): equivalent parameter matrix theta at ith samplingiGenerating equivalent parameters at the ith sampling time, namely eight equivalent parameters α, β, gamma, lambda, η, omega, mu at the ith sampling time;
step (V): and calculating seven model parameters of the high-order PNGV model according to the eight equivalent parameters alpha, beta, gamma, lambda, eta, omega and mu at the ith sampling.
Specifically, eight equivalent parameters (α, β, γ, λ, η, ω) at the ith sampling are calculated through a system of equations (one), so as to generate seven model parameters of a high-order PNGV model, wherein the system of equations (one) is as follows:
wherein α, β, gamma, lambda, η, omega, mu are eight equivalent parameters, Cp1Is a first polarization capacitance value, Rp1Is a first polarization internal resistance value, Cp2Is the second polarized capacitance value, Rp2Is the second polarization internal resistance value, CbIs equivalent capacitance value, UemfIs the voltage value of the voltage source, R is the ohmic internal resistance value, a1、a1、b1、b1C is a transition parameter, TsIs the sampling period.
The eight equivalent parameters α, β, γ, λ, η, ω, μ at the ith sampling are known, so that the model parameter at the ith sampling can be calculated by the equation set (one).
Step (six): according to a preset forgetting factor rho and an initial covariance matrix (the initial covariance matrix at the moment is the covariance matrix P at the sampling of the (i-1))i-1) Gain matrix G at the ith samplingiAnd the state matrix at the ith sampling
Calculating the covariance matrix P at the ith samplingi；
Specifically, a preset forgetting factor rho and a covariance matrix P at the time of sampling the (i-1) th time are calculated by formula (III)i-1Gain matrix G at the ith samplingiState matrix at ith sampling
Calculating to generate covariance matrix P at ith samplingiWherein the formula (III) is:
in the step (VI), the generation of the model parameters of the i times of sampling, the updating of the gain matrix and the updating of the covariance matrix are completed. Then the sample is processed by adding 1, step (seven),
step (seven): 1 is added to the sampling times i to generate the (i +1) th;
and (eight): judging whether i +1 is greater than a preset threshold value or not;
and (7) when the i +1 is larger than the preset threshold, executing the step (nine), namely stopping sampling the battery.
When the i +1 is smaller than or equal to a preset threshold value, sampling the terminal voltage and the loop current of the PNG model for the (i +1) th time according to a preset time interval, and generating a terminal voltage U when the (i +1) th time is sampledi+1And a loopCurrent Ii+1And according to the terminal voltage U at the time of sampling at the (i +1) th timei+1And the loop current Ii+1Updating the initial state matrix (the initial state matrix at this time is the state matrix at the ith sampling time
) Generating the state matrix at the i +1 th sampling
And according to a preset forgetting factor rho and an initial covariance matrix (namely the covariance matrix P at the ith sampling)i) And the state matrix at the i +1 th sampling
Calculating the gain matrix G at the i +1 th samplingi+1The method comprises the steps of continuously calculating a gain matrix at the i +1 th time, calculating an equivalent parameter matrix at the i +1 th time, generating equivalent parameters at the i +1 th time, calculating model parameters at the i th time and calculating a covariance matrix at the i +1 th time, namely, the step (I), the step (II), the step (III), the step (IV), the step (V) and the step (VI), and only taking i in the step (I), the step (II), the step (III), the step (IV), the step (V) and the step (VI) as i +1 in the execution process.
Because the battery is continuously sampled, each sampling can generate model parameters in each sampling through the steps (I) to (VI), the model parameters of PNGV can be rapidly identified on line in real time, the influence on the model parameters caused by the change of the parameters along with the factors such as temperature, charging and discharging current, aging degree and the like in the use process of the battery is reduced, the identification accuracy of the model parameters is improved, and the state estimation precision of the model on the battery is further improved.
As a second aspect of the present invention, as shown in fig. 4, an embodiment of the present invention further provides an apparatus for identifying parameters of a high-order PNGV model, where the high-order PNGV model is an equivalent circuit model of a battery, and the apparatus for identifying parameters of the high-order PNGV model includes:
the matrix construction module 1 is used for constructing an initial equivalent parameter matrix and an initial covariance matrix;
the sampling module 2 is used for sampling the terminal voltage and the loop current of the battery according to a preset time interval to obtain a plurality of terminal voltages and a plurality of loop currents;
the parameter calculation module 3 is used for updating the initial equivalent parameter matrix according to the terminal voltages and the loop currents, generating a plurality of equivalent parameters of the high-order PNGV model, and calculating a plurality of model parameters of the high-order PNGV model according to the equivalent parameters;
wherein the plurality of model parameters comprises a first polarization capacitance value Cp1First polarization internal resistance value Rp1A second value of the polarization capacitance Cp2Second polarization internal resistance Rp2Equivalent capacitance value CbVoltage value U of voltage sourceemfAnd an ohmic internal resistance value R.
In an embodiment of the present invention, as shown in fig. 5, the parameter calculating module 3 specifically includes:
a first updating unit 31, configured to update an initial state matrix according to the terminal voltage and the loop current at the ith sampling time, and generate a state matrix at the ith sampling time;
a first calculating unit 32, configured to calculate a gain matrix at the ith sampling according to a preset forgetting factor ρ, an initial covariance matrix, and the state matrix at the ith sampling;
a second calculating unit 33, configured to calculate an equivalent parameter matrix at the ith sampling according to the gain matrix at the ith sampling, the initial equivalent parameter matrix, the state matrix at the ith sampling, and the terminal voltage at the ith sampling, and generate multiple equivalent parameters of the high-order PNGV model;
a parameter generating module 34, configured to calculate a plurality of model parameters of the high-order PNGV model according to the plurality of equivalent parameters;
a third calculating unit 35, configured to calculate a covariance matrix at the ith sampling according to a preset forgetting factor ρ, the initial covariance matrix, the gain matrix at the ith sampling, and the state matrix at the ith sampling;
a fourth calculating unit 36, configured to add 1 to the sampling frequency i to generate i +1 sampling frequency;
a judging unit 37, configured to judge whether the sampling frequency (i +1) after the 1 adding process is greater than a preset threshold;
when the sampling frequency after the 1 adding processing is smaller than or equal to the preset threshold, sampling the terminal voltage and the loop current of the battery for the (i +1) th time according to the preset time interval, and generating the terminal voltage and the loop current in the (i +1) th sampling; taking the state matrix during the ith sampling as an initial state matrix, updating the state matrix during the ith sampling according to the terminal voltage and the loop current during the (i +1) th sampling, and generating the state matrix during the (i +1) th sampling; and taking the equivalent parameter matrix at the ith sampling as an initial equivalent matrix, taking the covariance matrix at the ith sampling as an initial covariance matrix, and calculating the gain matrix at the (i +1) th sampling according to a preset forgetting factor rho, the covariance matrix at the ith sampling and the state matrix at the (i +1) th sampling.
According to the high-order PNGV model parameter identification device provided by the embodiment of the invention, the equivalent parameter matrix is constructed, the terminal voltage and the loop current of the battery are collected in real time, the equivalent parameter matrix is updated according to the terminal voltage and the loop circuit which are collected for multiple times continuously, the equivalent parameter of the high-order PNGV model during sub-sampling is generated, the model parameter of the high-order PNGV model during sub-sampling is calculated according to the equivalent parameter of the high-order PNGV model during sub-sampling, the real-time parameter of the high-order PNGV model can be identified in real time and on line, the influence of the change of the parameter along with factors such as temperature, charging and discharging current, aging degree and the like on the model parameter during the use process of the battery is reduced, the identification accuracy of the model parameter is improved, and the state estimation accuracy of the model.
The embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for identifying parameters of a high-order PNGV model provided in the embodiment of the present application can be implemented.
The computer-readable storage medium described above may take any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only Memory (ROM), an Erasable Programmable read-only Memory (EPROM), a flash Memory, an optical fiber, a portable compact disc read-only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of Network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
An embodiment of the present application further provides an electronic device, which includes one or more processors and a memory, where the processor is configured to execute the above-mentioned online identification method for parameters of a high-order PNGV model.
The processor may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.
The memory may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium and executed by a processor to implement the power parameter adjustment method or the reinforcement learning model training method of the various embodiments of the present application described above, and/or other desired functions. Various contents such as an input signal, a signal component, a noise component, etc. may also be stored in the computer-readable storage medium.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention 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.
The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection". Similarly, the phrases "if determined" or "if detected (a stated condition or event)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. An online identification method for parameters of a high-order PNGV model, wherein the high-order PNGV model is an equivalent circuit model of a battery, the method comprising:
constructing an initial equivalent parameter matrix and an initial covariance matrix;
sampling the terminal voltage and the loop current of the battery according to a preset time interval to obtain a plurality of terminal voltages and a plurality of loop currents; and
updating the initial equivalent parameter matrix according to the terminal voltages and the loop currents, generating equivalent parameters of the high-order PNGV model, and calculating model parameters of the high-order PNGV model according to the equivalent parameters;
wherein the plurality of model parameters comprises a first polarization capacitance value Cp1First polarized internal resistanceValue Rp1A second value of the polarization capacitance Cp2Second polarization internal resistance Rp2Equivalent capacitance value CbVoltage value U of voltage sourceemfAnd an ohmic internal resistance value R.
2. The on-line identification method as claimed in claim 1,
the obtaining of the multiple terminal voltages and the multiple loop currents of the high-order PNGV model includes:
sampling the terminal voltage and the loop current of the battery for the ith time according to the preset time interval to generate the terminal voltage and the loop current during the sampling for the ith time;
the updating the initial equivalent parameter matrix according to the terminal voltages and the loop currents to generate equivalent parameters of the high-order PNGV model, and calculating model parameters of the high-order PNGV model according to the equivalent parameters, including:
updating an initial state matrix according to the terminal voltage and the loop current in the ith sampling to generate a state matrix in the ith sampling;
calculating a gain matrix during the ith sampling according to a preset forgetting factor, an initial covariance matrix and the state matrix during the ith sampling;
calculating an equivalent parameter matrix during the ith sampling according to the gain matrix during the ith sampling, the initial equivalent parameter matrix, the state matrix during the ith sampling and the terminal voltage during the ith sampling;
generating a plurality of equivalent parameters of the high-order PNGV model according to the equivalent parameter matrix during the ith sampling;
calculating a plurality of model parameters of the high-order PNGV model according to the equivalent parameters;
calculating a covariance matrix during the ith sampling according to the preset forgetting factor, the initial covariance matrix, the gain matrix during the ith sampling and the state matrix during the ith sampling;
1 is added to the sampling times;
judging whether the sampling frequency after the 1 adding processing is larger than a preset threshold value or not; and
when the sampling frequency after the 1 adding processing is smaller than or equal to the preset threshold, sampling the terminal voltage and the loop current of the battery for the (i +1) th time according to the preset time interval, and generating the terminal voltage and the loop current in the (i +1) th sampling; taking the state matrix during the ith sampling as an initial state matrix, updating the state matrix during the ith sampling according to the terminal voltage and the loop current during the (i +1) th sampling, and generating the state matrix during the (i +1) th sampling; and taking the equivalent parameter matrix during the ith sampling as an initial equivalent matrix, taking the covariance matrix during the ith sampling as an initial covariance matrix, and calculating the gain matrix during the (i +1) th sampling according to the preset forgetting factor, the covariance matrix during the ith sampling and the state matrix during the (i +1) th sampling.
3. The online identification method of claim 2, wherein the calculating a plurality of model parameters of the high-order PNGV model according to the equivalent parameters comprises:
calculating the equivalent parameters through a system of equations (I) to generate a plurality of model parameters of the high-order PNGV model, wherein the system of equations (I) is as follows:
wherein α, β, gamma, lambda, η, omega, mu are eight equivalent parameters, Cp1Is a first polarization capacitance value, Rp1Is a first polarization internal resistance value, Cp2Is the second polarized capacitance value, Rp2Is the second polarization internal resistance value, CbIs equivalent capacitance value, UemfIs the voltage value of the voltage source, R is the ohmic internal resistance value, a1、a1、b1、b1C is a transition parameter, TsIs the sampling period.
4. The online identification method according to claim 2, wherein the calculating the gain matrix at the ith sampling according to the preset forgetting factor, the initial covariance matrix and the state matrix at the ith sampling comprises:
calculating the preset forgetting factor, the initial covariance matrix and the state matrix at the ith sampling through a formula (I), and generating a gain matrix at the ith sampling, wherein the formula (I) is as follows:
wherein G isiIs the gain matrix at the ith sampling, Pi-1In order to be the initial covariance matrix,
and rho is a preset forgetting factor which is a state matrix at the ith sampling.
5. The online identification method according to claim 2, wherein the calculating the equivalent parameter matrix at the ith sampling according to the gain matrix at the ith sampling, the initial equivalent parameter matrix, and the state matrix at the ith sampling comprises:
calculating the gain matrix, the initial equivalent parameter matrix and the state matrix during the ith sampling through a formula (II), and generating the equivalent parameter matrix during the ith sampling, wherein the formula (II) is as follows:
wherein, thetai-1For an initial equivalent parameter matrix, θiIs an equivalent parameter matrix at the ith sampling, GiThe gain matrix at the time of the ith sample,
is the state matrix at the ith sampling, yiIs the terminal voltage at the ith sampling.
6. The online identification method according to claim 2, wherein the calculating the covariance matrix at the ith sampling according to the preset forgetting factor, the initial covariance matrix, the gain matrix at the ith sampling, and the state matrix at the ith sampling comprises:
calculating the preset forgetting factor, the initial covariance matrix, the gain matrix during the ith sampling and the state matrix during the ith sampling through a formula (III), and generating the covariance matrix during the ith sampling, wherein the formula (III) is as follows:
wherein, PiIs the covariance matrix at the ith sample, Pi-1In order to be the initial covariance matrix,
is the state matrix at the ith sampling, GiThe matrix is a gain matrix at the ith sampling, II is a unit matrix, and rho is a preset forgetting factor.
7. The on-line identification method of claim 1, wherein the constructing an initial equivalent parameter matrix and an initial covariance matrix comprises:
setting the initial equivalent parameter matrix to be 0, and generating an initial covariance matrix according to a preset matrix and a preset coefficient; or
Sampling the terminal voltage and the loop current of the battery for m times to generate the terminal voltage and the loop current at the time of the m-th sampling; updating a state matrix at the mth sampling according to the terminal voltage and the loop current at the mth sampling; and calculating an initial equivalent parameter matrix and an initial covariance matrix according to the state matrix during the mth sampling, the terminal voltage during the mth sampling and the loop current.
8. An apparatus for online identification of parameters of a high-level PNGV model, wherein the high-level PNGV model is an equivalent circuit model of a battery, the apparatus comprising:
the sampling module is used for sampling the terminal voltage and the loop current of the battery according to a preset time interval to obtain a plurality of terminal voltages and a plurality of loop currents;
the matrix construction module is used for constructing an initial equivalent parameter matrix and an initial covariance matrix;
the parameter calculation module is used for updating the initial equivalent parameter matrix according to the terminal voltages and the loop currents, generating equivalent parameters of the high-order PNGV model, and calculating model parameters of the high-order PNGV model according to the equivalent parameters;
wherein the plurality of model parameters comprises a first polarization capacitance value Cp1First polarization internal resistance value Rp1A second value of the polarization capacitance Cp2Second polarization internal resistance Rp2Equivalent capacitance value CbVoltage value U of voltage sourceemfAnd an ohmic internal resistance value R.
9. A computer readable storage medium, wherein a computer program is stored, and the computer program is used for executing the method for online identification of parameters of a high-order PNGV model according to any of the claims 1-7.
10. An electronic device, the electronic device comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor is used for executing the method for online identification of the parameters of the high-order PNGV model as claimed in any one of claims 1 to 7.
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Application publication date: 20200922 |