CN112946487B - Parameter identification method and device, storage medium and computer equipment - Google Patents
Parameter identification method and device, storage medium and computer equipment Download PDFInfo
- Publication number
- CN112946487B CN112946487B CN202110535857.3A CN202110535857A CN112946487B CN 112946487 B CN112946487 B CN 112946487B CN 202110535857 A CN202110535857 A CN 202110535857A CN 112946487 B CN112946487 B CN 112946487B
- Authority
- CN
- China
- Prior art keywords
- data
- parameter
- target
- parameters
- state
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Secondary Cells (AREA)
Abstract
The invention discloses a parameter identification method, a parameter identification device, a storage medium and computer equipment, wherein the parameter identification method comprises the following steps: acquiring charge and discharge data and preset conditions; screening target charging and discharging data in a target state from the charging and discharging data according to the preset conditions; obtaining target data through data processing according to the target charging and discharging data; acquiring current data in a parameter library; and constructing a correction coefficient model according to the current data and the target data to obtain target parameter data. According to the technical scheme of the invention, the target parameter data is obtained by deducting the parameters obtained by using the target charging and discharging data in the target state, so that the stability of a battery model is ensured, the robustness of an algorithm is improved, and meanwhile, the long-term prediction accuracy of the battery parameters is ensured.
Description
Technical Field
The present invention relates to the field of battery technologies, and in particular, to a parameter identification method, apparatus, storage medium, and computer device.
Background
The estimation Of the battery State is always an industrial problem Of battery operation, such as observing the remaining battery capacity by using the State Of Charge (SOC) Of the battery, monitoring the battery life by using the State Of Health (SOH) Of the battery, and checking the charging and discharging functions Of the battery by using the State Of Function (SOF) Of the battery. These state estimation methods can be broadly classified into three categories, one is a method based on characterization parameters, such as using internal resistance variation to characterize SOH, and using open-circuit voltage to characterize SOC; the second type is a model-based method, which corrects state quantities by taking a battery model as a trigger and according to the relation between model output and actual output, such as Kalman filtering estimation SOC, particle swarm filtering estimation SOC and the like; the third category is based on data-driven methods, which are simplified, abstracted and simulated processes based on a large amount of test data or real-time data in the early stage, such as estimation of SOC by a neural network algorithm and estimation of SOH by a deep learning algorithm.
Among the three methods, the method based on the characterization parameters is easily influenced by uncertainty factors, and has poor precision and good robustness; the method based on the model has strong dependence on the accuracy of the model, good precision and good robustness; the method based on data driving has the advantages of high algorithm complexity, high dependence on training data, good precision and poor robustness.
The model-based method has strong dependence on model parameters, and because the battery is a nonlinear time-varying strong-coupling system, the reliability of the model parameters in the full life cycle is always an industrial problem and a research direction, and the current battery model comprises an electrochemical model, an equivalent circuit model, an electrothermal model and the like. The equivalent circuit model can be further classified into a Rint model (internal resistance model), a Thevenin model (Withania somnifera model), a PNGV (The Partnership for a New Generation of Vehicles) model, and The like. The equivalent circuit model is a battery system simulated by using components such as capacitors, inductors and resistors. The values of these components accordingly change with battery state of charge, temperature and age. At present, a more traditional method is to use HPPC (Hybrid pulse power train characterization) to test values of a fitting component to obtain a parameter value at a certain moment. However, parameters in a dynamic system used by the battery change along with the temperature and the aging state at any moment, and fixed parameter values cannot meet the requirements. Therefore, automatic parameter identification technology comes, and fitting and searching algorithms such as a least square method, a kalman filter algorithm, a particle swarm filter algorithm and the like are used.
No matter the least square method of forgetting factor used in the above patent, or fitting algorithm or search algorithm such as kalman filter algorithm, genetic algorithm or particle swarm algorithm, etc., the known parameters are generally voltage, current, SOC and capacity. As is well known, the SOC is an estimated value, which has estimation errors, the battery parameters affect SOC estimation, and the SOC affects battery parameter identification in turn, which is a highly coupled process. No matter the battery parameters, the polarization voltage, the SOC and the capacity are greatly deviated, the whole battery identification is dispersed, the model system is unstable, and the robustness is poor. This is also the main reason why the online parameter identification method cannot be applied to practical projects, and the risk is too high.
Disclosure of Invention
The invention aims to provide a parameter identification method, a parameter identification device, a storage medium and computer equipment, which are used for deducing parameters obtained by using target charge and discharge data in a target state to obtain target parameter data, so that the stability of a battery model is ensured, the robustness of an algorithm is improved, and meanwhile, the long-term prediction accuracy of the battery parameters is ensured.
In order to achieve the purpose, the invention provides the following scheme:
a method of parameter identification, the method comprising:
acquiring charge and discharge data and preset conditions;
screening target charging and discharging data in a target state from the charging and discharging data according to the preset conditions;
performing data processing on the target charging and discharging data to obtain target data;
acquiring current data in a parameter library;
and constructing a correction coefficient model according to the current data and the target data to obtain target parameter data.
Optionally, the preset condition is that the battery standing time is longer than a preset time.
Optionally, the parameter library includes a sample parameter library, and after constructing a correction coefficient model according to the current data and the target data and obtaining target parameter data, the method further includes:
and storing the target parameter data into the sample parameter library.
Optionally, the parameter library further includes a preset parameter rule and an initial parameter library, and the constructing a correction coefficient model according to the current data and the target data to obtain target parameter data includes:
determining pre-identification state parameter data;
obtaining a correction coefficient group corresponding to the pre-identification state parameter data according to the current data and the target data;
updating initial data in the initial parameter database to intermediate parameter data according to the correction coefficient group;
and eliminating the parameters which do not meet the preset parameter rule in the intermediate parameter data to obtain the target parameter data.
Optionally, the initial data in the initial parameter library includes a plurality of state parameters and battery parameters corresponding to the state parameters, the pre-identified state parameter data includes a plurality of pre-identified state parameters, the correction coefficient set includes a plurality of correction coefficients corresponding to the pre-identified state parameters, and updating the initial data in the initial parameter library according to the correction coefficient set to obtain intermediate parameter data includes:
taking the battery parameter corresponding to the state parameter which is the same as the pre-identified state parameter as a parameter to be corrected corresponding to the pre-identified state parameter;
and inputting the parameters to be corrected into the correction coefficient model to obtain correction parameters corresponding to the pre-identified state parameters in the intermediate parameters.
Optionally, the initial parameter library further includes state parameter data, and the determining the pre-identified state parameter data includes:
acquiring historical temperature data;
obtaining the corresponding frequency of a plurality of temperature intervals according to the historical temperature data;
according to the frequency corresponding to the temperature intervals, taking the temperature interval with the highest frequency as the temperature interval of the data to be acquired;
and screening the state parameter data in the temperature range of the data to be acquired as the pre-identification state parameter data.
In another aspect, the present invention further provides a parameter identification apparatus, including:
the first acquisition unit is used for acquiring charge and discharge data and preset conditions;
the information determining unit is used for screening target charging and discharging data in a target state from the charging and discharging data according to the preset condition;
the data processing unit is used for carrying out data processing on the target charging and discharging data to obtain target data;
the second acquisition unit is used for acquiring current data in the parameter library;
and the deduction unit is used for constructing a correction coefficient model according to the current data and the target data and acquiring target parameter data.
Optionally, the deduction unit comprises:
the data determining module is used for determining the pre-identification state parameter data;
the deduction module is used for obtaining a correction coefficient group corresponding to the pre-identification state parameter data according to the current data and the target data;
the data updating module is used for updating the initial data in the initial parameter library to obtain intermediate parameter data according to the correction coefficient group;
and the data eliminating module is used for eliminating the parameters which do not meet the preset parameter rule in the intermediate parameter data to obtain the target parameter data.
In another aspect, the present invention further provides a storage medium, where a computer program is stored, and when the computer program is executed by a processor, the processor is enabled to execute the steps of the parameter identification method.
In another aspect, the present invention further provides a computer device, including a memory and a processor, where the memory stores a computer program, and the computer program, when executed by the processor, causes the processor to execute the steps of the above parameter identification method.
According to the parameter identification method, the parameter identification device, the storage medium and the computer equipment, the target parameter data is obtained by deducting the parameters obtained by using the target charging and discharging data in the target state, the stability of a battery model is ensured, the robustness of an algorithm is improved, and meanwhile, the long-term prediction accuracy of the battery parameters is ensured.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings used in the description of the embodiment or the prior art will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art it is also possible to derive other drawings from these drawings without inventive effort.
Fig. 1 is a flowchart of a parameter identification method according to an embodiment of the present invention.
Fig. 2 is a circuit diagram of a Thevenin battery model according to an embodiment of the present invention.
Fig. 3 is a flowchart of a method for constructing a correction coefficient model and acquiring target parameter data according to the current data and the target data provided in the embodiment of the present invention.
Fig. 4 is a flowchart of a method for constructing a correction coefficient model according to the current data and the target data to obtain target parameter data according to an embodiment of the present invention.
Fig. 5 is a flowchart of a method for updating initial data in the initial parameter library to obtain intermediate parameter data according to the correction coefficient set according to an embodiment of the present invention.
Fig. 6 is a flowchart of a method for determining pre-identified state parameter data according to an embodiment of the present invention, wherein the initial parameter library further includes state parameter data.
Fig. 7 is a comparison diagram of a parameter library of battery parameter R0=3.5m Ω deduction and a new battery parameter library recognized in the states of SOH =92%, T =22.5 ℃ and SOC =85% according to the embodiment of the present invention.
Fig. 8 is a comparison diagram of a parameter library of battery parameter R1=3.1m Ω deduction and a new battery parameter library recognized in the states of SOH =92%, T =22.5 ℃ and SOC =85% according to the embodiment of the present invention.
Fig. 9 is a comparison diagram of a parameter library of battery parameter C1=66084F deductions and a new battery parameter library identified in the states of SOH =92%, T =22.5 ℃ and SOC =85% according to the embodiment of the present invention.
Fig. 10 is a block diagram of a parameter identification apparatus according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The invention aims to provide a parameter identification method, a parameter identification device, a storage medium and computer equipment, which are used for deducing parameters obtained by using target charge and discharge data in a target state to obtain target parameter data, so that the stability of a battery model is ensured, the robustness of an algorithm is improved, and meanwhile, the long-term prediction accuracy of the battery parameters is ensured.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
An embodiment of a parameter identification method according to the present invention is described below, and fig. 1 is a flowchart of a parameter identification method according to an embodiment of the present invention. The present specification provides method steps as described in the examples or flowcharts, but may include more or fewer steps based on routine or non-inventive labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. In practice, the system products may be executed sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing) in accordance with the methods described in the embodiments or figures. As shown in fig. 1, the present embodiment provides a parameter identification method, which includes:
s100, acquiring charge and discharge data and preset conditions;
the charge and discharge data may be information measured during charge and discharge of the battery, and may include an open circuit voltage ocv (open circuit voltage) and a state of charge SOC of the battery. The preset condition may be a condition preset by a user for screening charge and discharge data, and the preset condition may include a limit condition on a time of the charge and discharge data.
S200, screening target charge and discharge data in a target state from the charge and discharge data according to a preset condition;
the target state may refer to a state of the battery, the target charge and discharge data in the target state may refer to charge and discharge data in the target state, and the target charge and discharge data screened from the charge and discharge data may be, for example, charge data in which a duration of maintaining the charge state exceeds a preset time, or discharge data in which a duration of maintaining the discharge state exceeds the preset time, and the like. By adopting the parameter data in the target state, the influence of temperature, previous SOC estimation errors, polarization voltage, error covariance and the like can be avoided in the current state, and the relative accuracy of the state parameters is ensured.
S300, carrying out data processing on target charging and discharging data to obtain target data;
wherein the target data may refer to data for identifying a single state of the parameter, and the target data may include ohmic internal resistance R of the battery in the single state0Battery polarization resistance RpAnd/or cell polarization capacitive reactance CpThe single state may be at a certain temperature and a certain SOC, for example, the single state may be at 25 ℃ and a SOC of 0.5. The ohmic internal resistance R of the battery can be obtained by calculation0Battery polarization resistance RpAnd/or cell polarization capacitive reactance Cp. The specific calculation method is as follows, and as shown in fig. 2, the circuit diagram of Thevenin battery model is shown, and the recursion formula of the battery model is as follows:
in the formula (I), the compound is shown in the specification,
，
for system sampling time, UOCAn ideal voltage source that is an "open circuit" battery voltage; r0Ohmic internal resistance of the battery; rpIs the cell polarization impedance; cpIs RpThe capacitive reactance of the surroundings; u shapeLIs the voltage across the battery; i is the battery load current; u shapepIs the polarization voltage.
The above is exemplified by Thevenin battery model, and may be a high-order RC model or other models, which is not limited herein.
S400, acquiring current data in a parameter base;
the parameter library may be a library for storing data used for identifying parameters, and the parameter library may include an initial parameter library and a sample parameter library, where the initial parameter library may be used for storing initial data, the initial data may be initial data input by a user, or initial data formed by calibrating battery parameters through an HPPC test and imported by another system, the sample parameter library may be used for storing sample data, the sample data may be historical data used for algorithm deduction, the historical data may be historical target parameter data, the current data may be data currently stored in the parameter library, the current data may include the initial data and the sample data, and the current data may be obtained by reading from the parameter library.
S500, constructing a correction coefficient model according to the current data and the target data, and acquiring target parameter data.
The current data and the target data are used for constructing a correction coefficient model to obtain target parameter data, and the target parameter data can be battery parameters in a plurality of different states. Specifically, the algorithm of the correction coefficient model may be one of a plurality of algorithms such as an empirical threshold algorithm, a fuzzy neural network algorithm, a rule-based neural network algorithm, and an LSTM neural network algorithm (Long Short-Term Memory network). Target parameter data is obtained by deducting parameters obtained by using target charging and discharging data in a target state, so that the stability of a battery model is ensured, the robustness of an algorithm is improved, and meanwhile, the long-term prediction accuracy of the battery parameters is ensured.
In one possible embodiment, the preset condition may be that the battery is left standing for a time period longer than a preset time period. Specifically, the voltage value obtained by standing for a long time is the OCV, so that the polarization voltage generated by the polarization parameter at the moment can be ensured to be about 0, the influence of the previous time parameter on the state is eliminated, and the internal temperature of the battery and the temperature of the sensor can be relatively close to each other. The identification algorithm is used for accurately estimating certain temperature, SOC and aging state battery parameters, the particle swarm algorithm or the least square method and the like are guaranteed to be converged at certain time, the parameters are relatively accurate at the time, and the battery model is stable at the SOC point.
In a possible implementation manner, fig. 3 is a flowchart of a method after current data and target data are used to construct a correction coefficient model and obtain target parameter data, where the parameter library includes a sample parameter library, and after the current data and the target data are used to construct the correction coefficient model and obtain the target parameter data, the method further includes:
s600, storing the target parameter data into a sample parameter library.
The sample parameter library may be used to store sample data, where the sample data may refer to historical data used for algorithm deduction, and the historical data may be historical target parameter data. The target parameter data can be parameters obtained by identification, and the target parameter data can comprise ohmic internal resistance R of the battery under a plurality of different states0Battery polarization resistance RpAnd/or cell polarization capacitive reactance Cp. The target parameter data is more approximate to the actual value by storing the target parameter data in the sample parameter library to enrich the data in the parameter library for algorithm deduction.
In a possible implementation manner, fig. 4 is a flowchart of a method for constructing a correction coefficient model according to current data and target data and obtaining target parameter data according to the embodiment of the present invention, where the parameter library further includes a preset parameter rule and an initial parameter library, and the method for constructing the correction coefficient model according to the current data and the target data and obtaining the target parameter data includes:
s301, determining pre-identification state parameter data;
the state parameter data may refer to parameter data for representing a plurality of different states of the battery, the pre-identified state parameter data may refer to parameter data of a plurality of states to be identified, the pre-identified state parameter data may include temperature, SOC, and OCV, for example, the temperature is 25 ℃, the SOC is 0.5, and the OCV is 3.643, which are a set of state parameters in the state parameter data, the pre-identified state parameter data may be state parameter data preset by a user as needed, or may be state parameter data obtained by screening a part of the preset state parameter data.
S302, obtaining a correction coefficient group corresponding to the pre-identification state parameter data according to the current data and the target data;
wherein, the correction coefficient group may refer to a group for correcting the original data so that the corrected parameter is closer to the actual value. The set of correction coefficients may be obtained by performing algorithmic deduction using the current data and the target data. Specifically, the deductive algorithm may be one of a plurality of algorithms such as an empirical threshold algorithm, a fuzzy neural network algorithm, a rule-based neural network algorithm, and an LSTM neural network algorithm. And obtaining a correction coefficient group corresponding to the pre-identification state parameter data, namely battery parameters of other SOC points, other temperature points or other aging states through the operation of the correction coefficient model according to the battery parameters under the target state, namely under a certain SOC point, a certain temperature and a certain aging state, namely, realizing the identification of the battery parameters.
S303, updating the initial data in the initial parameter database to the intermediate parameter data according to the correction coefficient group;
s304, parameters which do not meet the preset parameter rule in the intermediate parameter data are removed, and target parameter data are obtained.
The preset parameter rule may be a parameter rule preset by a user, the preset parameter rule may refer to a rule naturally existing between parameters, and the preset parameter rule may include rules between SOC, temperature, and internal resistance parameters, for example: 1) the lower the temperature is, the larger the internal resistance parameter is at the same SOC point; 2) at the same SOC point, the lower the temperature is, the larger the internal resistance difference delta R/1 ℃ per degree is; 3) the smaller the SOH (State Of Health, battery State Of Health) is, the larger the internal resistance is at the same SOC point; 4) at the same SOC point, the smaller the SOH is, the larger the difference delta R/1% of every 1% SOH is; 5) the internal resistance curves at different SOC points have similarities with different aging degrees. Similarly, the polarization internal resistance and the polarization capacitance have a rule with temperature, and the polarization internal resistance and the polarization capacitance also have a rule with SOH, and the specific preset parameter rule is not limited herein. It should be noted that SOH is difficult to obtain as an estimate and can be characterized by time series. The parameter rules are set according to a general rule that battery parameters evolve along with temperature and aging degree, and after target parameter data are guided by the parameter rules, a battery model system is stable.
In one possible implementation, fig. 5 is a flowchart of a method for obtaining intermediate parameter data by updating initial data in an initial parameter library according to a set of correction coefficients, where the initial data in the initial parameter library includes a plurality of state parameters and battery parameters corresponding to the plurality of state parameters, the pre-identified state parameter data includes a plurality of pre-identified state parameters, the set of correction coefficients includes a plurality of correction coefficients corresponding to the plurality of pre-identified state parameters, and updating the initial data in the initial parameter library according to the set of correction coefficients to obtain the intermediate parameter data includes:
s401, taking a battery parameter corresponding to a state parameter which is the same as the pre-identification state parameter as a parameter to be corrected corresponding to the pre-identification state parameter;
s402, inputting the parameters to be corrected into the correction coefficient model to obtain correction parameters corresponding to the pre-identification state parameters in the intermediate parameters.
In the correction coefficient model, the relationship between the parameter to be corrected and the correction parameter may be a linear relationship such as a direct ratio, an inverse ratio, or an exponential relationship.
In a possible implementation manner, fig. 6 is a flowchart of a method for determining pre-identified state parameter data, where the initial parameter library further includes state parameter data, and the determining of the pre-identified state parameter data includes:
s501, acquiring historical temperature data;
the historical temperature data may be temperature collection data of the battery within a preset range before the current time, the historical temperature data may include temperature data collected during parameter identification within one month before the current time and temperature collection time, the historical temperature data may be extracted from a memory for storing the temperature data, and the temperature data in the historical temperature data may be collected by a temperature sensor.
S502, obtaining corresponding frequencies of a plurality of temperature intervals according to historical temperature data;
s503, according to the frequency corresponding to the multiple temperature intervals, taking the temperature interval with the highest frequency as the temperature interval of the data to be acquired.
S504, screening out the state parameter data in the temperature range of the data to be acquired as the pre-identification state parameter data.
The temperature interval with the highest frequency is used as the temperature interval of the data to be acquired, namely the temperature main window of the identified parameter is determined, and under the condition that the accuracy of the identified parameter is ensured, the operation of the temperature state with a larger temperature difference with the temperature at the current moment is reduced, the operation amount is reduced, and the operation efficiency is improved.
The embodiment also provides the following specific implementation flows:
1) calibrating battery parameters through an HPPC test to form initial data, and storing the initial data into an initial parameter library;
for example, battery parameters for a ternary lithium battery are shown in table 1:
TABLE 1
2) Under a certain static state, storing a voltage and current data segment when discharging or charging is started, then estimating battery parameters [ R0_ n, R1_ n and C1_ n ] through a particle swarm algorithm, and recording current state parameters [ CC, T, SOC, OCV, R0, R1 and C1 ];
obtaining battery model parameters by using the following particle swarm search algorithm:
(1) initializing the particle speed and the particle position, wherein the particle is [ R0, R1, C1 ];
(2) calculating the fitness of each particle: using a first order RC cell model, the discretization equation is:
in the formula (I), the compound is shown in the specification,
，Uprepresents the electrochemical polarization voltage, R0Represents the ohmic resistance, R1Represents the polarization impedance, Volt _ pre represents the model output voltage, OCV represents the open circuit voltage.
The battery state is initialized as:
in the formula (I), the compound is shown in the specification,
is terminal voltage before charging or discharging process in a static state. The OCV can be obtained through the SOC and the capacity, and can also be obtained through acquisition under the condition that the length of the data segment participating in parameter identification is reasonable, such as under the condition that the length of the data segment is short, so that the influence of the SOC and the capacity on parameter identification is eliminated.
The polarization state is initialized as:
the residual of the estimated and acquired voltages is expressed as:
the cost function (fitness function) using an intelligent search algorithm such as particle filtering is:
wherein i represents a particle number, and N represents a length of a voltage-current data segment used for parameter identification.
(3) For each particle, comparing the fitness value of each particle with the historical optimal fitness value, and replacing the optimal position of each particle if the fitness value is smaller than the historical optimal fitness value;
(4) comparing the historical optimal fitness value with the population optimal fitness value of each particle, and replacing the population optimal position if the historical optimal fitness value is smaller than the population optimal fitness value;
(5) iteratively updating the speed and position of the particle;
(6) and (3) judging boundary conditions: judging whether the algorithm termination condition is met, if so, finishing the calculation and outputting a result; and if not, returning to the step of calculating the fitness value of each particle for iterative calculation.
3) Deducting and updating the parameter library:
since the state of aging SOH is also a difficult estimate to predict, and its accuracy also affects the deduction of the parameters, it is an option to update the parameters in a time-series sliding manner. Because the SOC has larger fluctuation between 0% and 20%, only 20% to 80% of parameter identification results need to be recorded.
(1) And selecting the length of the sliding window (1 month), recording the identification parameters, and determining the temperature interval of the data to be acquired.
For example: the parameter identification test is carried out within one month, the number of identification points is 5 from 30 degrees to 35 degrees, the number of identification points is 20 from 25 degrees to 30 degrees, the number of identification points is 10 from 20 degrees to 25 degrees, and the number of identification points is 1 from 15 degrees to 20 degrees. Therefore, the temperature window is the temperature interval of the data to be acquired between 20 degrees and 30 degrees.
(2) And obtaining the correction coefficient of each parameter.
Because the parameter curves of different SOC states at the same temperature for different aging states have similarities, the optimal correction coefficients [ φ _ R0, φ _ R1, φ _ C1] are obtained using the least squares method.
For example, the R0 parameter identifies 20 points in the temperature main window as follows:
TABLE 2
The values of the R0 parameters for the corresponding temperatures, corresponding SOC points found in the raw database are as follows in table 3:
TABLE 3
The optimal correction coefficient model in this example is:
using least square method, particle swarm algorithm or other optimal search algorithm to minimize the variance between the identification parameters in the main window and the model output parameters in the original database under the corresponding state, specifically, the optimal correction coefficient in the main window is
Similarly, can calculate
And
。
(3) the entire parameter library is updated with [ φ _ R0, φ _ R1, φ _ C1 ].
It is necessary to particularly explain the case of using multiplying power to update parameters in this example, especially when using a neural network algorithm or other correction coefficient models, by presetting parameter rules to address the condition of breaking the parameter rules.
As shown in fig. 7 to 9, the comparison between the parameter library derived from the battery parameters (R0=3.5m Ω, R1=3.1m Ω, C1=66084F) identified in a certain state (SOH =92%, T =22.5 ℃, SOC = 85%) and the new battery parameter library is performed.
Fig. 10 is a block diagram of a parameter identification apparatus according to an embodiment of the present invention, and the embodiment further provides a parameter identification apparatus, including:
a first obtaining unit 10 configured to obtain charge and discharge data and a preset condition;
the information determining unit 20 is configured to screen target charge and discharge data in a target state from the charge and discharge data according to a preset condition;
the data processing unit 30 is used for performing data processing on the target charging and discharging data to obtain target data;
a second obtaining unit 40, configured to obtain current data in the parameter library;
and the deduction unit 50 is used for constructing a correction coefficient model according to the current data and the target data and acquiring target parameter data.
In one possible embodiment, the deduction unit comprises:
the data determining module is used for determining the pre-identification state parameter data;
the deduction module is used for obtaining a correction coefficient group corresponding to the pre-identification state parameter data according to the current data and the target data;
the data updating module is used for updating the initial data in the initial parameter library to obtain intermediate parameter data according to the correction coefficient group;
and the data eliminating module is used for eliminating the parameters which do not meet the preset parameter rule in the intermediate parameter data to obtain the target parameter data.
The present embodiment also provides a computer storage medium, in which at least one instruction, at least one program, a code set, or an instruction set is stored, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the above-mentioned parameter identification method.
The embodiment also provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the parameter identification method.
According to the parameter identification method, the parameter identification device, the storage medium and the computer equipment, the target parameter data is obtained by deducting the parameters obtained by using the target charging and discharging data in the target state, the stability of a battery model is ensured, the robustness of an algorithm is improved, and meanwhile, the long-term prediction accuracy of the battery parameters is ensured.
It is noted that while for simplicity of explanation, the foregoing method embodiments have been presented as a series of interrelated states or acts, it should be appreciated by those skilled in the art that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Similarly, the modules of the parameter identification device refer to computer programs or program segments for performing one or more specific functions, and the distinction between the modules does not mean that the actual program codes are necessarily separated. Further, the above embodiments may be arbitrarily combined to obtain other embodiments.
In the foregoing embodiments, the descriptions of the embodiments have respective emphasis, and reference may be made to related descriptions of other embodiments for parts that are not described in detail in a certain embodiment. Those of skill in the art will further appreciate that the various illustrative logical blocks, units, and steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate the interchangeability of hardware and software, various illustrative components, elements, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design requirements of the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present embodiments.
The foregoing description has disclosed fully preferred embodiments of the present invention. It should be noted that those skilled in the art can make modifications to the embodiments of the present invention without departing from the scope of the appended claims. Accordingly, the scope of the appended claims is not to be limited to the specific embodiments described above.
Claims (8)
1. A method for parameter identification, the method comprising:
acquiring charge and discharge data and preset conditions;
screening target charging and discharging data in a target state from the charging and discharging data according to the preset conditions;
performing data processing on the target charging and discharging data to obtain target data;
acquiring current data in a parameter library, wherein the parameter library comprises a preset parameter rule and an initial parameter library;
according to the current data and the target data, a correction coefficient model is constructed, and target parameter data are obtained, wherein the method specifically comprises the following steps: determining pre-identification state parameter data; obtaining a correction coefficient group corresponding to the pre-identification state parameter data according to the current data and the target data; updating initial data in the initial parameter database to intermediate parameter data according to the correction coefficient group; and eliminating the parameters which do not meet the preset parameter rule in the intermediate parameter data to obtain the target parameter data.
2. The method according to claim 1, wherein the predetermined condition is that a battery resting time period is longer than a predetermined time period.
3. The method of claim 1, wherein the parameter database comprises a sample parameter database, and the constructing a correction coefficient model according to the current data and the target data and obtaining the target parameter data further comprises:
and storing the target parameter data into the sample parameter library.
4. The method of claim 1, wherein the initial data in the initial parameter database comprises a plurality of status parameters and battery parameters corresponding to the status parameters, the pre-recognition status parameter data comprises a plurality of pre-recognition status parameters, the set of modification coefficients comprises a plurality of modification coefficients corresponding to the pre-recognition status parameters, and the updating the initial data in the initial parameter database according to the set of modification coefficients obtains intermediate parameter data, comprising:
taking the battery parameter corresponding to the state parameter which is the same as the pre-identified state parameter as a parameter to be corrected corresponding to the pre-identified state parameter;
and inputting the parameters to be corrected into the correction coefficient model to obtain correction parameters corresponding to the pre-identified state parameters in the intermediate parameters.
5. The method of claim 1, wherein the initial parameter library further comprises state parameter data, and the determining pre-identified state parameter data comprises:
acquiring historical temperature data;
obtaining the corresponding frequency of a plurality of temperature intervals according to the historical temperature data;
according to the frequency corresponding to the temperature intervals, taking the temperature interval with the highest frequency as the temperature interval of the data to be acquired;
and screening the state parameter data in the temperature range of the data to be acquired as the pre-identification state parameter data.
6. An apparatus for parameter identification, the apparatus comprising:
the first acquisition unit is used for acquiring charge and discharge data and preset conditions;
the information determining unit is used for screening target charging and discharging data in a target state from the charging and discharging data according to the preset condition;
the data processing unit is used for carrying out data processing on the target charging and discharging data to obtain target data;
the second acquisition unit is used for acquiring current data in the parameter library;
the deduction unit is used for constructing a correction coefficient model according to the current data and the target data and acquiring target parameter data;
wherein the deduction unit comprises:
the data determining module is used for determining the pre-identification state parameter data;
the deduction module is used for obtaining a correction coefficient group corresponding to the pre-identification state parameter data according to the current data and the target data;
the data updating module is used for updating the initial data in the initial parameter library to obtain intermediate parameter data according to the correction coefficient group;
and the data eliminating module is used for eliminating the parameters which do not meet the preset parameter rule in the intermediate parameter data to obtain the target parameter data.
7. A storage medium having stored thereon a computer program which, when executed by a processor, causes the processor to carry out the steps of the parameter identification method of any one of claims 1 to 5.
8. A computer arrangement comprising a memory and a processor, the memory having stored thereon a computer program that, when executed by the processor, causes the processor to carry out the steps of the parameter identification method according to any of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110535857.3A CN112946487B (en) | 2021-05-17 | 2021-05-17 | Parameter identification method and device, storage medium and computer equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110535857.3A CN112946487B (en) | 2021-05-17 | 2021-05-17 | Parameter identification method and device, storage medium and computer equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112946487A CN112946487A (en) | 2021-06-11 |
| CN112946487B true CN112946487B (en) | 2021-08-03 |
Family
ID=76233911
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110535857.3A Active CN112946487B (en) | 2021-05-17 | 2021-05-17 | Parameter identification method and device, storage medium and computer equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112946487B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115453371B (en) * | 2022-09-29 | 2023-06-23 | 上海玫克生储能科技有限公司 | Cell data monitoring method, cell data monitoring device, medium and electronic equipment |
| CN115980590B (en) * | 2023-01-10 | 2023-08-08 | 上海玫克生储能科技有限公司 | Electrochemical parameter identification method, device, equipment and storage medium |
| CN117517979B (en) * | 2023-12-19 | 2024-04-05 | 浙江地芯引力科技有限公司 | Battery parameter updating method and device, electronic equipment and storage medium |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103576097A (en) * | 2013-11-19 | 2014-02-12 | 清华大学 | Method and system for estimating SOH of battery |
| DE102015203789A1 (en) * | 2014-03-05 | 2015-09-10 | Ford Global Technologies, Llc | Battery model with robustness to cloud-specific communication problems |
| EP3379278A1 (en) * | 2017-03-20 | 2018-09-26 | ABB Schweiz AG | Battery energy store |
| CN109143074A (en) * | 2018-06-28 | 2019-01-04 | 中国科学院光电研究院 | A kind of power battery model parameter identification method and system |
| CN109633453A (en) * | 2018-12-28 | 2019-04-16 | 东莞钜威动力技术有限公司 | Battery parameter on-line identification method, apparatus and computer readable storage medium |
| CN110320472A (en) * | 2019-05-17 | 2019-10-11 | 枣庄学院 | A kind of self-correction SOC estimation method for mining lithium battery |
| CN111426962A (en) * | 2020-03-23 | 2020-07-17 | 华为技术有限公司 | Battery parameter identification method and terminal |
| CN111537888A (en) * | 2020-05-09 | 2020-08-14 | 国网福建省电力有限公司莆田供电公司 | Data-driven echelon battery SOC prediction method |
| CN111736082A (en) * | 2020-08-24 | 2020-10-02 | 杭州华塑加达网络科技有限公司 | Alternating current impedance spectrum testing method, device and medium |
| CN112394287A (en) * | 2021-01-20 | 2021-02-23 | 杭州华塑科技股份有限公司 | Battery open circuit detection device and detection method thereof |
| CN112444749A (en) * | 2020-11-06 | 2021-03-05 | 南京航空航天大学 | Lithium battery state of charge joint estimation method based on temperature correction model |
| CN112526348A (en) * | 2020-11-18 | 2021-03-19 | 南通大学 | Battery model parameter identification method based on multi-innovation recursive Bayesian algorithm |
| CN112685356A (en) * | 2021-03-11 | 2021-04-20 | 杭州华塑科技股份有限公司 | Data processing method, device, equipment and storage medium |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102508167B (en) * | 2011-10-25 | 2014-08-06 | 上海交通大学 | Device and method for automatically testing and calibrating parameters of battery management systems |
| CN105652210B (en) * | 2016-01-04 | 2019-07-26 | 惠州市蓝微新源技术有限公司 | One kind carrying out the modified method and system of SOC according to battery status amount change procedure |
-
2021
- 2021-05-17 CN CN202110535857.3A patent/CN112946487B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103576097A (en) * | 2013-11-19 | 2014-02-12 | 清华大学 | Method and system for estimating SOH of battery |
| DE102015203789A1 (en) * | 2014-03-05 | 2015-09-10 | Ford Global Technologies, Llc | Battery model with robustness to cloud-specific communication problems |
| EP3379278A1 (en) * | 2017-03-20 | 2018-09-26 | ABB Schweiz AG | Battery energy store |
| CN109143074A (en) * | 2018-06-28 | 2019-01-04 | 中国科学院光电研究院 | A kind of power battery model parameter identification method and system |
| CN109633453A (en) * | 2018-12-28 | 2019-04-16 | 东莞钜威动力技术有限公司 | Battery parameter on-line identification method, apparatus and computer readable storage medium |
| CN110320472A (en) * | 2019-05-17 | 2019-10-11 | 枣庄学院 | A kind of self-correction SOC estimation method for mining lithium battery |
| CN111426962A (en) * | 2020-03-23 | 2020-07-17 | 华为技术有限公司 | Battery parameter identification method and terminal |
| CN111537888A (en) * | 2020-05-09 | 2020-08-14 | 国网福建省电力有限公司莆田供电公司 | Data-driven echelon battery SOC prediction method |
| CN111736082A (en) * | 2020-08-24 | 2020-10-02 | 杭州华塑加达网络科技有限公司 | Alternating current impedance spectrum testing method, device and medium |
| CN112444749A (en) * | 2020-11-06 | 2021-03-05 | 南京航空航天大学 | Lithium battery state of charge joint estimation method based on temperature correction model |
| CN112526348A (en) * | 2020-11-18 | 2021-03-19 | 南通大学 | Battery model parameter identification method based on multi-innovation recursive Bayesian algorithm |
| CN112394287A (en) * | 2021-01-20 | 2021-02-23 | 杭州华塑科技股份有限公司 | Battery open circuit detection device and detection method thereof |
| CN112685356A (en) * | 2021-03-11 | 2021-04-20 | 杭州华塑科技股份有限公司 | Data processing method, device, equipment and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| A techniquefordynamicbatterymodelidentificationinautomotive applications usinglinearparametervaryings;Y. Hu;《Control Engineering Practice》;20091231(第17期);第1190-1201页 * |
| 锂离子电池参数辨识与SOC估算研究;朱浩 等;《湖南大学学报（自然科学版)》;20140331;第41卷(第3期);第37-42页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112946487A (en) | 2021-06-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112946487B (en) | Parameter identification method and device, storage medium and computer equipment | |
| Yang et al. | A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve | |
| US10838010B2 (en) | Automatic method for estimating the capacitance of a cell of a battery | |
| JP2023518778A (en) | Method and apparatus for determining battery state of charge, battery management system | |
| CN111679199B (en) | Lithium ion battery SOC estimation method and device | |
| JP7157909B2 (en) | Battery capacity estimation method and battery capacity estimation device | |
| JP2017538931A5 (en) | ||
| WO2021189146A1 (en) | Apparatus and methods for testing electrochemical systems | |
| CN112904208A (en) | Method and system for estimating open circuit voltage of battery cell | |
| CN109669132A (en) | A kind of battery charge state estimation method based on variation Bayesian filter | |
| US11143705B2 (en) | Method and device for detecting battery cell states and battery cell parameters | |
| CN114035072A (en) | Battery pack multi-state joint estimation method based on cloud edge cooperation | |
| Zhu et al. | The SOH estimation of LiFePO4 battery based on internal resistance with Grey Markov Chain | |
| CN114441984A (en) | Lithium battery health state estimation method | |
| CN114114038A (en) | Lithium battery SOC and available capacity joint estimation method under full-life and full-temperature conditions | |
| CN115327417A (en) | Early warning method and system for abnormity of power battery monomer and electronic equipment | |
| CN112433154A (en) | Lithium ion battery SOC estimation algorithm based on FFRLS and EKF | |
| CN115128466A (en) | Battery SOC prediction method, device, equipment and medium based on digital twinning | |
| JP5847685B2 (en) | Parameter identification apparatus and identification method for continuous time system | |
| Rodríguez-Iturriaga et al. | A novel Dual Fractional-Order Extended Kalman Filter for the improved estimation of battery state of charge | |
| CN115935206A (en) | SOC estimation method and device of energy storage system, equipment and storage medium | |
| CN113125965B (en) | Method, device and equipment for detecting lithium separation of battery and storage medium | |
| CN112557925B (en) | Lithium ion battery SOC estimation method and device | |
| Takyi-Aninakwa et al. | Enhanced extended-input LSTM with an adaptive singular value decomposition UKF for LIB SOC estimation using full-cycle current rate and temperature data | |
| CN106154170B (en) | The evaluation method and system of battery remaining power |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |