CN114200327A - Method for estimating SOC of single battery in battery pack by considering multi-factor influence - Google Patents
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Abstract
A method for estimating the SOC of a single battery in a battery pack by considering multi-factor influence relates to a battery SOC estimation method. Performing an experiment on a single battery of the same type in the battery pack to obtain source domain data; migrating and transforming the source domain data and the target domain data by using a migration learning framework, wherein the migration learning framework comprises: feature enhancement, feature compression, and MPD adaptation; modeling the transformed source domain data by using a data driving algorithm, and predicting the SOC of the transformed target domain data to obtain an SOC estimation value. The method is based on a data-driven algorithm and adopts a transfer learning method to solve the adverse effect of multiple factors such as temperature and battery aging state on SOC estimation, and provides a transfer learning framework simultaneously adapting MPD and CPD aiming at the problems of the existing transfer learning method, and the method is simple and requires less experimental data.
Description
Technical Field
The invention relates to a battery SOC estimation method, in particular to a method for estimating the SOC of a single battery in a battery pack by considering multi-factor influence, and belongs to the technical field of battery research and development.
Background
The rapid development of the electric automobile effectively relieves the energy crisis and the environmental deterioration, and the power source of the electric automobile is a battery pack consisting of a large number of single batteries, wherein the State of Charge (SOC) of each single battery in the battery pack is accurately estimated, so that the method is of great importance to the safety of the electric automobile, and can also provide accurate information for designing a balance strategy and guaranteeing the endurance mileage of the electric automobile.
Due to the differences in different production processes and the environments to which they are subjected, there is inevitably an inconsistency between the individual batteries in the battery pack. Current methods for estimating the SOC of a battery cell include an Equivalent Circuit Model (ECM) based method, an electrochemical Model based method, a data-driven method, and the like, and if the above methods are applied to all battery cells in a battery pack, a large amount of computing power and storage space are required. At present, the most common SOC estimation method for each battery cell in a battery pack is based on an average difference model, but due to the inconsistency of the battery cells, the "average battery" model cannot well represent the entire battery pack, and most importantly, the above methods are proposed and verified under the conditions of constant temperature and fixed aging state, however, different influence factors exist in practical application, such as temperature and battery aging state, which may cause the existing estimation method to generate larger deviation or even not be applicable, for example, different influence factors may cause the parameter change of the ECM, in addition, although the data driving method does not need to consider the complex reaction principle inside the battery, different influence factors need to mix all data for modeling, which may greatly increase the complexity of the model and the storage space of the model. In fact, different influencing factors change the edge Probability Distribution (MPD) and Conditional Probability Distribution (CPD) of the data, so that a single data-driven model is no longer suitable for other influencing factors.
The core of the transfer learning is to find the similarity between the source domain data and the target domain data, so as to smoothly realize the transfer of knowledge. However, at present, the migration learning method based on the example is effective when the distribution difference between the two data domains is small, the migration learning method based on the characteristics assumes that the MPDs of the data domains are the same, and only adapts to the CPD of the data domains, the migration learning method based on the model needs to adjust the hyper-parameters in the model, and it is still assumed that the MPDs between the data domains are the same, so that it is difficult to obtain a good migration effect for the above problem in practical application. Therefore, it is necessary to improve the conventional transfer learning method and provide an SOC estimation method considering multi-factor influence on the basis of the conventional transfer learning method to solve the defects of the conventional SOC estimation method.
Disclosure of Invention
In order to solve the defects in the background art, the invention provides a method for estimating the SOC of a single battery in a battery pack in consideration of multi-factor influence, which is based on a data-driven algorithm and adopts a transfer learning method to solve the adverse influence of the multi-factor such as temperature and battery aging state on SOC estimation, and provides a transfer learning framework for simultaneously adapting MPD and CPD aiming at the problems of the existing transfer learning method, wherein the method is simple and the required experimental data are few.
In order to achieve the purpose, the invention adopts the following technical scheme: a method for estimating the SOC of a single battery in a battery pack considering multi-factor influence comprises the following steps:
the method comprises the following steps: performing an experiment on a single battery of the same type in the battery pack to obtain source domain data;
step two: migrating and transforming the obtained source domain data and the target domain data of the single battery by using a migration learning frame;
the migration learning framework specifically comprises:
1) feature enhancement
In order to obtain the implicit mapping relationship between the input variables and the target variables in the source domain data and the target domain data, the input variables containing MPD information are combined with the target variables containing CPD information, and a feature enhancement matrix simultaneously containing CPD and MPD information is obtained as follows:
in the formula (I), the compound is shown in the specification,
representing an input variable having m n-dimensional features,
representing output variables with m 1-dimensional features,
representing an enhancement matrix having m n + 1-dimensional features;
2) feature compression
Feature enhancement matrix by transformation matrix W based on principle of principal component analysis
Mapping to a new space to explicitly obtain implicit mapping between input variables and output variables in source domain data and target domain data to maximize
Is defined as an optimization problem, namely:
solving the optimization problem by using a Lagrange function to obtain a transformation matrix W, and converting the source domain data and the target domain data through the transformation matrix W to obtain a characteristic compression matrix as follows:
in the formula, WsA transformation matrix, W, representing source domain datatA transformation matrix representing the target domain data,
an enhancement matrix representing the source domain data,
an enhancement matrix representing the target domain data,
a compression matrix representing the source domain data,
a compression matrix representing target domain data;
3) MPD adaptation
MPD adaptation is carried out on the transformed source domain data and the transformed target domain data by using the existing transfer learning method;
step three: modeling the source domain data converted in the step two by using a data driving algorithm, and predicting the SOC of the target domain data converted in the step two, thereby obtaining the SOC estimation value of the single battery in the battery pack.
Compared with the prior art, the invention has the beneficial effects that: the invention provides a migration learning framework simultaneously adaptive to MPD and CPD (maximum power consumption) aiming at the problems of the existing migration learning method, source domain data and target domain data are effectively converted through the migration learning framework, the method is suitable for SOC (state of charge) estimation under different influence factors and different working conditions, the migration and prediction effects are better, the source domain data after migration are modeled based on a data driving algorithm, the target domain data after migration are predicted to obtain the SOC of a single battery, the complicated reaction mechanism in the battery is not required to be considered, the adverse effects of influence factors such as different temperatures and different aging states are not required to be considered, the adaptability is strong, only a small amount of experiments of single batteries of the same type in a battery pack under any environment are required to obtain the source domain data, the method is not required to be put forward and verified under the conditions such as constant temperature and fixed aging state, and the like in the existing method, and resources and time required by the experiments, the complexity of models and the occupied resources are greatly reduced compared with the existing method The method is simple and efficient, and has good application prospect.
Drawings
FIG. 1 is a block flow diagram of the present invention;
FIG. 2 is a comparison of SOC estimation results for various methods at different temperatures in the example;
FIG. 3 is a comparison of SOC estimation results of methods under different aging states in the example;
FIG. 4 is a comparison of SOC estimation results for various methods under different temperatures and aging conditions in the examples.
Detailed Description
The technical solutions in the present invention will be described clearly and completely with reference to the accompanying 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 invention, rather than all embodiments, and all other embodiments obtained by those skilled in the art without any creative work based on the embodiments of the present invention belong to the protection scope of the present invention.
With reference to the flowchart shown in fig. 1, a method for estimating SOC of a single battery in a battery pack considering multi-factor influence includes the following steps:
the method comprises the following steps: performing an experiment on a single battery of the same type in a battery pack to obtain source domain data, wherein the source domain data comprises an accurate SOC target variable value;
step two: migrating and transforming the obtained source domain data and the target domain data of the single battery by using a migration learning frame;
the migration learning framework specifically comprises:
1) feature enhancement
In order to obtain the implicit mapping relationship between the input variables and the target variables in the source domain data and the target domain data, the input variables containing MPD information are combined with the target variables containing CPD information, and a feature enhancement matrix simultaneously containing CPD and MPD information is obtained as follows:
in the formula (I), the compound is shown in the specification,
representing an input variable having m n-dimensional features,
representing output variables with m 1-dimensional features,
representing an enhancement matrix having m n + 1-dimensional features;
2) feature compression
Obtaining a feature enhancement matrix
Then, based on principle of principal component analysis, the feature enhancement matrix is obtained by transforming the matrix W
Mapping to a new space to explicitly obtain the implicit mapping relationship between the input variables and the output variables in the source domain data and the target domain data, and in order to retain most of the information of the source domain data and the target domain data in the new space, maximizing the mapping relationship
Can thus be defined as an optimization problem, namely:
the optimization problem is solved by using a Lagrangian function, namely:
by deriving W, we can obtain:
wherein W represents a group consisting of
A matrix of eigenvectors after eigen decomposition, - λ denotes a matrix of
Feature decompositionThe latter eigenvalues form a matrix which,
after obtaining the transformation matrix W, converting the source domain data and the target domain data through the transformation matrix W to obtain a feature compression matrix as follows:
in the formula, WsA transformation matrix, W, representing source domain datatA transformation matrix representing the target domain data,
an enhancement matrix representing the source domain data,
an enhancement matrix representing the target domain data,
a compression matrix representing the source domain data,
a compression matrix representing target domain data;
3) MPD adaptation
After a feature compression matrix with an implicit mapping relation between input variables and output variables in source Domain data and target Domain data is obtained, MPD Adaptation is performed on the transformed source Domain data and target Domain data by using an existing migration learning method, such as migration Component Analysis (TCA), Geodesic Flow Kernel (GFK), Maximum Independent Domain Adaptation (MIDA) and the like;
step three: through the migration learning framework in the second step, firstly, input variables (temperature, voltage and current) of source domain data and target domain data and a target variable represented by SOC calculated by an ampere-hour integration method are subjected to feature enhancement, wherein the target variable of the source domain data is an accurate SOC value, then, after feature compression and MPD adaptation are carried out on a feature enhancement matrix, modeling is carried out on the source domain data converted in the second step by using a data driving algorithm, and then, SOC prediction is carried out on the target domain data converted in the second step, so that an SOC estimation value of a single battery in a battery pack is obtained.
Examples
The present embodiment is verified based on actual battery pack experimental data, wherein the number of the single batteries in the battery pack is 100, the rated capacity of the battery pack is 96Ah, the charge-discharge cut-off voltages are 420v and 250v, respectively, the rated capacity of the single battery is 3.2Ah, the battery type is panasonic NCR18650BD, and the anode and cathode materials are li (nicomn) O, respectively2And graphite, including capacity test experiments, pulse test experiments and NEDC condition test experiments at different temperatures (-10 ℃, 15 ℃ and 45 ℃) and different aging states (100% SOH (State of Health) and 95% SOH).
The MPD Adaptation method based on the Long Short Term Memory (LSTM) neural network, the Geodesic Flow Kernel (GFK) and the Maximum Independent Domain Adaptation (MIDA) is experimentally verified at different temperatures and/or different aging states, and the ampere-hour integration method (Ah), the LSTM method, the GFK-based LSTM (GFK + LSTM) method, the GFK-based LSTM (IGFK + LSTM) method, the GFK-based LSTM (LSTM + LSTM) method, the MIDA-based LSTM (MIDA + LSTM) method and the migration learning framework-based LSTM (IDA) method are used to evaluate the ampere-hour integration method (Ah), the LSTM method, the GFK-based LSTM (GFK + LSTM) method, the GFK-based LSTM (IGFK + LSTM) method, and the migration learning framework of the present invention.
Referring to fig. 2, detailed values of SOC estimation results of the respective methods are shown in table 1 below, where the experimental data of the cell 1 (100% SOH) at-10 ℃ is used as the source domain data, and the experimental data of the cell 1 (100% SOH) at 15 ℃ is used as the target domain data:
TABLE 1 SOC estimation results for various methods at different temperatures
Referring to fig. 3, detailed values of SOC estimation results of the respective methods using the experimental data of the single cell 10 at 15 ℃ (100% SOH) as the source domain data and the experimental data of the single cell 10 at 15 ℃ (95% SOH) as the target domain data are shown in table 2 below:
TABLE 2 SOC estimation results of methods under different aging states
Referring to fig. 4, detailed values of SOC estimation results of the respective methods are shown in table 3 below, where the experimental data of the unit cell 10 (100% SOH) at-10 ℃ is used as the source domain data, and the experimental data of the unit cell 80 (95% SOH) at 45 ℃ is used as the target domain data:
TABLE 3 SOC estimation results for each method at different temperatures and different aging states
The SOC estimation method provided by the invention firstly obtains source domain data through experiments of single batteries of the same type in a battery pack under any environment, and then uses a provided transfer learning framework to transfer and convert the source domain data and target domain data of the single batteries in the battery pack, wherein the transfer learning framework comprises the following steps: and finally, modeling the migrated source domain data by using a data driving algorithm, and predicting the migrated target domain data to obtain an SOC estimation result of the single battery. As can be seen from fig. 2 to 4 and tables 1 to 3, the present invention can obtain an accurate SOC estimation result under the condition of considering the influence of multiple factors, and the migration effect of the proposed migration learning framework is far better than that of the existing migration learning method.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (3)
1. A method for estimating the SOC of a single battery in a battery pack in consideration of multi-factor influence is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: performing an experiment on a single battery of the same type in the battery pack to obtain source domain data;
step two: migrating and transforming the obtained source domain data and the target domain data of the single battery by using a migration learning frame;
the migration learning framework specifically comprises:
1) feature enhancement
In order to obtain the implicit mapping relationship between the input variables and the target variables in the source domain data and the target domain data, the input variables containing MPD information are combined with the target variables containing CPD information, and a feature enhancement matrix simultaneously containing CPD and MPD information is obtained as follows:
in the formula (I), the compound is shown in the specification,
representing an input variable having m n-dimensional features,
representing output variables with m 1-dimensional features,
representing an enhancement matrix having m n + 1-dimensional features;
2) feature compression
Feature enhancement matrix by transformation matrix W based on principle of principal component analysis
Mapping to a new space to explicitly obtain implicit mapping between input variables and output variables in source domain data and target domain data to maximize
Is defined as an optimization problem, namely:
solving the optimization problem by using a Lagrange function to obtain a transformation matrix W, and converting the source domain data and the target domain data through the transformation matrix W to obtain a characteristic compression matrix as follows:
in the formula, WsA transformation matrix, W, representing source domain datatA transformation matrix representing the target domain data,
enhanced matrix representing source domain data，
An enhancement matrix representing the target domain data,
a compression matrix representing the source domain data,
a compression matrix representing target domain data;
3) MPD adaptation
MPD adaptation is carried out on the transformed source domain data and the transformed target domain data by using the existing transfer learning method;
step three: modeling the source domain data converted in the step two by using a data driving algorithm, and predicting the SOC of the target domain data converted in the step two, thereby obtaining the SOC estimation value of the single battery in the battery pack.
2. The method of claim 1 for estimating SOC of a single battery in a battery pack considering multi-factor influence, wherein: in the feature compression of the second step, solving the optimization problem by using the lagrangian function to obtain the transformation matrix W specifically comprises:
by deriving W, we can obtain:
wherein W represents a group consisting of
A matrix of eigenvectors after eigen decomposition, - λ denotes a matrix of
And forming a matrix by the eigenvalues after the characteristic decomposition.
3. The method of claim 1 for estimating SOC of a single battery in a battery pack considering multi-factor influence, wherein: in the MPD adaptation in the second step, the existing migration learning method adopts migration component analysis TCA, geodesic flow kernel GFK or maximum independent domain adaptation MIDA.
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