CN117647747A - Energy storage battery health state evaluation method, system, device and medium - Google Patents
Energy storage battery health state evaluation method, system, device and medium Download PDFInfo
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Abstract
The invention discloses a method, a system, a device and a medium for evaluating the health state of an energy storage battery, which comprise the following steps: acquiring an actual measurement data set of the energy storage battery based on the calibration capacity and the alternating current impedance spectrum of the energy storage battery; respectively taking the health state data and the alternating current impedance data in the measured data set as data sources, and adopting LSTM to predict the data of the energy storage battery in the stage of not reaching the service life so as to obtain a predicted data set required by constructing an energy storage battery health state evaluation model; taking the actual measurement data set and the prediction data set as modeling data, and dividing the modeling data into a training set and a verification set; based on the training set and the verification set, a BP neural network algorithm is utilized to construct an energy storage battery health state evaluation model. The method and the device can make up the defect of insufficient training data caused by short cycle time of the energy storage battery, accurately evaluate the health state of the energy storage battery, prolong the service life of the energy storage battery to the greatest extent and reduce the safety risk.
Description
Technical Field
The invention belongs to the technical field of energy storage battery health status, and relates to an energy storage battery health status evaluation method, an energy storage battery health status evaluation system, an energy storage battery health status evaluation device and an energy storage battery health status evaluation medium.
Background
Lithium ion batteries are used as the representative of novel energy storage, and take absolute advantage in novel energy storage capacity, but the evaluation of the running state and safety of the lithium ion batteries is always valued in all the circles due to the complex structure and electrochemical mechanism of the lithium ion batteries. At present, common methods for evaluating the health state of the energy storage battery include a definition method, a model method, a data driving method and the like, but the methods cannot be applied practically because of the fact that the methods cannot be used for both accuracy and practical application convenience. BP (Back Propagation) the neural network is a multi-layer feed-forward network consisting of an input layer, an hidden layer and an output layer. The BP neural network solves the problem of multi-parameter nonlinear mapping. The algorithm performs model training through existing data, and finally builds a related model. However, in the practical application process, the data of the early training often cannot completely cover the full life cycle of the energy storage battery, so that the life stage model of the battery to be evaluated is not trained, and finally the evaluation result is inaccurate.
Disclosure of Invention
The invention aims to solve the problem that the method for evaluating the state of health of the energy storage battery in the prior art cannot achieve both accuracy and practical application convenience, so that the method is difficult to be practically applied. Under the condition that the data trained in the early stage cannot completely cover the full life cycle of the energy storage battery, the BP neural network is not trained in a life stage model of the battery to be evaluated, and finally the problem of inaccurate evaluation results is caused, so that a Long Short-Term Memory (LSTM) is introduced, the LSTM is a time recursive neural network which is suitable for processing and predicting important events with relatively Long intervals and delays in a time sequence, and therefore the method, the system, the device and the medium for evaluating the health state of the energy storage battery are provided, and can be used for predicting the data of the energy storage battery which does not reach the life stage by utilizing the LSTM, and the health state evaluation model of the whole life of the energy storage battery is built.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
an energy storage battery state of health evaluation method, comprising:
testing the calibration capacity and alternating current impedance spectrum of the energy storage battery;
acquiring an actual measurement data set of the energy storage battery based on the calibration capacity and the alternating current impedance spectrum of the energy storage battery;
respectively taking the health state data and the alternating current impedance data in the measured data set as data sources, and adopting LSTM to predict the data of the energy storage battery in the stage of not reaching the service life so as to obtain a predicted data set required by constructing an energy storage battery health state evaluation model;
taking the actual measurement data set and the prediction data set as modeling data, and dividing the modeling data into a training set and a verification set;
based on the training set and the verification set, a BP neural network algorithm is utilized to construct an energy storage battery health state evaluation model.
The invention further improves that:
further, the calibration capacity and the alternating current impedance spectrum of the energy storage battery to be tested are tested, specifically: the energy storage battery performs charge and discharge circulation according to analog peak regulation, frequency modulation and new energy consumption, capacity calibration is performed once in each circulation period, and a group of alternating current impedance spectrums are tested.
Further, capacity calibration is performed once in a cycle, specifically: carrying out 3 times of constant power charge and discharge tests on the energy storage battery according to the current constant current of 1/4-1/2C or the power of 1/4-1/2P, and recording the last discharge capacity as a calibration capacity as a basis for calculating the health state; the energy of 100 cycles of equivalent full charge and full discharge is recorded as 1 period.
Further, a set of ac impedance spectra were tested, specifically: the alternating current impedance spectrum is measured every 10 percent of SOC, and the exciting current is 0.15 to 0.20C of the energy storage battery.
Further, the measured data set of the energy storage battery is as followsWherein SOH n Is the health state of the nth cycle, n is the cycle period, EIS socn Is the total data of the impedance at all SOCs for a certain cycle period.
Further, the LSTM is adopted to predict the data of the energy storage battery in the stage of not reaching the life by using the health state data source in the actual measurement data set, and specifically:
and carrying out normalization processing on all the health state data sets of the actual measurement n periods.
Wherein X' is normalized data, X is original data, X min Is the minimum value of the original data, X max Is the maximum value of the original data;
taking all health state test data as training data, taking the health state data of every n periods as input parameters, taking the health state data of the n+1th period as output parameters, modeling by using LSTM, and predicting the health state of the n+1th period; and then taking the data of the n+1 period as training data, and repeating the training mode until the health state is predicted to be the health state corresponding to all the periods before 60%.
Further, the data of the energy storage battery in the stage of not reaching the service life is predicted by adopting the LSTM by taking the alternating current impedance data as a data source, and the method specifically comprises the following steps: normalizing the data in the data set of all actually measured n periods, taking the test data after normalization as training data, taking the alternating current impedance data of each n period as an input parameter, taking the alternating current impedance data of the n+1th period as an output parameter, modeling by using LSTM, and predicting the alternating current impedance data of the n+1th period; and repeating the training mode by taking the data in the period of n+1 as training data until the health state is predicted to reach the alternating current impedance data in the period of 60 percent.
An energy storage battery state of health evaluation system, comprising:
the test module is used for testing the calibration capacity and the alternating current impedance spectrum of the energy storage battery;
the system comprises an actual measurement data set acquisition module, a control module and a control module, wherein the actual measurement data set acquisition module acquires an actual measurement data set of the energy storage battery based on the calibration capacity and the alternating current impedance spectrum of the energy storage battery;
the prediction data acquisition module respectively takes the health state data and the alternating current impedance data in the measured data set as data sources, and adopts LSTM to predict the data of the energy storage battery in the stage of not reaching the service life so as to obtain a prediction data set required for constructing the energy storage battery health state evaluation model;
a dividing module which takes the measured data set and the predicted data set as modeling data and divides the modeling data into a training set and a verification set,
the model construction module is used for constructing an energy storage battery health state evaluation model by utilizing a BP neural network algorithm based on the training set and the verification set.
A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method as described above when the computer program is executed.
A computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of a method as described above.
Compared with the prior art, the invention has the following beneficial effects:
the method comprises the steps of obtaining an actual measurement data set of an energy storage battery based on the calibration capacity of the energy storage battery and an alternating current impedance spectrum; respectively taking the health state data and the alternating current impedance data in the measured data set as data sources, and adopting LSTM to predict the data of the energy storage battery in the stage of not reaching the service life so as to obtain a predicted data set required by constructing an energy storage battery health state evaluation model; taking the actual measurement data set and the prediction data set as modeling data, and dividing the modeling data into a training set and a verification set; based on the training set and the verification set, a BP neural network algorithm is utilized to construct an energy storage battery health state evaluation model. The method and the device can make up the defect of insufficient training data caused by short cycle time of the energy storage battery, accurately evaluate the health state of the energy storage battery, prolong the service life of the energy storage battery to the greatest extent and reduce the safety risk.
Drawings
For a clearer description of the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an energy storage battery state of health evaluation method of the present invention;
fig. 2 is a schematic structural diagram of the energy storage battery state of health evaluation system of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the description of the embodiments of the present invention, it should be noted that, if the terms "upper," "lower," "horizontal," "inner," and the like indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, or the azimuth or the positional relationship in which the inventive product is conventionally put in use, it is merely for convenience of describing the present invention and simplifying the description, and does not indicate or imply that the apparatus or element to be referred to must have a specific azimuth, be configured and operated in a specific azimuth, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
Furthermore, the term "horizontal" if present does not mean that the component is required to be absolutely horizontal, but may be slightly inclined. As "horizontal" merely means that its direction is more horizontal than "vertical", and does not mean that the structure must be perfectly horizontal, but may be slightly inclined.
In the description of the embodiments of the present invention, it should also be noted that, unless explicitly specified and limited otherwise, the terms "disposed," "mounted," "connected," and "connected" should be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
The invention is described in further detail below with reference to the attached drawing figures:
referring to fig. 1, the invention discloses a method for evaluating the health status of an energy storage battery, which comprises the following steps:
s101, testing the calibration capacity and alternating current impedance spectrum of the energy storage battery.
The energy storage battery performs charge and discharge circulation according to analog peak regulation, frequency modulation and new energy consumption, capacity calibration is performed once in each circulation period, and the energy storage battery tests a group of alternating current impedance spectrums before the charge and discharge circulation.
The capacity calibration is carried out once in each cycle, specifically: 3 times of charge and discharge tests are carried out on the energy storage battery according to the current constant current of 1/4-1/2C or the constant power of 1/4-1/2P, and the last discharge capacity is recorded as the calibration capacity and used as the basis for calculating the health state; the energy of 100 cycles of equivalent full charge and full discharge is recorded as 1 period.
Testing a group of alternating current impedance spectrums, specifically: the alternating current impedance spectrum is measured every 10 percent of SOC, and the exciting current is 0.15 to 0.20C of the energy storage battery.
S102, acquiring an actual measurement data set of the energy storage battery based on the calibration capacity of the energy storage battery and the alternating current impedance spectrum.
The actual measurement data set of the energy storage battery is as followsWherein SOH n Is the health state of the nth cycle, n is the cycle period, EIS socn For all data of impedance at all SOCs under a certain cycle period, such as real part, imaginary part, phase angle, etc., the impedance data at a certain SOC can be expressed as +.>Wherein Z 'is' fm Is the real part of frequency fm, -Z' fm Is the imaginary negative number of fm, |Z| fm Is the modulus of the frequency fm, +.>Is the phase angle at frequency fm.
S103, respectively taking the health state data and the alternating current impedance data in the measured data set as data sources, and adopting LSTM to predict the data of the energy storage battery in the stage of not reaching the service life so as to obtain a predicted data set required for constructing an energy storage battery health state evaluation model;
predicting the data of the energy storage battery in the stage of not reaching the service life by adopting an LSTM (least squares) by using a health state data source in an actual measurement data set, wherein the data comprises the following specific steps:
and carrying out normalization processing on all the health state data sets of the actual measurement n periods.
Wherein X' is normalized data, X is original data, X min Is the minimum value of the original data, X max Is the maximum value of the original data;
taking all health state test data as training data, taking the health state data of every n periods as input parameters, taking the health state data of the n+1th period as output parameters, modeling by using LSTM, and predicting the health state of the n+1th period; and then taking the data of the n+1 period as training data, and repeating the training mode until the health state is predicted to be the health state corresponding to all the periods before 60%.
The method comprises the steps of using alternating current impedance data as a data source, and predicting data of an energy storage battery in a stage of not reaching the service life by adopting LSTM, wherein the data comprises the following specific steps: normalizing the data in the data set of all actually measured n periods, taking the test data after normalization as training data, taking the alternating current impedance data of each n period as an input parameter, taking the alternating current impedance data of the n+1th period as an output parameter, modeling by using LSTM, and predicting the alternating current impedance data of the n+1th period; and repeating the training mode by taking the data in the period of n+1 as training data until the health state is predicted to reach the alternating current impedance data in the period of 60 percent.
S104, taking the actual measurement data set and the prediction data set as modeling data and dividing the modeling data into a training set and a verification set,
s105, based on the training set and the verification set, constructing an energy storage battery health state evaluation model by utilizing a BP neural network algorithm.
The alternating current impedance data is input into the BP neural network, the training set is used for modeling, the verification set is used for verifying the quality of the model, the training set and the verification set comprise the impedance data serving as characteristic parameters and the health state serving as label output, and the trained and verified BP neural network forms an energy storage battery health state evaluation model
The BP neural network is composed of an input layer, an implicit layer and an output layer, wherein the input layer is generally an extracted characteristic parameter, the input layer is in a vector form, different layers can be set in the implicit layer, different node numbers can be set in each layer, and the output layer is generally a parameter to be evaluated, and is in a healthy state in the scheme. The layers are connected by an activation function, typically sigmod, etc
Referring to fig. 2, the invention discloses an energy storage battery health status evaluation system, comprising:
the test module is used for testing the calibration capacity and the alternating current impedance spectrum of the energy storage battery;
the system comprises an actual measurement data set acquisition module, a control module and a control module, wherein the actual measurement data set acquisition module acquires an actual measurement data set of the energy storage battery based on the calibration capacity and the alternating current impedance spectrum of the energy storage battery;
the prediction data acquisition module respectively takes the health state data and the alternating current impedance data in the measured data set as data sources, and adopts LSTM to predict the data of the energy storage battery in the stage of not reaching the service life so as to obtain a prediction data set required for constructing the energy storage battery health state evaluation model;
a dividing module which takes the measured data set and the predicted data set as modeling data and divides the modeling data into a training set and a verification set,
the model construction module is used for constructing an energy storage battery health state evaluation model by utilizing a BP neural network algorithm based on the training set and the verification set.
The embodiment of the invention provides terminal equipment. The terminal device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. The steps of the various method embodiments described above are implemented when the processor executes the computer program. Alternatively, the processor may implement the functions of the modules/units in the above-described device embodiments when executing the computer program.
The computer program may be divided into one or more modules/units, which are stored in the memory and executed by the processor to accomplish the present invention.
The terminal equipment can be computing equipment such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The terminal device may include, but is not limited to, a processor, a memory.
The processor may be a central processing unit (CentralProcessingUnit, CPU), but may also be other general purpose processors, digital signal processors (DigitalSignalProcessor, DSP), application specific integrated circuits (ApplicationSpecificIntegratedCircuit, ASIC), off-the-shelf programmable gate arrays (Field-ProgrammableGateArray, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like.
The memory may be used to store the computer program and/or module, and the processor may implement various functions of the terminal device by running or executing the computer program and/or module stored in the memory and invoking data stored in the memory.
The modules/units integrated in the terminal device may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a Read-only memory (ROM), a random access memory (RAM, randomAccessMemory), an electrical carrier signal, a telecommunication signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An energy storage battery state of health evaluation method, characterized by comprising:
testing the calibration capacity and alternating current impedance spectrum of the energy storage battery;
acquiring an actual measurement data set of the energy storage battery based on the calibration capacity and the alternating current impedance spectrum of the energy storage battery;
respectively taking the health state data and the alternating current impedance data in the measured data set as data sources, and adopting LSTM to predict the data of the energy storage battery in the stage of not reaching the service life so as to obtain a predicted data set required by constructing an energy storage battery health state evaluation model;
taking the actual measurement data set and the prediction data set as modeling data, and dividing the modeling data into a training set and a verification set;
based on the training set and the verification set, a BP neural network algorithm is utilized to construct an energy storage battery health state evaluation model.
2. The method for evaluating the health status of an energy storage battery according to claim 1, wherein the testing of the calibration capacity and the ac impedance spectrum of the energy storage battery to be tested specifically comprises: the energy storage battery performs charge and discharge circulation according to analog peak regulation, frequency modulation and new energy consumption, capacity calibration is performed once in each circulation period, and a group of alternating current impedance spectrums are tested.
3. The method for evaluating the health status of an energy storage battery according to claim 2, wherein the capacity calibration is performed once per cycle, specifically: carrying out 3 times of constant power charge and discharge tests on the energy storage battery according to the current constant current of 1/4-1/2C or the power of 1/4-1/2P, and recording the last discharge capacity as a calibration capacity as a basis for calculating the health state; the energy of 100 cycles of equivalent full charge and full discharge is recorded as 1 period.
4. The method for evaluating the health status of an energy storage battery according to claim 3, wherein the testing a set of ac impedance spectrums is specifically: the alternating current impedance spectrum is measured every 10 percent of SOC, and the exciting current is 0.15 to 0.20C of the energy storage battery.
5. The method of claim 4, wherein the measured data set of the energy storage battery isWherein SOH n Is the health state of the nth cycle, n is the cycle period, EIS socn Is the total data of the impedance at all SOCs for a certain cycle period.
6. The method for evaluating the state of health of an energy storage battery according to claim 5, wherein the predicting the data of the energy storage battery in the less-than-life stage by using the LSTM from the data source of the state of health data in the measured data set is specifically:
normalizing all the health state data sets of the actual measurement n periods;
wherein X' is normalized data, X is original data, X min Is the minimum value of the original data, X max Is the maximum value of the original data;
taking all health state test data as training data, taking the health state data of every n periods as input parameters, taking the health state data of the n+1th period as output parameters, modeling by using LSTM, and predicting the health state of the n+1th period; and then taking the data of the n+1 period as training data, and repeating the training mode until the health state is predicted to be the health state corresponding to all the periods before 60%.
7. The method for evaluating the state of health of an energy storage battery according to claim 6, wherein the predicting the data of the energy storage battery in the non-life stage by using the LSTM with the ac impedance data as a data source comprises: normalizing the data in the data set of all actually measured n periods, taking the test data after normalization as training data, taking the alternating current impedance data of each n period as an input parameter, taking the alternating current impedance data of the n+1th period as an output parameter, modeling by using LSTM, and predicting the alternating current impedance data of the n+1th period; and repeating the training mode by taking the data in the period of n+1 as training data until the health state is predicted to reach the alternating current impedance data in the period of 60 percent.
8. An energy storage battery state of health evaluation system, comprising:
the test module is used for testing the calibration capacity and the alternating current impedance spectrum of the energy storage battery;
the system comprises an actual measurement data set acquisition module, a control module and a control module, wherein the actual measurement data set acquisition module acquires an actual measurement data set of the energy storage battery based on the calibration capacity and the alternating current impedance spectrum of the energy storage battery;
the prediction data acquisition module respectively takes the health state data and the alternating current impedance data in the measured data set as data sources, and adopts LSTM to predict the data of the energy storage battery in the stage of not reaching the service life so as to obtain a prediction data set required for constructing the energy storage battery health state evaluation model;
a dividing module which takes the measured data set and the predicted data set as modeling data and divides the modeling data into a training set and a verification set,
the model construction module is used for constructing an energy storage battery health state evaluation model by utilizing a BP neural network algorithm based on the training set and the verification set.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1-7 when the computer program is executed.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any one of claims 1-7.
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