CN115267586A - Lithium battery SOH evaluation method - Google Patents
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- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 20
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 20
- 238000011156 evaluation Methods 0.000 title claims description 17
- 238000000034 method Methods 0.000 claims abstract description 38
- 239000013598 vector Substances 0.000 claims abstract description 23
- 238000004146 energy storage Methods 0.000 claims abstract description 22
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- 230000036541 health Effects 0.000 claims abstract description 18
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- 238000012549 training Methods 0.000 claims abstract description 9
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- 238000012360 testing method Methods 0.000 claims description 8
- 238000007599 discharging Methods 0.000 claims description 7
- 238000007600 charging Methods 0.000 claims description 5
- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 claims description 5
- 238000010277 constant-current charging Methods 0.000 claims description 4
- 238000012544 monitoring process Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 description 7
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- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 2
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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]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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- G—PHYSICS
- G01—MEASURING; TESTING
- 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]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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Abstract
The embodiment of the application provides a lithium battery SOH assessment method for an energy storage power station, wherein the lithium battery SOH assessment method comprises the following steps: acquiring influence factors of the battery health state in each circulation; wherein the contributing factors to the state of health of the battery are characterized by a resistance and a recovery voltage; acquiring the battery capacity after each cycle to obtain the health state of the battery; converting the considered parameters into neural network learning sample directions; carrying out normalization processing and inverse normalization processing on the obtained learning sample vector to obtain a normalized vector; the neural net is trained using the normalized next vector to obtain a neural network training model that predicts battery SOH.
Description
Technical Field
The application provides a data processing method, in particular to a lithium battery SOH assessment method.
Background
The mathematical modeling is to establish a mathematical model according to an actual problem, solve the mathematical model, and then solve the actual problem according to a result. When a real problem needs to be analyzed and researched from a quantitative point of view, people need to use mathematical symbols and languages as expressions to establish a mathematical model on the basis of the work of deep research and research, object information understanding, simplifying assumptions, analyzing internal rules and the like.
Electric energy is undoubtedly an indispensable energy source for human life and work.
The lithium battery has become an important power source of the electric automobile due to the advantages of a higher discharging platform, long cycle service life, environmental protection, safety and the like. Meanwhile, with the rapid development of the micro-grid technology, a large-scale electrochemical energy storage power station is an effective means for consuming renewable energy sources such as solar energy, wind power and the like for power generation. The lithium iron phosphate battery has the advantages of high charging and discharging efficiency, reliable operation, less environmental pollution and the like, and is widely applied. The energy storage power station can also increase the elasticity of the power grid and reduce the impact range of the load impact of the power grid and the fault of the power grid. In the use process of the full life cycle of the battery, along with the increase of the use time and the charge-discharge cycle times of the battery, the battery can have the characteristic fading phenomena of capacity, energy, power and the like, if the battery cannot be found and treated in time, the fading phenomena can be more serious, so that the battery has the phenomena of bulging, cracking, heating and the like, and in the serious condition, the battery can generate thermal runaway thermal diffusion, so that the serious consequences of fire disasters of automobiles and energy storage power stations and the like are caused. For the scenes that batteries of automobiles and energy storage power stations are densely arranged, a fire disaster often causes serious personal casualty accidents and property loss. In order to ensure safe and reliable operation of the battery, the state of health (SOH) of the battery is evaluated and the short-term state of the battery is accurately predicted, so that early warning defects or fault batteries can be found in time, and the accident potential is reduced. The technology has important significance for improving the safety of the electric automobile, expanding the scale of the energy storage power station and promoting the realization of the carbon neutralization target, and is a hot spot of the current research.
The existing lithium battery SOH evaluation methods can be roughly divided into two categories: model-based methods and data-driven based methods. The model-based method is mainly used for constructing a degradation mathematical model of the lithium battery based on an equivalent circuit model and an electrochemical mechanism model. The method is suitable for SOH evaluation of a single or a small number of lithium batteries, the workload of model building is large for scenes with large battery quantity, such as electric vehicles or energy storage power stations, and the timeliness and the accuracy of evaluation are greatly reduced. The data-based method is characterized in that test data of battery performance are utilized, and information mining is carried out on the data to obtain a lithium battery performance evolution rule for performance and service life prediction. The method can avoid building and updating complex models, has good prediction efficiency for a large number of battery packs, but only considers common parameters such as current, voltage and internal resistance of the lithium battery when data acquisition and evaluation are carried out in the prior art, and does not consider the design, installation and use modes of the lithium battery, so that the selection of driving data characteristics and the accuracy and reliability of evaluation are still to be improved.
Chinese patent application No. CN201811200371.9, application date 2018-10-16, entitled "an online estimation method of health state of lithium ion battery"; the method discloses an online estimation method for the health state of a lithium ion battery. The method mainly comprises the following steps: the characteristic parameters are obtained from the capacity increment curve by adopting a capacity increment method, and the establishment of the characteristic parameters and the SOH function model is completed by utilizing a multi-output Gaussian process regression model method, so that the potential correlation among different outputs can be better utilized, and the estimation precision of the SOH is improved. The battery does not need to undergo a complete charging and discharging process, the characteristic parameters are easy to extract, and the method is beneficial to application in the BMS.
Chinese patent application No. CN202010621694.6, application No. 2020-06-30, entitled method and system for predicting service life of battery monomer in energy storage power station; the method and the system for predicting the service life of the battery monomer in the energy storage power station are disclosed. The method mainly comprises the following steps: and collecting historical test data of the cyclic degradation of the capacity of a plurality of batteries, and secondarily screening out the characteristic of high sensitivity. The method considers the internal resistance, the discharge time and the parameters under specific voltage, but does not consider the change situation of the corresponding parameters when the battery voltage changes when the battery is in a static state.
Disclosure of Invention
The application provides a data processing method, which can be used for more accurately evaluating the SOH of the lithium battery for the energy storage power station considering the recovery voltage characteristics.
In order to achieve the above object, an embodiment of the present application provides a method for evaluating an SOH of a lithium battery for an energy storage power station, which takes recovery voltage characteristics into consideration, and includes:
step 1, obtaining influence factors of the battery health state in each circulation; wherein the contributing factors to the state of health of the battery are characterized by a resistance and a recovery voltage; acquiring the battery capacity after each cycle to obtain the health state of the battery;
collecting the voltage of the battery every 1 second within 10 seconds after the constant current charging or constant current discharging of the battery is finished each time: u shape10,U11,U12,U13,U14,U15,U16,U17,U18,U19And the voltage collected by the nth battery is as follows: u shapen0,Un1,Un2,Un3,Un4,Un5,Un6,Un7,Un8,Un9；
Wherein the rated voltage of the battery is UNBy the formula Δ Unm＝UN-UnmCalculating the voltage difference between the measured voltage of the battery and the rated voltage of the battery at the mth second after the nth battery is charged;
ΔUn0,ΔUn1,ΔUn2,ΔUn3,ΔUn4,ΔUn5,ΔUn6,ΔUn7,ΔUn8,ΔUn9；
according to the strength of the relation between the SOH of the lithium iron phosphate battery and characteristic parameters and the acquired experimental data, selecting recovery voltage and basic operation data of the energy storage battery to reconstruct into a sample, wherein the SOH of the battery is a sample label;
wherein the battery SOH utilization capacity is expressed as:
wherein Q0Designing capacity for the battery, either calibrated by the manufacturer or obtained by means of tests, QnowIs the current maximum capacity of the battery;
step 2, converting the considered parameters into neural network learning sample vectors, wherein the ith vector
Wherein IciCurrent for charging (charge) of the ith cycle; u shapeOCiOpen Circuit (Open Circuit) voltage for cycle i; s. theOCiState of Charge for the ith cycle (State of Charge); delta UnmMeasuring the voltage difference between the voltage and the rated voltage of the battery at the mth second after the nth battery is charged; SOHiState of Health of the battery in the ith cycle; SOCiIs the percentage SOC of the remaining batteryiIs the percentage of the remaining electric quantity of the battery;
step 3, carrying out normalization processing on the obtained learning sample vector, wherein the normalization formula is as follows:
wherein xmaxIs the maximum value of the data; x is the number ofminIs the minimum value of the data;
normalizing the data obtained through the neural network further needs to perform inverse normalization processing; the denormalization formula is: x is the number ofi'＝(xmax-xmin)xi+xmin；
The normalized vector thus obtained:
and 4, training the neural saving by utilizing the normalized lower vector to obtain a neural network training model for predicting the SOH of the battery.
Further, the method further comprises:
collecting working current, voltage, resistance and working temperature parameters of the battery;
and transmitting the acquired data to a local database of the energy storage power station for storage, and reserving the original data in the database in the station for at least 3 months for subsequent monitoring and use.
Further, the method further comprises:
and taking one part of the acquired normalized vectors as a training set sample, and taking the other part of the acquired normalized vectors as a test set sample.
The beneficial effects of the above technical scheme of this application are as follows: the technical scheme provides a lithium battery SOH assessment method for the energy storage power station considering the recovery voltage characteristics, and the scheme has at least one of the following advantages:
1. the recovery voltage is added to be used as an input vector of the evaluation neural network, so that the accuracy of the evaluation effect is improved, and the evaluation speed is shortened;
2. the recovery voltage is obtained from the original voltage data of the battery, other sensing recording equipment is not needed, and the increase of construction and maintenance cost is avoided;
3. the recovery voltage is added as an input vector, the generalization performance of the evaluation neural network is improved, the evaluation neural network is convenient to transplant for other energy storage equipment with different scales, and the repeated design evaluation method and the waste of time and fund are avoided;
4. the method is reasonable in scheme, simple in principle and easy to realize, and can fully exert the advantages of the evaluation neural network.
Drawings
FIG. 1 is a schematic flow chart diagram of an embodiment of the present application;
fig. 2 is a schematic diagram of a neural network according to an embodiment of the present application.
Detailed Description
To make the technical problems, technical solutions and advantages to be solved by the present application clearer, the following detailed description is made with reference to the accompanying drawings and specific embodiments.
The application aims to provide a lithium battery SOH assessment method for an energy storage power station considering recovery voltage characteristics, which is used for solving the problem of accurate assessment of the health state of a lithium iron phosphate energy storage battery in an electric automobile and a large-scale electrochemical energy storage power station.
The embodiment of the application provides a lithium battery SOH evaluation method for an energy storage power station, wherein the method comprises the following steps of (1) adopting a neural network shown in fig. 2; the method comprises the following steps:
and acquiring the resistance and recovery voltage in each cycle as influencing factors for representing the health state of the battery, and acquiring the capacity of the battery after each cycle to acquire the health state of the battery. The battery is placed for a period of time after each constant current charging or constant current discharging so as to enable the battery to reach electrochemical balance, and the voltage of the battery changes to a certain extent in the placing stage, and the voltage which changes due to the placing is called recovery voltage. Collecting the voltage of the battery every 1 second within 10 seconds after each constant current charging or constant current discharging of the battery
U10,U11,U12,U13,U14,U15,U16,U17,U18,U19，
Wherein the voltage collected by the nth battery is
Un0,Un1,Un2,Un3,Un4,Un5,Un6,Un7,Un8,Un9。
Wherein the rated voltage of the battery is UNBy the formula Δ Unm＝UN-UnmCalculating the voltage difference between the measured voltage of the mth battery and the rated voltage of the mth battery in the second after the nth battery is charged
ΔUn0,ΔUn1,ΔUn2,ΔUn3,ΔUn4,ΔUn5,ΔUn6,ΔUn7,ΔUn8,ΔUn9。
Except for recovering voltage data, parameters such as battery working current, voltage, resistance, working temperature and the like are collected at the same time, the collected data are transmitted to a local database of an energy storage power station for storage, and original data are reserved in the database in the station for at least 3 months, so that subsequent monitoring and use are facilitated.
And selecting recovery voltage and basic operation data of the energy storage battery to reconstruct into a sample according to the strength of the relation between the SOH of the lithium iron phosphate battery and the characteristic parameters and the acquired experimental data, wherein the SOH of the battery is a sample label. The SOH of the battery is expressed by capacity, Q0The capacity is designed for the battery, and the parameter can be calibrated by the manufacturer or obtained by a test mode, QnowThe current maximum capacity of the battery is used as a learning sample label, and the following relations are provided:
converting the considered parameters into neural network learning sample vectors, wherein the ith vector
Wherein IciCurrent for charging (charge) of the ith cycle; u shapeOCiOpen Circuit (Open Circuit) voltage for cycle i; sOCiState of Charge (State of Charge) for the ith cycle; delta UnmMeasuring the voltage difference between the voltage and the rated voltage of the battery at the mth second after the nth battery is charged; SOHiState of Health of the battery in the ith cycle; SOCiIs the percentage SOC of the remaining batteryiIs the percentage of the remaining capacity of the battery.
In order to ensure the speed and convergence reliability of the data processed by the neural network and prevent the type and size of the data from influencing the prediction result of the neural network, the data needs to be normalized to meet the precondition. The normalization process can make the data sizes relatively closer, so that some data with large sample data value difference and less sample number can be properly processed.
The normalized formula is:
normalizing the data obtained via the neural network also requires an inverse normalization process.
The denormalization formula is:
xi'＝(xmax-xmin)xi+xmin
in the formula: x is the number ofmaxRepresents the maximum value of the data; x is the number ofminRepresents the minimum value of the data.
The normalized vector thus obtained:
and training the neural network after taking the vector as a sample to form the neural network for judging and predicting the SOH of the battery. Experimental group data 80% of the samples were set as training set and 20% of the samples were set as testing set. And after the neural network is formed, introducing a new sample for calculation, and realizing short-term SOH prediction of the battery in the energy storage power station.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs. The procedures or functions described in accordance with the embodiments of the present application are generated in whole or in part when the computer program is loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer program can be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer program can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a Digital Video Disk (DVD)), or a semiconductor medium (e.g., a Solid State Disk (SSD)), among others.
Those of ordinary skill in the art will understand that: the various numbers of the first, second, etc. mentioned in this application are only used for the convenience of description and are not used to limit the scope of the embodiments of this application, but also to indicate the sequence.
At least one of the present applications may also be described as one or more, and a plurality may be two, three, four or more, and the present application is not limited thereto. In the embodiment of the present application, for a technical feature, the technical features in the technical feature are distinguished by "first", "second", "third", "a", "B", "C", and "D", and the like, and the technical features described in "first", "second", "third", "a", "B", "C", and "D" are not in a sequential order or a size order.
The correspondence shown in the tables in the present application may be configured or predefined. The values of the information in each table are only examples, and may be configured to other values, which is not limited in the present application. When the correspondence between the information and each parameter is configured, it is not always necessary to configure all the correspondences indicated in each table. For example, in the table in the present application, the correspondence shown in some rows may not be configured. For another example, appropriate modification adjustments, such as splitting, merging, etc., can be made based on the above tables. The names of the parameters in the tables may be other names understandable by the communication device, and the values or the expression of the parameters may be other values or expressions understandable by the communication device. When the above tables are implemented, other data structures may be used, for example, arrays, queues, containers, stacks, linear tables, pointers, linked lists, trees, graphs, structures, classes, heaps, hash tables, or hash tables may be used.
Predefinition in this application may be understood as defining, predefining, storing, pre-negotiating, pre-configuring, curing, or pre-firing.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (3)
1. A lithium battery SOH assessment method is characterized by comprising the following steps:
step 1, obtaining influence factors of the battery health state in each circulation; wherein the contributing factors to the state of health of the battery are characterized by a resistance and a recovery voltage; acquiring the battery capacity after each cycle to obtain the health state of the battery;
collecting the voltage of the battery every 1 second within 10 seconds after each constant current charging or constant current discharging of the battery is finished: u shape10,U11,U12,U13,U14,U15,U16,U17,U18,U19And the voltage collected by the nth battery is as follows: u shapen0,Un1,Un2,Un3,Un4,Un5,Un6,Un7,Un8,Un9；
Wherein the rated voltage of the battery is UNBy the formula Δ Unm＝UN-UnmCalculating the voltage difference between the measured voltage of the battery and the rated voltage of the battery at the mth second after the nth battery is charged;
ΔUn0,ΔUn1,ΔUn2,ΔUn3,ΔUn4,ΔUn5,ΔUn6,ΔUn7,ΔUn8,ΔUn9；
according to the strength of the relation between the SOH of the lithium iron phosphate battery and characteristic parameters and the acquired experimental data, selecting recovery voltage and basic operation data of the energy storage battery to reconstruct into a sample, wherein the SOH of the battery is a sample label;
wherein the battery SOH utilization capacity is expressed as:
wherein Q is0Designing capacity for the battery, either by factory calibration or by test, QnowThe current maximum capacity of the battery;
step 2, converting the considered parameters into neural network learning sample vectors, wherein the ith vector
Wherein IciCurrent for charging of the i-th cycle; u shapeOCiOpen circuit voltage for the ith cycle; sOCiThe state of charge for the ith cycle; delta UnmMeasuring the voltage difference between the voltage and the rated voltage of the battery at the mth second after the nth battery is charged; SOHiIs the state of health of the battery in the ith cycle; SOCiIs the percentage of the remaining electric quantity of the battery;
step 3, carrying out normalization processing on the obtained learning sample vector, wherein the normalization formula is as follows:
wherein x ismaxIs the maximum value of the data; x is the number ofminIs the minimum value of the data;
normalizing the data obtained through the neural network further needs to perform inverse normalization processing; the denormalization formula is: x'i＝(xmax-xmin)xi+xmin；
The normalized vector thus obtained:
and 4, training the neural saving by utilizing the normalized lower vector to obtain a neural network training model for predicting the SOH of the battery.
2. The lithium battery SOH evaluation method of claim 1, further comprising:
collecting working current, voltage, resistance and working temperature parameters of a battery;
and transmitting the acquired data to a local database of the energy storage power station for storage, and reserving the original data in the database in the station for at least 3 months for subsequent monitoring and use.
3. The lithium battery SOH evaluation method of claim 1, further comprising:
and taking one part of the acquired normalized vectors as a training set sample, and taking the other part of the acquired normalized vectors as a test set sample.
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