CN116184244A - Energy storage battery health state online estimation method, device, equipment and medium - Google Patents
Energy storage battery health state online estimation method, device, equipment and medium Download PDFInfo
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- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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- G—PHYSICS
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- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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Abstract
The invention belongs to the technical field of electrical engineering, and discloses an energy storage battery health state online estimation method, device, equipment and medium; the method comprises the following steps: acquiring current middle section capacity Q of energy storage battery now The method comprises the steps of carrying out a first treatment on the surface of the Acquiring operation data in a period of time under the current state of the energy storage battery to be estimated, inputting the operation data into a pre-trained initial middle section capacity prediction model to obtain the initial middle section capacity Q of the energy storage battery start The method comprises the steps of carrying out a first treatment on the surface of the According to the current middle section capacity Q of the energy storage battery now And the initial middle section capacity Q of the energy storage battery start And calculating the SOH of the energy storage battery in the current state. The invention is based on the increment capacity analysis ICA and combines the artificial intelligence technology, thereby the energy storage batteryStarting from the electrochemical nature, the method is more reliable and has higher accuracy.
Description
Technical Field
The invention belongs to the technical field of electrical engineering, and particularly relates to an energy storage battery health state online estimation method, device, equipment and medium.
Background
With the development of energy storage battery technology, the energy storage battery is gradually introduced into electric automobiles, portable electronic equipment and various devices with energy storage requirements as a common energy storage element so as to realize the storage and conversion of electric energy. For energy storage batteries, state of health (SOH) is the most critical state quantity. SOH is not uniformly defined, is generally defined by the change of the capacity and the internal resistance of the energy storage battery, can quantitatively describe the aging condition of the energy storage battery, directly influences the performance of the energy storage battery by accurate SOH estimation, provides an important reference basis for replacement of the aged energy storage battery, and has great significance for the performance and safe operation of the optimal performance of the energy storage battery.
Chinese patent application publication No. CN115219937A discloses a method for estimating the health state of energy storage batteries of different aging paths based on deep learning, comprising the following steps: acquiring battery charging voltage data of the energy storage battery under different working conditions, inputting the battery charging voltage data into a battery health estimation model, and outputting a battery health state estimation value under corresponding working conditions; constructing a battery health estimation model based on a deep neural network and performing two times of training; training for the first time: training a battery health estimation model according to battery aging experimental data under a certain working condition; training for the second time: freezing the nuclear network layer parameters of the pre-training estimation model and calling the model parameters thereof; and then training an optimized estimation model through battery aging experimental data under different working conditions to adjust parameters of a fully connected output layer of the optimized estimation model, so as to obtain a trained battery health estimation model. The method and the device can be suitable for battery health state estimation under various working conditions without large-scale training data, so that the training difficulty and cost of an estimation model can be reduced, and the application range of the estimation model can be improved.
The data of the training model used by the method is experimental data, but not real operation data, and cannot reflect the real operation condition of the real battery, so that the trained prediction model is applied to the real scene, and the prediction accuracy cannot be ensured; the characteristic variables used for training the model are only one voltage, and the input variables are too simple; the algorithm used belongs to the field of deep learning, and has higher requirement on hardware configuration; the deep learning algorithm belongs to an artificial intelligence technology, the method is too single, and the electrochemical correlation principle is not involved, so that the method is not persuasive.
Disclosure of Invention
The invention aims to provide an on-line estimation method, device, equipment and medium for the state of health of an energy storage battery, which are used for solving the technical problems that the prediction accuracy cannot be ensured due to single input variable and single method in the prior art.
Compared with the prior art, the invention has the following beneficial effects:
in a first aspect, the present invention provides an online estimation method for a state of health of an energy storage battery, including:
acquiring current middle section capacity Q of energy storage battery now ；
Acquiring operation data in a period of time under the current state of the energy storage battery to be estimated, inputting the operation data into a pre-trained initial middle section capacity prediction model to obtain the initial middle section capacity Q of the energy storage battery start ；
According to the current middle section capacity Q of the energy storage battery now And the initial middle section capacity Q of the energy storage battery start Calculating SOH of the energy storage battery in the current state:
the invention is further improved in that: the operational data includes voltage, current, and temperature.
The invention is further improved in that: current middle section capacity Q of energy storage battery now Obtained by the following steps:
obtaining an IC curve of the energy storage battery, and calculating the middle-section capacity Q of the energy storage battery according to the IC curve now ；
Q (u) represents the capacity of the battery when the voltage of the battery is u; ICA (u) represents an ICA value when the voltage of the battery is u; u1 is the voltage value corresponding to the second peak of the IC curve, and u2 is selected as the minimum cut-off voltage that can be reached by all the battery cells.
The invention is further improved in that: the IC curve is obtained by the following steps:
acquiring real operation data of an energy storage battery; the real operation data of the energy storage battery comprise voltage, current and temperature;
calculating the charge capacity of the energy storage battery by an ampere-hour integration method based on the real operation data of the energy storage battery, and drawing a first curve graph with the charge capacity as an abscissa and the battery voltage as an ordinate, wherein the first curve graph is provided with a first curve;
exchanging the abscissa of the first curve graph to obtain a second curve graph with a second curve;
and deriving the second curve based on the voltage to obtain an IC curve with an ICA value on the ordinate and a battery voltage on the abscissa.
The invention is further improved in that: the pre-trained initial middle section capacity prediction model is obtained through the following steps:
establishing a regression model reflecting the relation between the initial middle section capacity of the battery and the battery operation parameters by adopting an artificial intelligent algorithm;
collecting real operation data of the battery in an initial operation state, and arranging the real operation data into an input data set and an output data set; input characteristics include voltage, current, temperature of the battery cell; the initial middle section capacity Q is obtained through integration start An output part for training data set is arranged as a prediction target value of the model;
model training is carried out on the regression model based on the tidied input and output data sets; obtaining the initial middle section capacity Q which can be predicted start Is a model of the initial mid-section capacity prediction.
The invention is further improved in that: the artificial intelligence algorithm is an artificial intelligence algorithm with a regression prediction function.
The invention is further improved in that: the artificial intelligence algorithm is XGboost.
In a second aspect, the present invention provides an energy storage battery state of health online estimation device, including:
an acquisition module for acquiring the current middle section capacity Q of the energy storage battery now ；
The prediction module is used for acquiring the operation data in a period of time under the current state of the energy storage battery to be estimated, inputting the operation data into a pre-trained initial middle section capacity prediction model to obtain the initial middle section capacity Q of the energy storage battery start ；
An estimation module for estimating the current middle section capacity Q of the energy storage battery now And the initial middle section capacity Q of the energy storage battery start Calculating SOH of the energy storage battery in the current state:
in a third aspect, the present invention provides an electronic device, including a processor and a memory, where the processor is configured to execute a computer program stored in the memory to implement the method for online estimating a state of health of an energy storage battery.
In a fourth aspect, the present invention provides a computer readable storage medium storing at least one instruction that when executed by a processor implements the method for online estimation of state of health of an energy storage battery.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides an energy storage battery health state online estimation method, a device, equipment and a medium, wherein the method comprises the following steps: acquiring current middle section capacity Q of energy storage battery now The method comprises the steps of carrying out a first treatment on the surface of the Acquiring operation data in a period of time under the current state of the energy storage battery to be estimated, inputting the operation data into a pre-trained initial middle section capacity prediction model to obtain the initial middle section capacity Q of the energy storage battery start The method comprises the steps of carrying out a first treatment on the surface of the According to the current middle section capacity Q of the energy storage battery now And the initial middle section capacity Q of the energy storage battery start Calculating the energy storage under the current stateSOH of the battery. The method comprehensively considers battery operation data comprising voltage, current and temperature as input, and compared with the prior art, the input variables are more stable; input data are input into a pre-trained initial middle section capacity prediction model to obtain initial middle section capacity Q of the energy storage battery start The method comprises the steps of carrying out a first treatment on the surface of the According to the current middle section capacity Q of the energy storage battery now And the initial middle section capacity Q of the energy storage battery start Calculating SOH of the energy storage battery in the current state; the method is based on Incremental Capacity Analysis (ICA) and combines artificial intelligence technology, and starts from electrochemical essential characteristics of the energy storage battery, and has higher reliability and higher accuracy.
The method can be widely applied to actual application scenes such as peak shaving, frequency modulation and the like. The model is trained by adopting the real operation data, is more suitable for the real application scene, and has higher prediction precision compared with the model trained by adopting the experimental data. The method has robustness to the depth of discharge (DoD), charging current and temperature variation, and can meet the requirements of an energy storage battery which runs truly.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of an IC curve;
FIG. 2 is a schematic illustration of ICA mid-section capacity calculation;
FIG. 3 is a flow chart of an online estimation method for the state of health of an energy storage battery according to the present invention;
FIG. 4 is a block diagram of an on-line estimation device for the state of health of an energy storage battery according to the present invention;
fig. 5 is a block diagram of an electronic device according to the present invention.
Detailed Description
The invention will be described in detail below with reference to the drawings in connection with embodiments. It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
The following detailed description is exemplary and is intended to provide further details of the invention. Unless defined otherwise, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the invention.
The energy storage battery is gradually aged through a plurality of charge and discharge cycles when in use, and is monitored by using an energy storage Battery Management System (BMS). The invention discloses an online estimation method for the state of health of an energy storage battery based on Incremental Capacity Analysis (ICA) and combined with artificial intelligence technology.
The sensors needed to determine SoH should be as simple as possible to ensure that the cost of the BMS remains high. The present invention uses only the measured values of voltage, current, temperature, etc. existing in the BMS. The invention utilizes the battery response characteristic of the energy storage battery under the actual working condition, and the classical method generally adopts the charging state as the basis of analysis, because the state is better controlled than the discharging state. It is therefore necessary to obtain as much information as possible from the current, voltage and temperature measurements during the battery charging phase.
Incremental capacity analysis is an electrochemical technique that can obtain information on the internal state of a battery by measuring only the voltage and current of the battery. Incremental Capacity (IC) represents the change in capacity associated with a voltage step, and the IC curve equation is expressed as:
q represents a charging capacity, U Cell Each peak of the incremental capacity curve, representing the cell voltage, has a unique shape, intensity and location, indicating the process in which the electrochemical feature occurs within the cell. Because the battery voltage only varies in the range of 150mV when the state of charge varies from 10% to 90%. Thus, for this battery technology, direct voltage analysis to determine any state of the battery is inaccurate, so the use of Incremental Capacity Analysis (ICA) is nowThe wire capacity estimation method can obtain better effects.
Example 1
The invention provides an energy storage battery health state online estimation method, which is based on Incremental capacity analysis (increment CapacityAnalysis, ICA) and innovatively merges an artificial intelligent algorithm to carry out online estimation on the health state of the energy storage battery, and SOH estimation by taking the curve integral area between u1 and u2 as a characteristic factor, and specifically comprises the following steps:
s1: acquiring real operation data of the energy storage battery, including real measured value data of voltage, current and temperature;
s2: calculating the charge capacity of the energy storage battery by an ampere-hour integration method based on the real operation data of the energy storage battery, and drawing a first curve graph with the charge capacity as an abscissa and the battery voltage as an ordinate, wherein the first curve graph is provided with a first curve;
s3: exchanging the abscissa and the ordinate of the first curve obtained in the step S2 to obtain a second curve with a second curve;
s4: and (3) deriving the second curve obtained in the step (S3) based on the voltage to obtain an IC curve with an ICA value on the ordinate and a battery voltage on the abscissa. The calculation formula is as follows:
wherein U is cell Representing the voltage of the battery cell; q (U) cell ) The voltage of the cell is U cell Capacity at time; ICA (U) cell ) The voltage of the cell is U cell ICA value at that time; ICA allows analysis of the voltage curve shape (slope and plateau) rather than absolute value. The steps of obtaining the IC curve are represented by steps S1 to S4 as shown in fig. 1.
S5: the SOH of the energy storage battery was estimated based on the ICA mid-section capacity (curve integration area between u1 and u 2) and using ICA. Thus, the curve integration area Q between u1 and u2 is calculated now Represents the current middle section capacity, Q now The calculation formula of (2) is as follows:
as shown in fig. 2, wherein Q (u) represents the capacity of the battery when the voltage of the battery is u; ICA (u) represents an ICA value when the voltage of the battery is u; u1 is the voltage value corresponding to the second peak of the IC curve, and u2 is selected as the minimum cut-off voltage that can be reached by all the battery cells.
S6: obtaining initial midsection capacity (Q) of battery through training based on artificial intelligence technology start ) The prediction model inputs operation data (voltage, current and temperature) of the energy storage battery to be estimated in a period of time under the current state of the energy storage battery into the initial middle section capacity prediction model to obtain initial middle section capacity Q of the energy storage battery start Therefore, the SOH of the energy storage battery in the current state can be estimated, and the formula is as follows:
construction of initial midstream Capacity (Q) start ) Prediction model:
since the actual operating energy storage project is affected by two factors, the initial middle section capacity (Q start ) Is one of the innovative points of the present invention. On the one hand, the energy storage project actually operated is difficult to obtain the most initial operation data, so that the initial middle section capacity (Q start ) The method comprises the steps of carrying out a first treatment on the surface of the On the other hand, even if the initial middle-stage capacity (Q start ) However, various differences in the battery's operating conditions during subsequent operation may occur, the initial midsection capacity (Q start ) The actual value of the battery will be changed in various ways, and the initial capacity state of the battery cannot be truly reflected, so that the SOH cannot be estimated by the above method.
Thus, one of the key techniques of the present invention is modeling by artificial intelligence algorithms to achieve a battery initial midsection capacity (Q start ) Is a prediction of (2). According to the prior arrangementIs used for model training:
step 101: an artificial intelligent algorithm with a regression prediction function such as XGboost and the like is adopted, but not limited to, and a regression model reflecting the relation between the initial middle section capacity of the battery and the battery operation parameters is established;
step 102: collecting real operation data of the battery in an initial operation state, and arranging the real operation data into an input data set and an output data set; input characteristics include voltage, current, temperature of the battery cell; by integration, the u1, u2 capacity difference (initial mid-section capacity Q start ) The training data set output part formed by arrangement is used as a prediction target value of the model;
step 103: model training is carried out on the regression model based on the tidied input and output data sets; obtaining the initial middle section capacity (Q) which can be predicted under different operation conditions start ) An initial mid-section capacity prediction model of (a);
step 104: verifying the accuracy of the initial middle-section capacity prediction model, wherein the error between the prediction effect parameter and the preset effect parameter is smaller than a preset threshold value, and considering that the accuracy of the initial middle-section capacity prediction model is good and meets the expectations;
after predicting the initial mid-section capacity (Q) start ) On the premise of (1), calculate Q now The SOH of the energy storage battery in the actual running process can be estimated, and the formula is as follows:
example 2
Referring to fig. 3, the present invention provides an on-line estimation method for the state of health of an energy storage battery, which includes:
s100, acquiring the current middle section capacity Q of the energy storage battery now ；
S200, acquiring operation data in a period of time under the current state of the energy storage battery to be estimated, inputting the operation data into a pre-trained initial middle section capacity prediction model to obtain initial middle section capacity Q of the energy storage battery start ；
S300. According to the current middle section capacity Q of the energy storage battery now And the initial middle section capacity Q of the energy storage battery start Calculating SOH of the energy storage battery in the current state:
in one embodiment: the operational data includes voltage, current, and temperature.
In one embodiment: current middle section capacity Q of energy storage battery now Obtained by the following steps:
obtaining an IC curve of the energy storage battery, and calculating the middle-section capacity Q of the energy storage battery according to the IC curve now ；
Q (u) represents the capacity of the battery when the voltage of the battery is u; ICA (u) represents an ICA value when the voltage of the battery is u; u1 is the voltage value corresponding to the second peak of the IC curve, and u2 is selected as the minimum cut-off voltage that can be reached by all the battery cells.
In one embodiment: the IC curve is obtained by the following steps:
acquiring real operation data of an energy storage battery; the real operation data of the energy storage battery comprise voltage, current and temperature;
calculating the charge capacity of the energy storage battery by an ampere-hour integration method based on the real operation data of the energy storage battery, and drawing a first curve graph with the charge capacity as an abscissa and the battery voltage as an ordinate, wherein the first curve graph is provided with a first curve;
exchanging the abscissa of the first curve graph to obtain a second curve graph with a second curve;
and deriving the second curve based on the voltage to obtain an IC curve with an ICA value on the ordinate and a battery voltage on the abscissa.
In one embodiment: the pre-trained initial middle section capacity prediction model is obtained through the following steps:
establishing a regression model reflecting the relation between the initial middle section capacity of the battery and the battery operation parameters by adopting an artificial intelligent algorithm;
collecting real operation data of the battery in an initial operation state, and arranging the real operation data into an input data set and an output data set; input characteristics include voltage, current, temperature of the battery cell; the initial middle section capacity Q is obtained through integration start An output part for training data set is arranged as a prediction target value of the model;
model training is carried out on the regression model based on the tidied input and output data sets; obtaining the initial middle section capacity Q which can be predicted start Is a model of the initial mid-section capacity prediction.
In one embodiment: the artificial intelligence algorithm is an artificial intelligence algorithm with a regression prediction function.
In one embodiment: the artificial intelligence algorithm is XGboost.
Example 3
Referring to fig. 4, the present invention provides an on-line estimation device for the state of health of an energy storage battery, comprising:
an acquisition module for acquiring the current middle section capacity Q of the energy storage battery now ；
The prediction module is used for acquiring the operation data in a period of time under the current state of the energy storage battery to be estimated, inputting the operation data into a pre-trained initial middle section capacity prediction model to obtain the initial middle section capacity Q of the energy storage battery start ；
An estimation module for estimating the current middle section capacity Q of the energy storage battery now And the initial middle section capacity Q of the energy storage battery start Calculating SOH of the energy storage battery in the current state:
in one embodiment: the operational data includes voltage, current, and temperature.
In one placeIn the specific embodiment, the method comprises the following steps: current middle section capacity Q of energy storage battery now Obtained by the following steps:
obtaining an IC curve of the energy storage battery, and calculating the middle-section capacity Q of the energy storage battery according to the IC curve now ；
Q (u) represents the capacity of the battery when the voltage of the battery is u; ICA (u) represents an ICA value when the voltage of the battery is u; u1 is the voltage value corresponding to the second peak of the IC curve, and u2 is selected as the minimum cut-off voltage that can be reached by all the battery cells.
In one embodiment: the IC curve is obtained by the following steps:
acquiring real operation data of an energy storage battery; the real operation data of the energy storage battery comprise voltage, current and temperature;
calculating the charge capacity of the energy storage battery by an ampere-hour integration method based on the real operation data of the energy storage battery, and drawing a first curve graph with the charge capacity as an abscissa and the battery voltage as an ordinate, wherein the first curve graph is provided with a first curve;
exchanging the abscissa of the first curve graph to obtain a second curve graph with a second curve;
and deriving the second curve based on the voltage to obtain an IC curve with an ICA value on the ordinate and a battery voltage on the abscissa.
In one embodiment: the pre-trained initial middle section capacity prediction model is obtained through the following steps:
establishing a regression model reflecting the relation between the initial middle section capacity of the battery and the battery operation parameters by adopting an artificial intelligent algorithm;
collecting real operation data of the battery in an initial operation state, and arranging the real operation data into an input data set and an output data set; input characteristics include voltage, current, temperature of the battery cell; the initial middle section capacity Q is obtained through integration start Is arranged into a training data set output part which is used as a predicted target value of a model；
Model training is carried out on the regression model based on the tidied input and output data sets; obtaining the initial middle section capacity Q which can be predicted start Is a model of the initial mid-section capacity prediction.
In one embodiment: the artificial intelligence algorithm is an artificial intelligence algorithm with a regression prediction function.
In one embodiment: the artificial intelligence algorithm is XGboost.
Example 4
Referring to fig. 5, the present invention further provides an electronic device 100 for implementing an online estimation method of a state of health of an energy storage battery; the electronic device 100 comprises a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.
The memory 101 may be used to store the computer program 103, and the processor 102 implements the steps of the method for online estimating the state of health of an energy storage battery described in embodiment 1 or 2 by running or executing the computer program stored in the memory 101 and invoking the data stored in the memory 101. The memory 101 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data) created according to the use of the electronic device 100, and the like. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), at least one disk storage device, a Flash memory device, or other non-volatile solid state storage device.
The at least one processor 102 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The processor 102 may be a microprocessor or the processor 102 may be any conventional processor or the like, the processor 102 being a control center of the electronic device 100, the various interfaces and lines being utilized to connect various portions of the overall electronic device 100.
The memory 101 in the electronic device 100 stores a plurality of instructions to implement an on-line estimation method of the state of health of the energy storage battery, and the processor 102 can execute the plurality of instructions to implement:
acquiring current middle section capacity Q of energy storage battery now ；
Acquiring operation data in a period of time under the current state of the energy storage battery to be estimated, inputting the operation data into a pre-trained initial middle section capacity prediction model to obtain the initial middle section capacity Q of the energy storage battery start ；
According to the current middle section capacity Q of the energy storage battery now And the initial middle section capacity Q of the energy storage battery start Calculating SOH of the energy storage battery in the current state:
example 5
The modules/units integrated in the electronic device 100 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. Based on this 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 the computer program may implement the steps of each method embodiment described above when executed by a processor. 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, and a Read-Only Memory (ROM).
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (10)
1. An energy storage battery state of health on-line estimation method is characterized by comprising the following steps:
acquiring current middle section capacity Q of energy storage battery now ；
Acquiring operation data in a period of time under the current state of the energy storage battery to be estimated, inputting the operation data into a pre-trained initial middle section capacity prediction model to obtain the initial middle section capacity Q of the energy storage battery start ；
According to the current middle section capacity Q of the energy storage battery now And the initial middle section capacity Q of the energy storage battery start Calculating SOH of the energy storage battery in the current state:
2. the method of claim 1, wherein the operating data comprises voltage, current, and temperature.
3. The method for online estimation of state of health of an energy storage battery of claim 1, wherein the energy storage battery is configured to store energyCurrent middle capacity Q of battery now Obtained by the following steps:
obtaining an IC curve of the energy storage battery, and calculating the middle-section capacity Q of the energy storage battery according to the IC curve now ；
Q (u) represents the capacity of the battery when the voltage of the battery is u; ICA (u) represents an ICA value when the voltage of the battery is u; u1 is the voltage value corresponding to the second peak of the IC curve, and u2 is selected as the minimum cut-off voltage that can be reached by all the battery cells.
4. The method for online estimation of the state of health of an energy storage battery according to claim 3, wherein the IC curve is obtained by:
acquiring real operation data of an energy storage battery; the real operation data of the energy storage battery comprise voltage, current and temperature;
calculating the charge capacity of the energy storage battery by an ampere-hour integration method based on the real operation data of the energy storage battery, and drawing a first curve graph with the charge capacity as an abscissa and the battery voltage as an ordinate, wherein the first curve graph is provided with a first curve;
exchanging the abscissa of the first curve graph to obtain a second curve graph with a second curve;
and deriving the second curve based on the voltage to obtain an IC curve with an ICA value on the ordinate and a battery voltage on the abscissa.
5. The method for online estimation of the state of health of an energy storage battery according to claim 3, wherein the pre-trained initial midsection capacity prediction model is obtained by:
establishing a regression model reflecting the relation between the initial middle section capacity of the battery and the battery operation parameters by adopting an artificial intelligent algorithm;
the real operation data of the battery in the initial operation state is collected and is organized intoInputting and outputting a data set; input characteristics include voltage, current, temperature of the battery cell; the initial middle section capacity Q is obtained through integration start An output part for training data set is arranged as a prediction target value of the model;
model training is carried out on the regression model based on the tidied input and output data sets; obtaining the initial middle section capacity Q which can be predicted start Is a model of the initial mid-section capacity prediction.
6. The method for online estimation of the state of health of an energy storage battery according to claim 5, wherein the artificial intelligence algorithm is an artificial intelligence algorithm with a regression prediction function.
7. The method for online estimation of the state of health of an energy storage battery of claim 6, wherein the artificial intelligence algorithm is XGboost.
8. An energy storage battery state of health on-line estimation device, characterized by comprising:
an acquisition module for acquiring the current middle section capacity Q of the energy storage battery now ；
The prediction module is used for acquiring the operation data in a period of time under the current state of the energy storage battery to be estimated, inputting the operation data into a pre-trained initial middle section capacity prediction model to obtain the initial middle section capacity Q of the energy storage battery start ；
An estimation module for estimating the current middle section capacity Q of the energy storage battery now And the initial middle section capacity Q of the energy storage battery start Calculating SOH of the energy storage battery in the current state:
9. an electronic device comprising a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement a method of online estimation of the state of health of an energy storage battery as claimed in any one of claims 1 to 7.
10. A computer readable storage medium storing at least one instruction that when executed by a processor implements a method of online estimation of state of health of an energy storage battery according to any one of claims 1 to 7.
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