CN112507640A - Method, device, equipment and storage medium for acquiring circuit model parameter values - Google Patents
Method, device, equipment and storage medium for acquiring circuit model parameter values Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a storage medium for acquiring circuit model parameter values. The method comprises the following steps: acquiring Electrochemical Impedance Spectroscopy (EIS) data, and determining a circuit model matched with the EIS data and each element in the circuit model; respectively calculating terminal voltage estimated values according to the parameter values of a plurality of groups of elements, and calculating a fitness function value according to the terminal voltage estimated values; and if the fitness function value corresponding to the target group meets the error threshold condition, taking each parameter value corresponding to the target group as the parameter value of the circuit model. By using the technical scheme of the invention, the accurate estimation of the circuit model parameter value can be realized.
Description
Technical Field
The embodiment of the invention relates to the technical field of electrochemistry, in particular to a method, a device, equipment and a storage medium for acquiring circuit model parameter values.
Background
Electrochemical Impedance Spectroscopy (EIS) is ubiquitous in the fields of Electrochemical research, development and quality control. And comparing the data obtained by the EIS test with a circuit model formed by combining resistors, capacitors, inductors and other theoretical elements, and if the data is matched with the circuit, namely the data is fitted with the model, the circuit is regarded as an effective circuit model of the EIS data.
In the prior art, fitting tools such as ZView and ZSimpWin are usually adopted to fit EIS data and identify circuit model parameters. The circuit model parameters obtained from the prior art fitting tools are less expected.
Disclosure of Invention
The embodiment of the invention provides a method, a device, equipment and a storage medium for acquiring circuit model parameter values, so as to realize accurate estimation of the circuit model parameter values.
In a first aspect, an embodiment of the present invention provides a method for obtaining a parameter value of a circuit model, where the method includes:
acquiring Electrochemical Impedance Spectroscopy (EIS) data, and determining a circuit model matched with the EIS data and each element in the circuit model;
respectively calculating terminal voltage estimated values according to the parameter values of a plurality of groups of elements, and calculating a fitness function value according to the terminal voltage estimated values;
and if the fitness function value corresponding to the target group meets the error threshold condition, taking each parameter value corresponding to the target group as the parameter value of the circuit model.
In a second aspect, an embodiment of the present invention further provides an apparatus for obtaining a parameter value of a circuit model, where the apparatus includes:
the EIS data acquisition module is used for acquiring electrochemical impedance spectroscopy EIS data and determining a circuit model matched with the EIS data and each element in the circuit model;
the fitness function value calculation module is used for calculating terminal voltage estimation values according to the parameter values of a plurality of groups of elements and calculating fitness function values according to the terminal voltage estimation values;
and the circuit model parameter value determining module is used for taking each parameter value corresponding to the target group as the parameter value of the circuit model if the fitness function value corresponding to the target group is determined to meet the error threshold condition.
In a third aspect, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the method for obtaining the circuit model parameter value according to any one of the embodiments of the present invention when executing the program.
In a fourth aspect, embodiments of the present invention further provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are used to perform the method for obtaining the parameter values of the circuit model according to any one of the embodiments of the present invention.
The embodiment of the invention determines the matched circuit model and elements by acquiring EIS data, calculates the fitness function value corresponding to each group of parameter values, and takes the parameter values of the target group as the parameter values of each element in the circuit model when the fitness function value of the target group meets the error threshold condition. The problem of poor expected effect of circuit model parameters obtained by adopting fitting tools such as ZView and ZSimpWin to fit EIS data and identifying the circuit model parameters in the prior art is solved, and accurate estimation of the circuit model parameter values is realized.
Drawings
FIG. 1a is a flowchart of a method for obtaining parameter values of a circuit model according to a first embodiment of the present invention;
FIG. 1b is a schematic diagram of a second order equivalent circuit model suitable for use in embodiments of the present invention;
FIG. 2 is a flowchart of a method for obtaining parameter values of a circuit model according to a second embodiment of the present invention;
FIG. 3 is a schematic structural diagram of an apparatus for obtaining parameter values of a circuit model according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a computer device in the fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1a is a flowchart of a method for obtaining circuit model parameter values according to an embodiment of the present invention, where the embodiment is applicable to fitting EIS data and identifying circuit model parameters, and the method may be executed by a device for obtaining circuit model parameter values, which may be implemented by software and/or hardware and is generally integrated in a computer device.
As shown in fig. 1a, the technical solution of the embodiment of the present invention specifically includes the following steps:
and S110, acquiring Electrochemical Impedance Spectroscopy (EIS) data, and determining a circuit model matched with the EIS data and each element in the circuit model.
The EIS data is test data obtained by performing electrochemical impedance spectroscopy test on the battery at different temperatures and different charge states, namely SOC (State of charge), which is an important parameter for describing the chargeable and dischargeable capacity of the battery in the using process, and different temperatures and different SOCs have different influences on the storage of the lithium battery. The EIS data may include sampling frequency, real axis data, imaginary axis data, and terminal voltage measurements, among others. And fitting the EIS data to obtain the parameter values of each element in the equivalent circuit model of the battery.
Each element in the circuit model refers to a passive element in the circuit model, such as a resistor, a capacitor, an inductor, and the like, and each element in the circuit model is an ideal element.
In the embodiment of the present invention, after the EIS data is obtained, a matched circuit model is selected, for example, the circuit model may be a first-order equivalent circuit model, a second-order equivalent circuit model, or a third-order equivalent circuit model, and the specific type of the selected equivalent circuit model is not limited in this embodiment. FIG. 1b provides a schematic diagram of a second-order equivalent circuit model, as shown in FIG. 1b, where W represents a diffusion resistor, CPE represents a constant phase angle element, and R represents a resistor, and in the second-order equivalent circuit model shown in FIG. 1b, each element is W1、R0、R1、R2、CPE1And CPE2. Therefore, after fitting the EIS data, the W in the second-order equivalent circuit model can be obtained1、R0、R1、R2、CPE1And CPE2The optimum parameter value of (2).
And S120, respectively calculating terminal voltage estimated values according to the parameter values of the plurality of groups of elements, and calculating a fitness function value according to the terminal voltage estimated values.
The terminal voltage estimated value is the battery voltage obtained by calculation according to the parameter values of each element, and the fitness function value is used for searching the global optimal solution of each parameter value.
In the embodiment of the invention, iteration can be performed according to a genetic algorithm to obtain a plurality of groups of parameter values, the terminal voltage estimated value is calculated according to each group of parameter values, and the fitness function value is calculated according to the terminal voltage estimated value and the terminal voltage measured value in the EIS data, so that the optimal parameter value in each group of parameter values is obtained according to the fitness function value of each group of parameter values.
And S130, if the fitness function value corresponding to the target group meets the error threshold condition, taking each parameter value corresponding to the target group as a parameter value of the circuit model.
In the embodiment of the invention, the EIS data comprises a plurality of terminal voltage measurement values, for each group of parameter values, the terminal voltage estimation value is calculated according to the current corresponding to each terminal voltage measurement value, and the fitness function value corresponding to each terminal voltage measurement value is calculated according to the difference value between the terminal voltage estimation value and the terminal voltage measurement value. The fitness function value corresponding to the target group is the sum of the fitness function values corresponding to each terminal voltage measurement value under the parameter value of the target group.
In the embodiment of the present invention, when the fitness function value corresponding to the target group is close to 0, it is considered that the optimal circuit model parameter value is found.
According to the technical scheme of the embodiment, the matched circuit model and elements are determined by obtaining EIS data, the fitness function value corresponding to each group of parameter values is calculated, and when the fitness function value of the target group meets the error threshold condition, the parameter values of the target group are used as the parameter values of the elements in the circuit model. The problem of poor expected effect of circuit model parameters obtained by adopting fitting tools such as ZView and ZSimpWin to fit EIS data and identifying the circuit model parameters in the prior art is solved, and accurate estimation of the circuit model parameter values is realized.
Example two
Fig. 2 is a flowchart of a method for obtaining a circuit model parameter value according to a second embodiment of the present invention, which further embodies the process of calculating a terminal voltage estimated value and the process of calculating a fitness function value on the basis of the second embodiment of the present invention, and adds a process of calculating a cross probability and a variation probability, and changing a parameter value group according to the cross probability and the variation probability.
Correspondingly, as shown in fig. 2, the technical solution of the embodiment of the present invention specifically includes the following steps:
and S210, acquiring Electrochemical Impedance Spectroscopy (EIS) data, and determining a circuit model matched with the EIS data and each element in the circuit model.
S220, setting initial parameter values of a plurality of groups of elements.
In the embodiment of the present invention, before iteratively obtaining a plurality of sets of parameter values through a genetic algorithm, the number of sets of parameter values and the initial values of the parameter values of each set need to be set.
And S230, respectively calculating the voltage estimation value of each element corresponding to each current according to each current and the parameter value of each element in the EIS data.
And S240, calculating a terminal voltage estimated value corresponding to each current according to each voltage estimated value corresponding to each current.
In the embodiment of the invention, the EIS data comprises a plurality of terminal voltage measurement values, the current corresponding to each terminal voltage measurement value is obtained, and the voltage estimation value corresponding to each element is calculated according to the current and the parameter value of each element in the target group. And taking the sum of the voltage estimation values of all the elements as a terminal voltage estimation value corresponding to the current, and calculating the terminal voltage estimation value corresponding to the current matched with each terminal voltage measurement value.
And S250, acquiring terminal voltage measured values matched with the current currents from the EIS data.
And S260, calculating the sum of the difference values between the terminal voltage estimated value and the terminal voltage measured value corresponding to each current as a fitness function value.
In the embodiment of the invention, the difference value between each terminal voltage measured value and the terminal voltage estimated value corresponding to the terminal voltage measured value is calculated, and the difference values are added to obtain the fitness function value corresponding to the target group.
And S270, judging whether the fitness function value corresponding to the target group meets the error threshold condition, if so, executing S2120, and otherwise, executing S280.
And judging whether iteration is needed to be continued or not by judging whether the fitness function value corresponding to the target group meets the error threshold value condition or not. When the fitness function value corresponding to the target group meets the error threshold condition, the optimal solution of the parameter value of each element in the equivalent circuit model is found; if the fitness function values of all the groups do not meet the error threshold condition, the condition shows that no proper parameter values of all elements in the equivalent circuit model exist at the moment, iteration is needed to be carried out continuously according to the genetic algorithm, and a new parameter value group is obtained.
And S280, counting the current iteration times.
In the embodiment of the present invention, it may also be determined whether iteration needs to be continued by setting an iteration time threshold and determining whether the current iteration time exceeds the iteration time threshold.
It should be noted that in this embodiment, in S270, it is determined whether iteration needs to be continued through the fitness function value, and in S280-S290, it is determined whether iteration needs to be continued through the number of iterations, but the specific manner of determining whether iteration needs to be continued is not limited in this embodiment, and it may also be determined whether iteration needs to be continued only through the fitness function value or only through the number of iterations.
And S290, judging whether the current iteration number is larger than or equal to a preset number threshold, if so, executing S2130, and otherwise, executing S2100.
In the embodiment of the present invention, when the current iteration number reaches the preset iteration number threshold, the iteration is stopped, the minimum fitness function value is obtained from the fitness function values corresponding to each set of parameter values, and the parameter value set corresponding to the minimum fitness function value is used as the parameter value of each element in the circuit model.
And S2100, calculating the cross probability and the variation probability.
In the embodiment of the invention, if iteration is needed to be carried out continuously, the cross probability and the mutation probability are calculated. The calculation of the cross probability and the mutation probability is a key factor influencing the performance of the genetic algorithm, the cross probability determines the global search capability of the genetic algorithm, when the cross probability is too small, the search speed is too slow, and when the cross probability is too large, the gene of an elite individual is easily damaged. In the embodiment of the invention, the crossing probability is too small, the speed of obtaining the optimal solution of the circuit model parameter values is slow, and the crossing probability is too large, so that the parameter values with better fitness function value performance in each group of parameter values are easy to damage.
The mutation probability determines the local searching capability of the genetic algorithm, when the mutation probability is too small, a new gene structure is not easy to generate, and when the mutation probability is too large, the genetic algorithm becomes a random searching algorithm. Therefore, the calculation of the cross probability and the mutation probability is the key of the genetic algorithm iteration in the embodiment of the invention.
The crossover probability is calculated by the following formula:
wherein, Pc1、Pc2Is the upper and lower limit values of the cross probability, fbigFor a larger fitness function value, f, corresponding to two groups participating in the cross-over operationavIs the average fitness function value of each set of parameter values, fmaxIs the maximum value of the fitness function value corresponding to each group of parameter values;
optionally, Pc1＝0.9、Pc2＝0.6。
Calculating the mutation probability by the following formula:
wherein, Pm1、Pm2Is the upper and lower limit values of the variation probability, and f is the fitness function value corresponding to the parameter value set of the current variation operation.
Optionally, Pm1＝0.1、Pm2＝0.01。
In the embodiment of the invention, the calculation of the cross probability and the mutation probability is improved, and the genetic algorithm is optimized, so that the global optimal solution can be searched, and the optimal parameter value of each element in the equivalent circuit model can be obtained.
And S2110, selecting a cross group and a variation group from the parameter values of each group according to the cross probability and the variation probability to carry out cross and variation respectively, calculating the terminal voltage estimated value according to the parameter values of each group after cross and variation respectively, and calculating a fitness function value according to the terminal voltage estimated value. Return to execution S270.
In the embodiment of the invention, after the cross probability and the mutation probability are calculated, a cross group and a mutation group are selected from each group. For example, when the iteration parameter values of the current round have 100 sets, the crossover probability is 0.8, and the mutation probability is 0.08, 80 sets of the parameter values of each set are selected as crossover sets to be crossed, 8 sets of the parameter values are selected as mutation sets to be mutated, so that a new parameter value set is obtained, the fitness function value is recalculated according to the new parameter value set, and the iteration of the next round is performed.
And S2120, using the parameter values corresponding to the target group as the parameter values of the circuit model.
In the embodiment of the invention, when a target group with a fitness function value meeting an error threshold condition is found, iteration is stopped, and each parameter value of the target group is used as the optimal parameter value of each element in the equivalent circuit model.
And S2130, acquiring each parameter value corresponding to the minimum fitness function value as a parameter value of the circuit model.
According to the technical scheme of the embodiment, the matched circuit model and elements are determined by obtaining EIS data, the fitness function value corresponding to each group of parameter values is calculated, when no fitness function value meets the error threshold condition, the crossing probability and the variation probability are calculated, the crossing group and the variation group are selected from each group of parameter values for crossing and variation, the fitness function value is recalculated by using the new each group of parameter values until the fitness function value of the target group meets the error threshold condition, and the parameter values of the target group are used as the parameter values of each element in the circuit model. The problem of poor expected effect of circuit model parameters obtained by adopting fitting tools such as ZView and ZSimpWin to fit EIS data and identifying the circuit model parameters in the prior art is solved, and accurate estimation of the circuit model parameter values is realized.
EXAMPLE III
Fig. 3 is a schematic structural diagram of an apparatus for obtaining circuit model parameter values according to a third embodiment of the present invention, where the apparatus includes: an EIS data acquisition module 310, a fitness function value calculation module 320, and a circuit model parameter value determination module 330, wherein:
an EIS data acquisition module 310, configured to acquire electrochemical impedance spectroscopy EIS data, and determine a circuit model matched with the EIS data and each element in the circuit model;
the fitness function value calculating module 320 is used for calculating terminal voltage estimated values according to multiple groups of parameter values of each element and calculating a fitness function value according to the terminal voltage estimated values;
a circuit model parameter value determining module 330, configured to, if it is determined that the fitness function value corresponding to the target group meets the error threshold condition, take each parameter value corresponding to the target group as a parameter value of the circuit model.
On the basis of the above embodiment, the fitness function value calculating module 320 includes:
the voltage estimation value calculation unit is used for respectively calculating the voltage estimation value of each element corresponding to each current according to each current and the parameter value of each element in the EIS data;
and the terminal voltage estimated value calculating unit is used for calculating the terminal voltage estimated value corresponding to each current according to each voltage estimated value corresponding to each current.
On the basis of the above embodiment, the fitness function value calculating module 320 includes:
the terminal voltage measurement value acquisition unit is used for acquiring terminal voltage measurement values matched with current currents in EIS data;
and the fitness function value calculating unit is used for calculating the sum of the difference values between the terminal voltage estimated value and the terminal voltage measured value corresponding to each current as a fitness function value.
On the basis of the above embodiment, the apparatus further includes:
the probability calculation unit is used for calculating the cross probability and the variation probability if the fitness function values corresponding to all the groups do not meet the error threshold condition;
and the parameter set cross mutation unit is used for selecting a cross group and a mutation group from the parameter values of each group according to the cross probability and the mutation probability to respectively cross and mutate, respectively calculating the terminal voltage estimated value according to the parameter values of each group after cross and mutation, and calculating the fitness function value according to the terminal voltage estimated value.
On the basis of the above embodiment, the probability calculation unit is configured to:
the crossover probability is calculated by the following formula:
wherein, Pc1、Pc2Is the upper and lower limit values of the cross probability, fbigFor a larger fitness function value, f, corresponding to two groups participating in the cross-over operationavIs the average fitness function value of each set of parameter values, fmaxIs the maximum value of the fitness function value corresponding to each group of parameter values;
calculating the mutation probability by the following formula:
wherein, Pm1、Pm2Is a variation summaryAnd f is a fitness function value corresponding to the parameter value set of the current variation operation.
On the basis of the above embodiment, the apparatus further includes:
and the initial parameter value setting module is used for setting initial parameter values of a plurality of groups of elements.
On the basis of the above embodiment, the apparatus further includes:
the current iteration frequency counting module is used for counting the current iteration frequency if the fitness function values corresponding to all the groups do not meet the error threshold value condition;
and the current iteration frequency judging module is used for acquiring each parameter value corresponding to the minimum fitness function value as the parameter value of the circuit model if the current iteration frequency is determined to be greater than or equal to the preset frequency threshold value.
The device for obtaining the circuit model parameter value provided by the embodiment of the invention can execute the method for obtaining the circuit model parameter value provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Example four
Fig. 4 is a schematic structural diagram of a computer apparatus according to a fourth embodiment of the present invention, as shown in fig. 4, the computer apparatus includes a processor 70, a memory 71, an input device 72, and an output device 73; the number of processors 70 in the computer device may be one or more, and one processor 70 is taken as an example in fig. 4; the processor 70, the memory 71, the input device 72 and the output device 73 in the computer apparatus may be connected by a bus or other means, and the connection by the bus is exemplified in fig. 4.
The memory 71 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, and modules, such as modules corresponding to the circuit model parameter value obtaining method in the embodiment of the present invention (for example, the EIS data obtaining module 310, the fitness function value calculating module 320, and the circuit model parameter value determining module 330 in the circuit model parameter value obtaining device). The processor 70 executes various functional applications and data processing of the computer device, namely, the above-mentioned circuit model parameter value obtaining method, by executing the software programs, instructions and modules stored in the memory 71. The method comprises the following steps:
acquiring Electrochemical Impedance Spectroscopy (EIS) data, and determining a circuit model matched with the EIS data and each element in the circuit model;
respectively calculating terminal voltage estimated values according to the parameter values of a plurality of groups of elements, and calculating a fitness function value according to the terminal voltage estimated values;
and if the fitness function value corresponding to the target group meets the error threshold condition, taking each parameter value corresponding to the target group as the parameter value of the circuit model.
The memory 71 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 71 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 71 may further include memory located remotely from the processor 70, which may be connected to a computer device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 72 may be used to receive input numeric or character information and generate key signal inputs relating to user settings and function controls of the computer apparatus. The output device 73 may include a display device such as a display screen.
EXAMPLE five
An embodiment of the present invention further provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a method for obtaining parameter values of a circuit model, the method including:
acquiring Electrochemical Impedance Spectroscopy (EIS) data, and determining a circuit model matched with the EIS data and each element in the circuit model;
respectively calculating terminal voltage estimated values according to the parameter values of a plurality of groups of elements, and calculating a fitness function value according to the terminal voltage estimated values;
and if the fitness function value corresponding to the target group meets the error threshold condition, taking each parameter value corresponding to the target group as the parameter value of the circuit model.
Of course, the storage medium containing the computer-executable instructions provided by the embodiments of the present invention is not limited to the method operations described above, and may also perform related operations in the method for obtaining the circuit model parameter values provided by any embodiments of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods according to the embodiments of the present invention.
It should be noted that, in the embodiment of the apparatus for obtaining parameter values of a circuit model, the units and modules included in the apparatus are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method for obtaining a parameter value of a circuit model, comprising:
acquiring Electrochemical Impedance Spectroscopy (EIS) data, and determining a circuit model matched with the EIS data and each element in the circuit model;
respectively calculating terminal voltage estimated values according to the parameter values of a plurality of groups of elements, and calculating a fitness function value according to the terminal voltage estimated values;
and if the fitness function value corresponding to the target group meets the error threshold condition, taking each parameter value corresponding to the target group as the parameter value of the circuit model.
2. The method of claim 1, wherein calculating respective terminal voltage estimates from respective sets of component parameter values comprises:
respectively calculating the voltage estimation value of each element corresponding to each current according to each current and the parameter value of each element in the EIS data;
and calculating the terminal voltage estimated value corresponding to each current according to each voltage estimated value corresponding to each current.
3. The method of claim 2, wherein calculating the fitness function value based on the terminal voltage estimates comprises:
acquiring terminal voltage measured values matched with current currents from EIS data;
and calculating the sum of the difference values between the terminal voltage estimated value and the terminal voltage measured value corresponding to each current as a fitness function value.
4. The method of claim 1, further comprising, after calculating the fitness function value based on the terminal voltage estimates, the steps of:
if the fitness function values corresponding to all the groups do not meet the error threshold condition, calculating the cross probability and the variation probability;
and selecting a cross group and a variation group from the parameter values of each group according to the cross probability and the variation probability to carry out cross and variation respectively, calculating the estimated value of each terminal voltage according to the parameter values of each group after cross and variation respectively, and calculating the fitness function value according to the estimated value of each terminal voltage.
5. The method of claim 4, wherein calculating the cross probability and the variant probability comprises:
the crossover probability is calculated by the following formula:
wherein, Pc1、Pc2Is the upper and lower limit values of the cross probability, fbigFor a larger fitness function value, f, corresponding to two groups participating in the cross-over operationavIs the average fitness function value of each set of parameter values, fmaxIs the maximum value of the fitness function value corresponding to each group of parameter values;
calculating the mutation probability by the following formula:
wherein, Pm1、Pm2Is the upper and lower limit values of the variation probability, and f is the fitness function value corresponding to the parameter value set of the current variation operation.
6. The method of claim 1, further comprising, prior to calculating respective terminal voltage estimates from respective sets of component parameter values:
and setting initial parameter values of a plurality of groups of elements.
7. The method of claim 4, further comprising, after calculating the fitness function value based on the terminal voltage estimates, the steps of:
if the fitness function values corresponding to all groups do not meet the error threshold value condition, counting the current iteration times;
and if the current iteration times are determined to be larger than or equal to the preset times threshold, acquiring each parameter value corresponding to the minimum fitness function value as the parameter value of the circuit model.
8. An apparatus for obtaining a parameter value of a circuit model, comprising:
the EIS data acquisition module is used for acquiring electrochemical impedance spectroscopy EIS data and determining a circuit model matched with the EIS data and each element in the circuit model;
the fitness function value calculation module is used for calculating terminal voltage estimation values according to the parameter values of a plurality of groups of elements and calculating fitness function values according to the terminal voltage estimation values;
and the circuit model parameter value determining module is used for taking each parameter value corresponding to the target group as the parameter value of the circuit model if the fitness function value corresponding to the target group is determined to meet the error threshold condition.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method for obtaining values of parameters of a circuit model according to any of claims 1-7 when executing the program.
10. A storage medium containing computer-executable instructions for performing the method of obtaining circuit model parameter values according to any one of claims 1 to 7 when executed by a computer processor.
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