CN115469230A - OCV-SOC online estimation method and device, computer equipment and storage medium - Google Patents
OCV-SOC online estimation method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The invention discloses an OCV-SOC online estimation method, an OCV-SOC online estimation device, computer equipment and a storage medium, wherein the OCV-SOC online estimation method based on a reconfigurable battery network comprises the following steps: establishing an OCV-SOC mapping relation by adopting a GBDT algorithm; acquiring an OCV value of a battery module to be tested in the reconfigurable battery network; and carrying out online estimation on the SOC value of the battery module to be measured according to the OCV value of the battery module to be measured and the OCV-SOC mapping relation. Therefore, online estimation of the SOC value of the battery module to be measured is achieved through the reconfigurable battery network, and meanwhile, before online estimation of the SOC value of the battery module to be measured is carried out, the OCV-SOC mapping relation is established through the GBDT algorithm, so that fitting errors of the OCV value and the SOC value of the battery module are reduced, fitting accuracy of the OCV-SOC mapping relation is improved, and online estimation accuracy of the SOC value of the battery module to be measured is guaranteed.
Description
Technical Field
The invention relates to the field of battery state estimation and calculation, in particular to an OCV-SOC online estimation method based on a reconfigurable battery network, a computer-readable storage medium, computer equipment and an OCV-SOC online estimation device based on the reconfigurable battery network.
Background
At present, lithium ion batteries have the advantages of fast response, high energy density, long service life and the like, are widely applied to the fields of electric automobiles, mobile communication, aerospace and the like, and are considered as main alternative energy sources for large-scale energy storage application.
In the related art, the SOC of the lithium ion battery is defined as the ratio of the remaining capacity to the maximum available capacity, which is an important index for evaluating the electrical state of the battery, so that accurate SOC estimation can effectively prevent overcharge or overdischarge, improve the energy utilization rate of the battery, ensure safe, reliable, efficient and stable operation of the battery system, and prolong the service life of the battery system as much as possible.
However, the related art has a problem that, in the conventional fixed series/parallel battery module, the terminal voltage and the current of the battery module can only be measured to calculate the internal resistance of the battery by combining with a circuit equivalent model, and then estimate the OCV of the battery module, and the estimation process introduces two error transmissions, so that the fitting accuracy between the OCV and the SOC of the battery module is reduced, the OCV of the battery module cannot be accurately estimated, and the accuracy of system energy control is seriously affected.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art. Therefore, a first object of the present invention is to provide an OCV-SOC online estimation method based on a reconfigurable battery network, which can realize online estimation of an SOC value of a battery module to be measured through the reconfigurable battery network, and at the same time, before online estimation of the SOC value of the battery module to be measured, can ensure online estimation accuracy of the SOC value of the battery module to be measured by establishing an OCV-SOC mapping relationship by using a GBDT algorithm.
A second object of the invention is to propose a computer-readable storage medium.
A third object of the invention is to propose a computer device.
The fourth purpose of the invention is to provide an OCV-SOC online estimation device based on a reconfigurable battery network.
In order to achieve the above object, an OCV-SOC online estimation method based on a reconfigurable battery network according to an embodiment of the first aspect of the present invention includes the following steps: establishing an OCV-SOC mapping relation by adopting a GBDT algorithm; acquiring an OCV value of a battery module to be tested in the reconfigurable battery network; and according to the OCV value of the battery module to be tested and the OCV-SOC mapping relation, carrying out online estimation on the SOC value of the battery module to be tested.
According to the OCV-SOC online estimation method based on the reconfigurable battery network, which is provided by the embodiment of the invention, the OCV-SOC mapping relation is established by adopting a GBDT algorithm, the OCV value of the battery module to be detected in the reconfigurable battery network is further obtained, and the SOC value of the battery module to be detected is estimated online according to the OCV value of the battery module to be detected and the OCV-SOC mapping relation. Therefore, the SOC value of the battery module to be measured is estimated on line through the reconfigurable battery network, and meanwhile, before the SOC value of the battery module to be measured is estimated on line, the OCV-SOC mapping relation is established through the GBDT algorithm, so that the fitting error of the OCV value and the SOC value of the battery module is reduced, the fitting precision of the OCV-SOC mapping relation is improved, and the SOC value on-line estimation precision of the battery module to be measured is ensured.
In addition, the OCV-SOC online estimation method based on the reconfigurable battery network according to the above embodiment of the present invention may further have the following additional technical features:
according to an embodiment of the present invention, the obtaining an OCV value of a battery module to be tested in a reconfigurable battery network includes: and controlling the to-be-tested battery module in the reconfigurable battery network to dynamically separate so as to keep other battery modules in normal working operation while acquiring the OCV value of the to-be-tested battery module.
According to one embodiment of the invention, the establishing the OCV-SOC mapping relationship by using the GBDT algorithm comprises the following steps: collecting multiple sets of SOC-OCV sample data; carrying out data cleaning on the multiple groups of SOC-OCV sample data to remove abnormal sample data; setting operation parameters of the GDBT algorithm; and establishing the OCV-SOC mapping relation according to SOC-OCV sample data after data cleaning and the operation parameters of the GDBT algorithm.
According to one embodiment of the invention, the acquiring multiple sets of SOC-OCV sample data comprises: and under different temperature conditions, charging/discharging the battery module to a preset SOC value, standing for a preset time, and acquiring an OCV value corresponding to the preset SOC value so as to acquire the sample data of the plurality of sets of SOC-OCV.
According to an embodiment of the present invention, the performing data washing on the plurality of sets of SOC-OCV sample data to remove abnormal sample data includes: judging whether the OCV value corresponding to the preset SOC value is larger than a preset voltage upper limit value or smaller than a preset voltage lower limit value; and when the OCV value corresponding to the preset SOC value is larger than the upper limit value of the preset voltage or smaller than the lower limit value of the preset voltage, determining that the OCV value corresponding to the preset SOC value is the abnormal sample data.
According to an embodiment of the present invention, said establishing said OCV-SOC mapping relationship according to SOC-OCV sample data after data washing and operating parameters of said GDBT algorithm comprises: initializing a first decision tree model; performing data iterative fitting on the SOC-OCV sample data by adopting the decision tree model according to the operation parameters of the GDBT algorithm, and acquiring a data fitting error and a model iteration number; whether the data fitting error is larger than the preset threshold or not, or whether the model iteration frequency is smaller than or equal to the preset iteration frequency or not; when the data fitting error is larger than the preset threshold value, or the model iteration times are smaller than or equal to the preset iteration times, establishing a new decision tree model according to the data fitting error and the SOC-OCV sample data, and updating the GBDT model according to the new decision tree model; and when the data fitting error is smaller than the preset threshold value, or the model iteration times are larger than the preset iteration times, establishing the OCV-SOC mapping relation according to the GBDT model updated at the last time.
According to an embodiment of the present invention, the online estimation of the SOC of the battery module to be tested includes: and acquiring a corresponding SOC value from the OCV-SOC mapping relation according to the OCV value of the battery module to be tested, and determining the corresponding SOC value as the SOC value of the battery module to be tested.
In order to achieve the above object, a computer-readable storage medium according to an embodiment of a second aspect of the present invention is provided, on which an OCV-SOC online estimation program based on a reconfigurable battery network is stored, and when executed by a processor, the OCV-SOC online estimation program based on the reconfigurable battery network implements an OCV-SOC online estimation method based on the reconfigurable battery network according to an embodiment of the first aspect.
According to the computer-readable storage medium provided by the embodiment of the invention, when the OCV-SOC online estimation program based on the reconfigurable battery network is executed through the processor, the SOC value of the battery module to be measured can be estimated online through the reconfigurable battery network, and simultaneously, before the SOC value of the battery module to be measured is estimated online, the OCV-SOC mapping relation is established by adopting the GBDT algorithm, so that the fitting error between the OCV value and the SOC value of the battery module is reduced, the fitting precision of the OCV-SOC mapping relation is improved, and the online estimation precision of the SOC value of the battery module to be measured is ensured.
In order to achieve the above object, a computer device according to an embodiment of a third aspect of the present invention includes a memory, a processor, and an OCV-SOC online estimation program based on a reconfigurable battery network, which is stored in the memory and is executable on the processor, and when the processor executes the OCV-SOC online estimation program based on the reconfigurable battery network, the OCV-SOC online estimation method based on the reconfigurable battery network according to the embodiment of the first aspect is implemented.
According to the computer equipment provided by the embodiment of the invention, when the processor executes an OCV-SOC online estimation program based on the reconfigurable battery network, the SOC value of the battery module to be measured can be estimated online through the reconfigurable battery network, and simultaneously, before the SOC value of the battery module to be measured is estimated online, the OCV-SOC mapping relation is established by adopting a GBDT algorithm, so that the fitting error between the OCV value and the SOC value of the battery module is reduced, the fitting precision of the OCV-SOC mapping relation is improved, and the online estimation precision of the SOC value of the battery module to be measured is ensured.
In order to achieve the above object, a reconfigurable-battery-network-based OCV-SOC online estimation apparatus according to a fourth aspect of the present invention includes: the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for establishing an OCV-SOC mapping relation by adopting a GBDT algorithm; the second acquisition module is used for acquiring the OCV value of the battery module to be detected in the reconfigurable battery network; and the online estimation module is used for online estimating the SOC value of the battery module to be measured according to the OCV value of the battery module to be measured and the OCV-SOC mapping relation.
According to the OCV-SOC online estimation device based on the reconfigurable battery network, the OCV-SOC mapping relation is established through the first obtaining module by adopting a GBDT algorithm, the OCV value of the battery module to be detected in the reconfigurable battery network is obtained through the second obtaining module, and then the SOC value of the battery module to be detected is estimated online through the online estimation module according to the OCV value of the battery module to be detected and the OCV-SOC mapping relation. Therefore, the SOC value of the battery module to be measured is estimated on line through the reconfigurable battery network, and meanwhile, before the SOC value of the battery module to be measured is estimated on line, the OCV-SOC mapping relation is established through the GBDT algorithm, so that the fitting error of the OCV value and the SOC value of the battery module is reduced, the fitting precision of the OCV-SOC mapping relation is improved, and the SOC value on-line estimation precision of the battery module to be measured is ensured.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a schematic flow chart of an OCV-SOC online estimation method based on a reconfigurable battery network according to an embodiment of the invention;
FIG. 2 is a schematic flow diagram of an OCV-SOC online estimation method based on a reconfigurable battery network according to an embodiment of the invention;
FIG. 3 is a schematic flow diagram of an OCV-SOC online estimation method based on a reconfigurable battery network according to an embodiment of the invention;
FIG. 4 is a schematic diagram of the voltage level of the battery module under the set test condition;
FIG. 5 is a schematic view illustrating the current level of the battery module under the set test condition;
FIG. 6 is a diagram illustrating fitting results corresponding to different fitting modes under a set test condition;
fig. 7 is a block schematic diagram of an OCV-SOC online estimation device based on a reconfigurable battery network according to an embodiment of the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
An OCV-SOC online estimation method based on a reconfigurable battery network, a computer-readable storage medium, a computer device, and an OCV-SOC online estimation apparatus based on a reconfigurable battery network according to embodiments of the present invention are described below with reference to the accompanying drawings.
Before introducing the OCV-SOC online estimation method based on the reconfigurable battery network and the OCV-SOC online estimation device based on the reconfigurable battery network according to the embodiments of the present invention, the reconfigurable battery network according to the embodiments of the present invention is introduced.
Fig. 1 is a flow chart of an OCV-SOC online estimation method based on a reconfigurable battery network according to an embodiment of the present invention.
As shown in fig. 1, the OCV-SOC online estimation method based on the reconfigurable battery network includes the following steps:
s101, establishing an OCV-SOC mapping relation by adopting a GBDT algorithm.
It should be noted that the GBDT algorithm is an iterative decision tree algorithm, which is composed of a plurality of weak learners (decision trees), and is trained in a direction of reducing residual errors by using a gradient enhancement algorithm, wherein when residual errors are reduced to a certain range, all the weak learners are combined to obtain a final strong learner.
It should be understood that, in some embodiments of the present invention, a GBDT algorithm may be used to perform machine learning on the OCV-SOC mapping relationship to establish the OCV-SOC mapping relationship, so as to reduce an error in fitting the OCV value and the SOC value of the battery module, improve the fitting accuracy of the OCV-SOC mapping relationship, and ensure the online estimation accuracy of the SOC value of the battery module to be tested, compared to a method in which the accuracy of establishing the OCV-SOC mapping relationship is not high due to the adoption of piecewise fitting in an actual project and the artificial decision of the piecewise points also include great uncertainty.
And S102, obtaining an OCV value of the battery module to be tested in the reconfigurable battery network.
It should be understood that, compared to the conventional fixed series/parallel battery modules, a reconfigurable battery network is further introduced in some embodiments of the present invention, specifically, the reconfigurable battery network can overcome the "short plate effect" of the system caused by the conventional fixed series/parallel battery connection manner, and can form battery unit arrays by using high-frequency power electronic switches, and can perform individual and flexible operations on battery cells/modules, so that the connection topology structure between the battery units can be dynamically reconfigured.
S103, estimating the SOC value of the battery module to be tested on line according to the OCV value of the battery module to be tested and the OCV-SOC mapping relation.
Specifically, in some embodiments of the present invention, before the online estimation of the SOC value of the battery module to be measured, the GBDT algorithm is used to establish the OCV-SOC mapping relationship, so as to reduce the fitting error between the OCV value and the SOC value of the battery module, improve the fitting accuracy of the OCV-SOC mapping relationship, further obtain the OCV value of the battery module to be measured in the reconfigurable battery network, and perform the online estimation of the SOC value of the battery module to be measured according to the OCV value and the OCV-SOC mapping relationship of the battery module to be measured, thereby ensuring the online estimation accuracy of the SOC value of the battery module to be measured while achieving the online estimation of the SOC value of the battery module to be measured through the reconfigurable battery network.
Further, the OCV value of the battery module to be tested in the reconfigurable battery network is obtained, and the method comprises the following steps: and controlling the battery module to be tested in the reconfigurable battery network to dynamically separate so as to keep other battery modules in normal operation while acquiring the OCV value of the battery module to be tested.
Specifically, because the reconfigurable battery network has the reconfigurable characteristic, in some embodiments of the present invention, the battery module to be tested in the reconfigurable battery network can be controlled to be dynamically disconnected, so as to obtain the OCV value of the battery module to be tested, and at the same time, keep other battery modules in normal operation, so as to realize online estimation of the SOC value of the battery module to be tested, and at the same time, continuously control other battery modules in the reconfigurable battery network to provide sufficient power supply for the electric equipment, thereby improving the utilization rate of each battery module as much as possible while ensuring reliable operation of the reconfigurable battery network, and avoiding resource waste of the battery modules.
Further, as shown in fig. 2, the GBDT algorithm is used to establish the OCV-SOC mapping relationship, which includes:
s201, collecting multiple sets of SOC-OCV sample data.
It should be appreciated that in some embodiments of the present invention, multiple sets of SOC-OCV sample data may be collected to facilitate machine learning of the OCV-SOC mapping relationship based on sufficient training samples using the GBDT algorithm to establish a more accurate OCV-SOC mapping relationship.
S202, carrying out data cleaning on the plurality of groups of SOC-OCV sample data to remove abnormal sample data.
It should be appreciated that, in some embodiments of the present invention, after the plurality of sets of SOC-OCV sample data are collected, data cleaning may be performed on the plurality of sets of SOC-OCV sample data to remove abnormal sample data, so that the training accuracy of the GBDT algorithm may be further improved.
S203, setting the operation parameters of the GDBT algorithm.
Optionally, the operating parameters of the GDBT algorithm may include a maximum number of iterations of the weak learner, initialized weak learner, loss parameters, and the like.
And S204, establishing an OCV-SOC mapping relation according to SOC-OCV sample data after data cleaning and operation parameters of a GDBT algorithm.
Specifically, in some embodiments of the present invention, SOC-OCV sample data after data cleaning may be used as a training sample of the GDBT algorithm, and the GBDT algorithm is used to perform machine learning on the SOC-OCV sample data after data cleaning based on the operation parameters of the GDBT algorithm, so as to establish an OCV-SOC mapping relationship, so as to perform online estimation on the SOC value of the battery module to be measured subsequently according to the OCV value and the OCV-SOC mapping relationship of the battery module to be measured.
Further, collecting multiple sets of SOC-OCV sample data, comprising: and under different temperature conditions, charging/discharging the battery module to a preset SOC value, standing for a preset time, and acquiring an OCV value corresponding to the preset SOC value so as to acquire multiple sets of SOC-OCV sample data.
Specifically, in some embodiments of the present invention, the battery module may be charged/discharged to a preset SOC value and left standing for a preset time, so as to obtain multiple sets of OCV values corresponding to the preset SOC value under different temperature conditions, so as to collect multiple sets of SOC-OCV sample data, thereby facilitating establishment of an OCV-SOC mapping relationship based on the multiple sets of SOC-OCV sample data by using a GBDT algorithm.
Alternatively, the preset time may be calibrated according to the time taken for the battery module to charge/discharge to the preset SOC value and reach the steady state, for example, the preset time may preferably be 30s.
Specifically, the data cleaning is performed on multiple sets of SOC-OCV sample data to remove abnormal sample data, and the method includes: and when the OCV value corresponding to the preset SOC value is larger than the upper limit value of the preset voltage or smaller than the lower limit value of the preset voltage, determining the OCV value corresponding to the preset SOC value as abnormal sample data.
Specifically, in some embodiments of the present invention, when it is determined that the OCV value corresponding to the preset SOC value is greater than the preset voltage upper limit or less than the preset voltage lower limit, the sample data of the OCV value corresponding to the preset SOC value in the present group may be considered to be outside the normal operating interval of the OVC value of the battery module, and at this time, the OCV value corresponding to the preset SOC value may be determined to be an abnormal sample data, and removed from the plurality of groups of SOC-OCV sample data, so as to facilitate establishing a more accurate OCV-SOC mapping relationship according to the SOC-OCV sample data after data cleaning and the operation parameters of the GDBT algorithm.
Further, as shown in fig. 3, establishing an OCV-SOC mapping relationship according to SOC-OCV sample data after data washing and operating parameters of the GDBT algorithm, including:
s301, initializing a first decision tree model.
Specifically, the algorithm model of the GBDT algorithm may be specifically expressed as:
where x is the input sample, ω is the model parameter, h represents the decision tree, α is the weight of each tree, and T = { (x) for a given training set 1 ,y 1 ),(x 2 ,y 2 ),...,(x N ,y N ) With an input space X satisfying
Output space Y satisfies
Thus, in some embodiments of the present invention, the initialized first decision tree model may be represented as:
wherein, L (y) i And c) is a loss function.
And S302, performing data iterative fitting on SOC-OCV sample data by adopting a decision tree model according to the operation parameters of the GDBT algorithm, and acquiring a data fitting error and the number of model iterations.
In particular, in some embodiments of the present invention, the negative gradient r for each sample may be calculated by the following formula im To apply a negative gradient r im As data fitting error:
s303, judging whether the data fitting error is larger than a preset threshold value or not, or judging whether the iteration times of the model are smaller than or equal to the preset iteration times or not.
Specifically, in some embodiments of the present invention, it may be determined whether the decision tree model has achieved the best fit to the OCV-SOC mapping relationship according to the data fitting error and the number of model iterations.
S304, when the data fitting error is larger than a preset threshold value, or the model iteration frequency is smaller than or equal to the preset iteration frequency, establishing a new decision tree model according to the data fitting error and the SOC-OCV sample data, and updating the GBDT model according to the new decision tree model.
Specifically, in some embodiments of the present invention, when the data fitting error is greater than the preset threshold, or the number of model iterations is less than or equal to the preset number of iterations, it may be considered that the decision tree model does not achieve the best fit to the OCV-SOC mapping relationship, and at this time, a new decision tree model needs to be established according to the data fitting error and SOC-OCV sample data, and the GBDT model needs to be updated according to the new decision tree model, so as to improve the fitting degree of the decision tree model to the OCV-SOC mapping relationship.
Further, in some embodiments of the invention, the data fitting error may be taken as a new value for the sample, and the SOC-OCV sample data (x) i ,r im ) As training data for the next tree to obtain a new regression decision tree f m (X) the leaf node region corresponding thereto is R jm Wherein J =1,2 m And J is m The number of leaf nodes of the mth regression tree and the leaf area c jm The best fit value of (c) is:
further, in some embodiments of the present invention, the GBDT model may be updated according to the new decision tree model, and the updated GBDT model may be represented by the following formula:
s305, when the data fitting error is smaller than a preset threshold value, or the model iteration frequency is larger than the preset iteration frequency, establishing an OCV-SOC mapping relation according to the GBDT model updated for the last time.
Specifically, in some embodiments of the present invention, when the data fitting error is smaller than the preset threshold, or the number of model iterations is greater than the preset number of iterations, it may be considered that the decision tree model achieves the best fit to the OCV-SOC mapping relationship, and at this time, the GBDT model does not need to be updated any more, and the OCV-SOC mapping relationship may be established according to the last updated GBDT model.
Specifically, in some embodiments of the present invention, the OCV-SOC mapping relationship established from the last updated GBDT model may be represented by the following equation:
wherein, F M (x) Corresponding to the SOC value of the battery module to be tested, x corresponding to the OCV value of the battery module to be tested, F 0 (x) Is the initialized first decision tree model.
Taking 1 parallel 16 strings of battery modules as the minimum management and control unit of the reconfigurable battery network as an example, comparing under the test conditions shown in fig. 4 and 5, and adopting an 8-time linear fitting mode as a comparison group to illustrate that the OCV-SOC mapping relationship established by adopting the GBDT algorithm in the embodiment of the present invention has higher fitting accuracy compared with the linear fitting mode in the prior art.
Further, a Mean Absolute Percentage Error (MAPE), a goodness of fit (R2), and a maximum Error value are introduced as evaluation functions of model accuracy, and the expressions are:
where i denotes a sample sequence, for a total of n samples,
for the predicted value, y is the true value,
the mean of the true values.
Specifically, the results of the linear fitting method and the results of the fitting using the GDBT algorithm are shown in fig. 6 and table 1 below:
TABLE 1
As shown in the above Table 1 and FIG. 6, the fitting accuracy of the OCV-SOC mapping relationship established by the GBDT algorithm is greatly improved compared with the fitting accuracy of the OCV-SOC mapping relationship established by the linear fitting method in the prior art.
Further, carry out online estimation to the SOC of the battery module that awaits measuring, include: and acquiring a corresponding SOC value from the OCV-SOC mapping relation according to the OCV value of the battery module to be tested, and determining the corresponding SOC value as the SOC value of the battery module to be tested.
Specifically, in some embodiments of the present invention, after the OCV value of the battery module to be tested is obtained dynamically through the reconfigurable battery network, the SOC value corresponding to the OCV value of the battery module to be tested may be obtained from the OCV-SOC mapping relationship established by using the GDBT algorithm, and the corresponding SOC value is determined as the SOC value of the battery module to be tested, thereby implementing online estimation of the SOC value of the battery module to be tested with high precision.
In summary, according to the OCV-SOC online estimation method based on the reconfigurable battery network provided by the embodiment of the invention, the OCV-SOC mapping relationship is established by using the GBDT algorithm, so as to obtain the OCV value of the battery module to be measured in the reconfigurable battery network, and the SOC value of the battery module to be measured is estimated online according to the OCV value of the battery module to be measured and the OCV-SOC mapping relationship. Therefore, the SOC value of the battery module to be measured is estimated on line through the reconfigurable battery network, and meanwhile, before the SOC value of the battery module to be measured is estimated on line, the OCV-SOC mapping relation is established through the GBDT algorithm, so that the fitting error of the OCV value and the SOC value of the battery module is reduced, the fitting precision of the OCV-SOC mapping relation is improved, and the SOC value on-line estimation precision of the battery module to be measured is ensured.
Based on the OCV-SOC online estimation method based on the reconfigurable battery network according to the foregoing embodiment of the present invention, an embodiment of the present invention further provides a computer-readable storage medium, on which an OCV-SOC online estimation program based on the reconfigurable battery network is stored, and when being executed by a processor, the OCV-SOC online estimation program based on the reconfigurable battery network implements the OCV-SOC online estimation method based on the reconfigurable battery network according to the foregoing embodiment of the present invention.
It should be noted that, when executing the OCV-SOC online estimation program based on the reconfigurable battery network, the computer-readable storage medium according to the embodiment of the present invention can implement the specific implementation of the OCV-SOC online estimation program based on the reconfigurable battery network according to the foregoing embodiment of the present invention, which is not described herein again.
In summary, according to the computer-readable storage medium provided by the embodiment of the invention, when the OCV-SOC online estimation program based on the reconfigurable battery network is executed by the processor, the SOC value of the battery module to be measured can be estimated online through the reconfigurable battery network, and simultaneously, before the SOC value of the battery module to be measured is estimated online, the OCV-SOC mapping relationship is established by using the GBDT algorithm, so that the fitting error between the OCV value and the SOC value of the battery module is reduced, the fitting accuracy of the OCV-SOC mapping relationship is improved, and the online estimation accuracy of the SOC value of the battery module to be measured is ensured.
Based on the OCV-SOC online estimation method based on the reconfigurable battery network according to the embodiment of the present invention, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and an OCV-SOC online estimation program based on the reconfigurable battery network, stored in the memory and operable on the processor, and when the processor executes the OCV-SOC online estimation program based on the reconfigurable battery network, the OCV-SOC online estimation method based on the reconfigurable battery network according to the embodiment of the present invention is implemented.
It should be noted that, when executing the OCV-SOC online estimation program based on the reconfigurable battery network, the computer device according to the embodiment of the present invention can implement a specific implementation of the OCV-SOC online estimation program based on the reconfigurable battery network according to the foregoing embodiment of the present invention, which is not described herein again.
In summary, according to the computer device provided by the embodiment of the invention, when the processor executes the OCV-SOC online estimation program based on the reconfigurable battery network, the SOC value of the battery module to be measured can be estimated online through the reconfigurable battery network, and simultaneously, before the SOC value of the battery module to be measured is estimated online, the OCV-SOC mapping relationship is established by using the GBDT algorithm, so that the fitting error between the OCV value and the SOC value of the battery module is reduced, the fitting accuracy of the OCV-SOC mapping relationship is improved, and the online estimation accuracy of the SOC value of the battery module to be measured is ensured.
Fig. 7 is a block schematic diagram of an OCV-SOC online estimation apparatus based on a reconfigurable battery network according to an embodiment of the present invention.
As shown in fig. 7, the OCV-SOC online estimation apparatus 100 based on the reconfigurable battery network includes: a first acquisition module 10, a second acquisition module 20 and an online estimation module 30.
Specifically, the first obtaining module 10 is configured to establish an OCV-SOC mapping relationship by using a GBDT algorithm; the second obtaining module 20 is configured to obtain an OCV value of a battery module to be tested in the reconfigurable battery network; the online estimation module 30 is configured to perform online estimation on the SOC value of the battery module to be tested according to the OCV value of the battery module to be tested and the OCV-SOC mapping relationship.
Further, the second obtaining module 20 is further configured to control the battery module to be tested in the reconfigurable battery network to dynamically disengage, so that other battery modules keep working normally while obtaining the OCV value of the battery module to be tested.
Further, the first obtaining module 10 is further configured to collect multiple sets of SOC-OCV sample data; carrying out data cleaning on the multiple groups of SOC-OCV sample data to remove abnormal sample data; setting operation parameters of a GDBT algorithm; and establishing an OCV-SOC mapping relation according to SOC-OCV sample data after data cleaning and the operation parameters of the GDBT algorithm.
Further, the first obtaining module 10 is further configured to, under different temperature conditions, charge/discharge the battery module to a preset SOC value and stand for a preset time, and obtain an OCV value corresponding to the preset SOC value to collect multiple sets of SOC-OCV sample data.
Further, the first obtaining module 10 is further configured to determine whether an OCV value corresponding to the preset SOC value is greater than a preset voltage upper limit value or less than a preset voltage lower limit value; and when the OCV value corresponding to the preset SOC value is larger than the upper limit value of the preset voltage or smaller than the lower limit value of the preset voltage, determining the OCV value corresponding to the preset SOC value as abnormal sample data.
Further, the first obtaining module 10 is further configured to initialize the first decision tree model; performing data iterative fitting on SOC-OCV sample data by adopting a decision tree model according to the operation parameters of the GDBT algorithm, and acquiring a data fitting error and the number of model iterations; judging whether the data fitting error is larger than a preset threshold value or not, or whether the number of model iterations is smaller than or equal to the preset number of iterations or not; when the data fitting error is larger than a preset threshold value, or the number of model iterations is smaller than or equal to the preset number of iterations, establishing a new decision tree model according to the data fitting error and SOC-OCV sample data, and updating the GBDT model according to the new decision tree model; and when the fitting error of the data is judged to be smaller than a preset threshold value, or the iteration times of the model are larger than the preset iteration times, establishing an OCV-SOC mapping relation according to the GBDT model updated at the last time.
Further, the online estimation module 30 is further configured to obtain a corresponding SOC value from the OCV-SOC mapping relationship according to the OCV value of the battery module to be tested, and determine the corresponding SOC value as the SOC value of the battery module to be tested.
It should be noted that, the specific implementation of the OCV-SOC online estimation apparatus 100 based on the reconfigurable battery network according to the embodiment of the present invention corresponds to the specific implementation of the OCV-SOC online estimation method based on the reconfigurable battery network according to the embodiment of the present invention, and details are not repeated here.
In summary, according to the OCV-SOC online estimation apparatus based on the reconfigurable battery network provided by the embodiment of the invention, the first obtaining module adopts the GBDT algorithm to establish the OCV-SOC mapping relationship, the second obtaining module obtains the OCV value of the battery module to be tested in the reconfigurable battery network, and the online estimation module performs online estimation on the SOC value of the battery module to be tested according to the OCV value of the battery module to be tested and the OCV-SOC mapping relationship. Therefore, online estimation of the SOC value of the battery module to be measured is achieved through the reconfigurable battery network, and meanwhile, before online estimation of the SOC value of the battery module to be measured is carried out, the OCV-SOC mapping relation is established through the GBDT algorithm, so that fitting errors of the OCV value and the SOC value of the battery module are reduced, fitting accuracy of the OCV-SOC mapping relation is improved, and online estimation accuracy of the SOC value of the battery module to be measured is guaranteed.
It should be noted that the logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Further, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be considered limiting of the invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature "under," "beneath," and "under" a second feature may be directly under or obliquely under the second feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. An OCV-SOC online estimation method based on a reconfigurable battery network is characterized by comprising the following steps:
establishing an OCV-SOC mapping relation by adopting a GBDT algorithm;
acquiring an OCV value of a battery module to be tested in the reconfigurable battery network;
and carrying out online estimation on the SOC value of the battery module to be measured according to the OCV value of the battery module to be measured and the OCV-SOC mapping relation.
2. The OCV-SOC online estimation method based on the reconfigurable battery network according to claim 1, wherein the obtaining of the OCV value of the battery module to be tested in the reconfigurable battery network comprises:
and controlling the to-be-tested battery module in the reconfigurable battery network to dynamically separate so as to keep other battery modules in normal working operation while acquiring the OCV value of the to-be-tested battery module.
3. The OCV-SOC online estimation method based on the reconfigurable battery network according to claim 1, wherein the establishing of the OCV-SOC mapping relationship by adopting the GBDT algorithm comprises:
collecting multiple sets of SOC-OCV sample data;
carrying out data cleaning on the multiple groups of SOC-OCV sample data to remove abnormal sample data;
setting operation parameters of the GDBT algorithm;
and establishing the OCV-SOC mapping relation according to SOC-OCV sample data after data cleaning and the operation parameters of the GDBT algorithm.
4. The OCV-SOC online estimation method based on the reconfigurable battery network according to claim 3, wherein the collecting multiple sets of SOC-OCV sample data comprises:
and under different temperature conditions, charging/discharging the battery module to a preset SOC value, standing for a preset time, and acquiring an OCV value corresponding to the preset SOC value so as to acquire the multiple sets of SOC-OCV sample data.
5. The OCV-SOC online estimation method based on the reconfigurable battery network according to claim 4, wherein the data washing of the multiple sets of SOC-OCV sample data to remove abnormal sample data comprises:
judging whether the OCV value corresponding to the preset SOC value is larger than a preset voltage upper limit value or smaller than a preset voltage lower limit value;
and when the OCV value corresponding to the preset SOC value is larger than the upper limit value of the preset voltage or smaller than the lower limit value of the preset voltage, determining that the OCV value corresponding to the preset SOC value is the abnormal sample data.
6. The OCV-SOC online estimation method based on the reconfigurable battery network according to claim 3, wherein the establishing of the OCV-SOC mapping relationship according to SOC-OCV sample data after data cleaning and the operation parameters of the GDBT algorithm comprises:
initializing a first decision tree model;
performing data iterative fitting on the SOC-OCV sample data by adopting the decision tree model according to the operation parameters of the GDBT algorithm, and acquiring a data fitting error and a model iteration number;
judging whether the data fitting error is larger than the preset threshold value or not, or whether the model iteration frequency is smaller than or equal to the preset iteration frequency or not;
when the data fitting error is larger than the preset threshold value, or the model iteration times are smaller than or equal to the preset iteration times, establishing a new decision tree model according to the data fitting error and the SOC-OCV sample data, and updating the GBDT model according to the new decision tree model;
and when the data fitting error is smaller than the preset threshold value, or the model iteration times are larger than the preset iteration times, establishing the OCV-SOC mapping relation according to the GBDT model updated at the last time.
7. The OCV-SOC online estimation method based on the reconfigurable battery network according to any one of claims 1-6, wherein the online estimation of the SOC of the battery module to be tested comprises:
and acquiring a corresponding SOC value from the OCV-SOC mapping relation according to the OCV value of the battery module to be tested, and determining the corresponding SOC value as the SOC value of the battery module to be tested.
8. A computer-readable storage medium, wherein a reconfigurable-battery-network-based OCV-SOC online estimation program is stored thereon, which when executed by a processor implements the reconfigurable-battery-network-based OCV-SOC online estimation method according to any one of claims 1 to 7.
9. A computer device comprising a memory, a processor and an OCV-SOC online estimation program based on a reconfigurable battery network stored on the memory and executable on the processor, wherein the processor implements the OCV-SOC online estimation program based on the reconfigurable battery network according to any one of claims 1 to 7 when executing the OCV-SOC online estimation program.
10. An OCV-SOC online estimation device based on a reconfigurable battery network, characterized in that the device comprises:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for establishing an OCV-SOC mapping relation by adopting a GBDT algorithm;
the second acquisition module is used for acquiring an OCV value of a battery module to be detected in the reconfigurable battery network;
and the online estimation module is used for online estimating the SOC value of the battery module to be measured according to the OCV value of the battery module to be measured and the OCV-SOC mapping relation.
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