Patent Description:
The present application relates to the technical field of SOC estimation of lithium batteries, and for example, relates to a method, device and computer readable storage medium for estimating SOC of a lithium battery.

With the development of lithium battery manufacturing and integration technology, advantages of lithium-ion batteries, such as a high energy density, a high unit voltage and a long cycle life, have been continuously excavated, and thus lithium-ion batteries have become the mainstream choice of new energy vehicles, energy storage power supplies and other systems. For energy storage power supplies, how to estimate the state of charge (SOC) of lithium batteries accurately and in real time is one of the core technologies of the energy storage power supplies. Accurate SOC estimation can avoid abnormal working modes such as over-charge and over-discharge of batteries, prolong the service life of batteries and reduce the incidence of safety accidents.

However, in the prior art, mapping relationships between battery voltage, current, temperature or the like and SOC are usually obtained by off-line training with machine learning algorithms, and then the measured data is substituted into the model to calculate the estimated SOC value. However, this method usually constructs a single global model, which is not conducive to representing the local process characteristics of SOC under multiple working conditions, and leads to insufficient accuracy and poor reliability of SOC estimation.

Relevant prior art is disclosed in <CIT>, <CIT>, <CIT>, <CIT> and <CIT>.

A technical problem to be solved by the present application is to provide a method and a computer readable storage medium for estimating SOC of a lithium battery, so as to improve the accuracy and reliability of the estimated SOC value of the lithium battery.

To solve the above technical problem, a technical solution adopted by an embodiment of the present application is to provide a method for estimating SOC of a lithium battery as defined in independent claim <NUM>, and the method comprises steps of:.

Preferably, the state data of the lithium battery comprises at least one of charging and discharging current, terminal voltage and temperature of the lithium battery.

Preferably, the step of performing clustering analysis on the sample set to obtain a plurality of sample subsets comprises:
performing clustering analysis on the sample set to obtain a plurality of sample subsets by using the K-means algorithm, which comprises steps of:.

Preferably, the step of establishing a corresponding sub-model for each of the sample subsets to obtain sub-model functions of the plurality of sample subsets comprises:.

Preferably, the operation of adding the state data of a sample to be tested respectively into the state data of each of the sample subsets and calculating a change value of the state data of each of the sample subsets before and after the adding operation comprises:.

Preferably, the step of selecting at least one sub-model close to the sample to be tested as the selected sub-model according to the change value comprises:.

As defined in independent claim <NUM>, the step of assigning a weight to the selected sub-model comprises:.

Preferably, the step of calculating the SOC value of the sample to be tested is to calculate the SOC value through the following formula: <MAT>.

To solve the above technical problem, another technical solution adopted by an embodiment of the present application, which is not encompassed by the wording of the claims but is considered as useful for understanding the invention, is to provide a device for estimating SOC of a lithium battery, and the device comprises:.

To solve the above technical problem, yet another technical solution adopted by an embodiment of the present application is to provide a computer readable storage medium having computer executable instructions stored therein, and the computer executable instructions enable a computer to execute the method described above.

To solve the above technical problem, which is not encompassed by the wording of the claims but is considered as useful for understanding the invention, yet another technical solution adopted by an embodiment of the present application is to provide an electronic equipment which comprises:.

To solve the above technical problem, yet another technical solution adopted by an embodiment of the present application is to provide a computer program product comprising a computer program stored on a nonvolatile computer readable storage medium, the computer program comprises program instructions which, when executed by an electronic equipment, enable the electronic equipment to execute the method as described above.

Embodiments of the present application provide a method and a computer readable storage medium for estimating SOC of a lithium battery. For the method, device and computer readable storage medium for estimating SOC of the lithium battery, state data and corresponding SOC values of lithium batteries under different working conditions are collected to establish a sample set, and clustering analysis is performed on the sample set to obtain a plurality of sample subsets; then a corresponding sub-model is established for each of the sample subsets to obtain sub-model functions of the plurality of sample subsets. Piecewise approximate linearization is performed on the nonlinear lithium battery system by clustering analysis to obtain a multi-segment local linear sub-model, thereby avoiding the defect of low generalization performance of a single global model. The state data of a sample to be tested is respectively added into the state data of each of the sample subsets to calculate a change value of the state data of each of the sample subsets before and after the adding operation, and at least one sub-model close to the sample to be tested is selected as the selected sub-model according to the change value; and finally, a weight is assigned to the selected sub-model to calculate the SOC value of the sample to be tested. The relevant sub-models are selected from the perspective of probability density distribution of samples, the difference of probability density distribution among sample sets is taken into full consideration to accordingly assign weights to the relevant sub-models, and the final SOC estimation is output so that the SOC estimation result has higher accuracy and reliability.

One or more embodiments are illustrated by corresponding attached drawings, and this does not constitute limitation of the embodiments. Element labeled with the same reference numerals in the attached drawings represent similar elements, and unless otherwise stated, figures in the attached drawings do not constitute scale limitation.

In order to make objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail hereinafter with reference to attached drawings and embodiments. It shall be appreciated that, the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

It shall be noted that, all features in the embodiments of the present application may be combined with each other without conflict as long as all the combinations are within the scope claimed in the present application. In addition, although functional module division is made in the schematic diagrams of the device and logical sequences are shown in the flowchart diagrams, in some cases, the steps shown or described can be executed with module division or sequences different from those in the schematic diagrams of the device and the flowchart diagrams.

Unless otherwise defined, all technical and scientific terms used in this specification have the same meanings as commonly understood by those skilled in the art of the present application. The terms used in the specification of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The term "and/or" used in this specification comprises any and all combinations of one or more associated items listed.

Please refer to <FIG>, which is a flowchart diagram of a method for estimating SOC of a lithium battery provided according to an embodiment of the present application. As shown in <FIG>, steps of the method comprise:
S1: collecting state data and corresponding SOC values of lithium batteries under different working conditions and establishing a sample set, and performing clustering analysis on the sample set to obtain a plurality of sample subsets.

The sample subsets comprise (X<NUM>,Y<NUM>), (X<NUM>,Y<NUM>),. ,(XN,YN), wherein <NUM>≤j≤N, N represents the total number of sample subsets, X represents the state data of the sample subsets, and Y represents the SOC value of the sample subsets. (X<NUM>,Y<NUM>), (X<NUM>,Y<NUM>),. ,(XN,YN) are respectively sets of a plurality of samples, i.e., (X<NUM>,Y<NUM>)={ (x<NUM>,y<NUM>), (x<NUM>,y<NUM>),. , (x1a,y1a)}, (X<NUM>,Y<NUM>)= { (x<NUM>,y<NUM>), (x<NUM>,y<NUM>),. , (x2b,y2b)},. , (Xj,Yj) ={ (xj1,yj1), (xj2,yj2),. , (xjc,yjc)},. , (XN,YN)= { (xN1,yN1), (xN2,yN2),. , (xNn,yNn)}. X represents the state data of a certain sample, y represents the SOC value of a certain sample, A = a + b + c. + n, and A represents the total number of samples in the sample set.

Specifically, the state data comprises at least one of charging and discharging current, terminal voltage and temperature of the lithium battery, and the expression formula of state data x of a certain sample is x=[I,U,T], wherein I, U, and T are respectively sampling values of charging and discharging current, terminal voltage and temperature of the lithium battery.

The SOC value refers to the ratio of the remaining capacity of the lithium battery to the capacity of the lithium battery in a fully charged state, and the SOC value ranges from <NUM>% to <NUM>%. When the SOC value is equal to <NUM>%, it means that the lithium battery is fully discharged, and when the SOC value is <NUM>%, it means that the lithium battery is fully charged. By knowing the SOC value, the operation of the lithium battery can be controlled.

Under each working condition, each group of state data x corresponds to an SOC value y, the state data x is an independent variable, and the corresponding SOC value y is a dependent variable. The independent variable x is taken as an input model and the dependent variable y is taken as an output model, and the relationship between the independent variable x and the dependent variable y is calculated to acquire an SOC estimation model of the lithium battery.

The sample sets collected during the above steps are all used as training sets to obtain the SOC estimation model. In the specific application process, in order to test the accuracy of the established SOC estimation model, the sample sets under different working conditions may be divided into training sets and test sets. For example, <NUM>% of the sample sets are used as training sets Dtrain={Xtrain,Ytrain} and the other <NUM>% are used as test sets Dtest={Xtest,Ytest}.

Specifically, the clustering analysis is used for performing piecewise analysis on the nonlinear lithium battery system for approximate linearization.

Specifically, the clustering analysis may adopt any clustering analysis algorithm currently available. In one implementation, the K-means clustering algorithm is used to perform clustering analysis on the sample set, and the specific steps are as follows:.

S2: establishing a corresponding sub-model for each of the sample subsets to obtain sub-model functions of the plurality of sample subsets.

Linear regression operation is performed on the sample subsets (X<NUM>,Y<NUM>), (X<NUM>,Y<NUM>),. ,(XN,YN) after the clustering analysis in the step S1 to acquire a regression classification model for SOC of the lithium battery.

Preferably, partial least squares (PLS) regression method is used to establish a corresponding PLS sub-model for each sample subset so as to obtain PLS sub-model functions of the plurality of sample subsets. PLS is a kind of statistical method which mainly uses the characteristics of principal component analysis to respectively project predicted variables and observed variables into a new space so as to find one linear regression model.

The PLS sub-model is expressed as follows: <MAT>.

The score matrices are linked by linear regression: <MAT> wherein Bj and Ej are respectively the diagonal matrix and regression residual matrix of the jth PLS sub-model. The diagonal matrix refers to a matrix in which all elements other than the main diagonal are <NUM>.

Finally, the PLS sub-model functions of the plurality of sample subsets are expressed as follows: <MAT>.

Preferably, principal component regression (PCR) is used to establish a corresponding PCR sub-model for each sample subset so as to obtain PCR sub-model functions of the plurality of sample subsets. The PCR sub-model is expressed as follows: <MAT>.

Specifically, according to the jth sample subset (Xj,Yj), after the sample matrix Xj is standardized, the covariance matrix Σj thereof may be expressed as follows: <MAT> spectral decomposition is performed thereon:.

S3: adding the state data of a sample to be tested respectively into the state data of each of the sample subsets, calculating a change value of the state data of each of the sample subsets before and after the adding operation, and selecting at least one sub-model close to the sample to be tested as the selected sub-model according to the change value.

The step of calculating a change value of the state data of each of the sample subsets before and after the adding operation may be performed by KL divergence <MAT>, which measures the difference of probability density distribution before and after the change of state data.

Specifically, the data state xtest of the sample to be tested is acquired, and the data state xtest of the sample to be tested is added to the state data x<NUM>,. , xN of each of the sample subsets to obtain new state data (X<NUM>, xtest),. , (Xj, xtest),. , (XN, xtest), and a first divergence information value Kj (KL divergence) between Xj and (Xj, xtest) is calculated, wherein the formula of the first divergence information value Kj is as follows: <MAT> wherein Σ<NUM> and σ<NUM> are respectively the covariance matrix and mean of Xj, Σ<NUM> and σ<NUM> are respectively the covariance matrix and mean of (Xj, xtest), and trace is the matrix tracing operator.

Normalization processing is performed on the first divergence information value Kj to acquire a second divergence information value <MAT>, wherein the formula for normalization is as follows: <MAT> a larger <MAT> represents a higher similarity between xtest and Xj, i.e., xtest being closer to the working conditions of lithium batteries characterized by Xj. Therefore, Nc sub-models corresponding to the larger <MAT> are selected from the sample probability density distribution.

Specifically, the second divergence information value <MAT> is compared with a preset divergence information value ε, and the sub-model which corresponds to <MAT> not less than the preset divergence information value ε is taken as the selected sub-model close to the sample to be tested. The expression formula of a set of the selected sub-models is as follows: Qc = {q<NUM>,q<NUM>,···qNc}, Qc is determined by the following formula: <MAT>, wherein Nc is the total number of the selected sub-models.

S4: assigning a weight to the selected sub-model, and calculating the SOC value of the sample to be tested.

The weight of each selected sub-model is related to the second divergence information value <MAT>, and the weight of each selected sub-model in the selected sub-models is made to be , s=q<NUM>,q<NUM>,.

It is assumed that the probability of each sub-model being selected for integration is equal, then: <MAT>.

The output result of SOC integration estimation corresponding to the test sample xtest is obtained according to the weight assigned to each selected sub-model and in combination with the sub-model function thereof.

Preferably, when partial least squares (PLS) regression method is used to establish a corresponding PLS sub-model for each sample subset, the output result of SOC integration estimation corresponding to the test sample xtest is as follows: <MAT>.

When principal component regression (PCR) is used to establish a corresponding PCR sub-model for each sample subset, the output result of SOC integration estimation corresponding to the test sample xtest is as follows: <MAT>.

In some embodiments, the method for estimating SOC of the lithium battery further comprises verifying the SOC estimation model of the lithium battery after acquiring the SOC estimation model. After acquiring the SOC estimation model of the lithium battery, the SOC value obtained by the SOC estimation model of the lithium battery may be verified by root mean square error and average relative error, so as to determine whether the SOC value obtained by the SOC estimation model of the lithium battery is accurate or not.

Specifically, the formula of the error term is: <MAT> <MAT> wherein I is the number of test samples, ytest is the true value of SOC, and ŷtest is the estimated value of SOC.

The verification results of the SOC estimation model of the lithium battery are shown in the following table.

Please refer to <FIG>, which is a diagram illustrating an estimation result of a method for estimating SOC of a lithium battery provided according to an embodiment of the present application. The straight line represents the true value of SOC of the lithium battery, and the dotted line represents the estimated value obtained according to the SOC estimation model of the lithium battery. As shown in <FIG>, the true value of SOC of the lithium battery and the estimated value of SOC of the lithium battery are approximately on the same straight line.

In actual measurement, at least one of the terminal voltage, charging and discharging current and temperature of the lithium battery is acquired in real time, and input into the SOC estimation model of the lithium battery, so as to acquire the corresponding SOC values of the lithium battery corresponding to the terminal voltage, charging and discharging current and temperature.

Different from the situation of related technologies, the embodiments of the present application provide a method for estimating SOC of a lithium battery, in the method for estimating SOC of the lithium battery, state data and corresponding SOC values of lithium batteries under different working conditions are collected to establish a sample set, and clustering analysis is performed on the sample set to obtain a plurality of sample subsets; then a corresponding sub-model is established for each of the sample subsets to obtain sub-model functions of the plurality of sample subsets; next, the state data of a sample to be tested is respectively added into the state data of each of the sample subsets to calculate a change value of the state data of each of the sample subsets before and after the adding operation, and at least one sub-model close to the sample to be tested is selected as the selected sub-model according to the change value; and finally, a weight is assigned to the selected sub-model to calculate the SOC value of the sample to be tested. By obtaining the estimated SOC value of the lithium battery in the aforementioned manner, the accuracy and reliability of the estimated SOC value of the lithium battery are improved.

Please refer to <FIG>, which is a structural block diagram of a device for estimating SOC of a lithium battery provided according to an embodiment of the present application. As shown in <FIG>, the device <NUM> for estimating SOC of the lithium battery comprises an acquisition module <NUM>, a model establishing module <NUM>, a selection module <NUM> and an SOC calculating module <NUM>.

The acquisition module <NUM> is configured to collect state data and corresponding SOC values of lithium batteries under different working conditions and establish a sample set, and perform clustering analysis on the sample set to obtain a plurality of sample subsets.

The model establishing module <NUM> is configured to establish a corresponding sub-model for each of the sample subsets to obtain sub-model functions of the plurality of sample subsets.

The selection module <NUM> is configured to add the state data of a sample to be tested respectively into the state data of each of the sample subsets, calculate a change value of the state data of each of the sample subsets before and after the adding operation, and select at least one sub-model close to the sample to be tested as the selected sub-model according to the change value.

The SOC calculating module <NUM> is configured to assign a weight to the selected sub-model, and calculate the SOC value of the sample to be tested.

It shall be noted that the device for estimating SOC of the lithium battery described above can execute the method for estimating SOC of the lithium battery provided according to the embodiment of the present application, and has corresponding functional modules and beneficial effects for executing the method. For the technical details not described in detail in the embodiment of the device for estimating SOC of the lithium battery, please refer to the method for estimating SOC of the lithium battery provided according to the embodiment of the present application.

The embodiments of the device described above are only for illustrative purpose. The units illustrated as separate components may be or may not be physically separated, and components displayed as units may be or may not be physical units. That is, these units and components may be located in one place or distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiments.

Referring to <FIG>, an embodiment of the present application provides an electronic equipment <NUM>, which comprises: at least one processor <NUM>, one processor <NUM> being taken as an example in <FIG>; and a memory <NUM> communicatively connected to the at least one processor <NUM>, connection through a bus being taken as an example in <FIG>.

The memory <NUM> stores instructions that can be executed by the at least one processor <NUM>, and the instructions are executed by the at least one processor <NUM> to enable the at least one processor <NUM> to execute the method for estimating SOC of the lithium battery described above.

As a nonvolatile computer readable storage medium, the memory <NUM> may be used to store nonvolatile software programs, nonvolatile computer executable programs and modules, such as program instructions/modules corresponding to the method for estimating SOC of the lithium battery in the embodiments of the present application. The processor <NUM> runs the nonvolatile software programs, instructions and modules stored in the memory <NUM>, thereby executing various functional applications and data processing of the electronic equipment <NUM>, i.e., implementing the method for estimating SOC of the lithium battery provided by the embodiments of the method described above.

The memory <NUM> may comprise a program storage area and a data storage area, wherein the program storage area may store operating systems and application programs required by at least one function. In addition, the memory <NUM> may comprise a high-speed random access memory, and may also comprise a nonvolatile memory. For example, the memory <NUM> comprises at least one magnetic disk memory device, flash memory device, or other nonvolatile solid-state memory device. In some embodiments, the memory <NUM> optionally comprises memories remotely provided relative to the processor <NUM>.

The one or more modules are stored in the memory <NUM>, and when executed by the one or more processors <NUM>, the one or more modules execute the method for estimating SOC of the lithium battery in any of the embodiments of the method described above, e.g., execute the steps of the method of <FIG> described above.

The electronic equipment described above may execute the method provided according to the embodiments of the present application, and have corresponding functional modules for executing the method. For technical details not described in detail in this embodiment, please refer to the method provided according to the embodiments of the present application.

The electronic equipment of the embodiment of the present application exists in various forms, including but not limited to:.

An embodiment of the present application further provides a computer readable storage medium, in which computer executable instructions are stored. The computer executable instructions are executed by one or more processors to for example execute the steps of the method of <FIG> described above and implement the functions of the modules in <FIG>.

An embodiment of the present invention provides a computer program product, which comprises a computer program stored on a nonvolatile computer readable storage medium. The computer program comprises program instructions which, when executed by the electronic equipment, enable the electronic equipment to execute the method for estimating SOC of the lithium battery in any of the embodiments of the method described above, e.g., execute the steps S1 to S4 of the method of <FIG> described above and implement the function of modules <NUM> to <NUM> in <FIG>.

From the description of the above embodiments, those of ordinary skill in the art may clearly appreciate that each embodiment may be realized by means of software plus a general hardware platform, and of course, it may also be realized by hardware. As shall be appreciated by those of ordinary skill in the art, the implementation of all or part of the processes in the embodiments of the method described above may be completed by instructing related hardware through a computer program, and the program may be stored in a computer readable storage medium. When it is executed, the program may comprise the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM) or a Random Access Memory (RAM) or the like.

Claim 1:
A method for estimating SOC of a lithium battery, comprising steps of:
collecting state data and corresponding SOC values of lithium batteries under different working conditions and establishing a sample set, and performing clustering analysis on the sample set to obtain a plurality of sample subsets;
establishing a corresponding sub-model for each of the sample subsets by performing linear regression operation to obtain sub-model functions of the plurality of sample subsets;
characterized by
adding the state data of a sample to be tested respectively into the state data of each of the sample subsets, calculating a change value of the state data of each of the sample subsets before and after the adding operation, and selecting at least one sub-model close to the sample to be tested as the selected sub-model according to the change value;
assigning a weight to the selected sub-model, and calculating the SOC value of the sample to be tested;
the step of assigning a weight to the selected sub-model comprises:
making the weight of each selected sub-model in the selected sub-models be P(Xs|xtest), , s=q1, q2, ..., qNc;
the expression formula of the weight is as follows: <MAT>
wherein <MAT> <MAT>
wherein P(Xs) is the prior probability that Xs can describe the current working condition of the lithium battery, P(xtest | Xs) represents the probability that xtest may be generated by Xs;
the step of calculating the SOC value of the sample to be tested is to calculate the SOC value through the following formula: <MAT>
wherein ytest is the estimated value of SOC, xtest is the state data of a sample to be tested, q<NUM> is the selected <NUM>st sub-model, qNc is the selected Ncst P(Xs|xtest) sub-model, s is the selected sst sub-model, is the weight of the selected sst sub-model, fs(xtest) is the sub-model function of the selected sst sub-model, <MAT> is the divergence information value.