CN114611836A - Energy storage battery risk prediction method, device, medium and equipment - Google Patents
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Abstract
The embodiment of the invention discloses a risk prediction method for an energy storage battery. Determining recoverable loss capacity Q according to functional relationship between recoverable loss resistance and permanent loss resistance and loss capacity corresponding to the sameCan be recoveredAnd permanent loss capacity QPermanent loss(ii) a The Q is addedCan be recoveredAnd QPermanent lossAnd as a sample, training a risk prediction model to obtain the occurrence probability and tolerance of various risks of the energy storage battery, predicting the occurrence of possible risks in advance, early warning in advance, and taking a coping strategy in advance.
Description
Technical Field
The embodiment of the invention relates to the technical field of energy storage batteries, in particular to a method, a device, a medium and equipment for predicting risk of an energy storage battery.
Background
At present, in the field of lithium batteries, the lithium battery has the advantages of high energy density, environmental protection, long service life and the like, and is widely applied to new energy vehicles, electric tools and various energy storage application scenes.
The remaining life of a battery is typically described in terms of battery state of health (SOH). There are various methods for evaluating state of health (SOH) at present, and the most common are the internal resistance method and the residual capacity method. When the SOH is reduced to below 80%, the use is stopped or limited. Most of the existing technologies implement a method for predicting the SOH of a battery by performing SOC calculation and the like according to collected data from rated information and state monitoring data (voltage, current, temperature, SOC, and the like) of the battery, and then predict the risk of the battery based on the SOH result.
The prior art lacks a mode for quickly and accurately predicting the SOH of the battery, and the prior art cannot distinguish different types of risk conditions corresponding to different types of health degree reduction of the battery.
Disclosure of Invention
The embodiment of the invention provides a method, a device, a medium and equipment for predicting the risk of an energy storage battery, which can predict the occurrence of possible risk in advance, early warn and adopt a coping strategy in advance.
In a first aspect, an embodiment of the present invention provides a method for predicting a risk of an energy storage battery, where the method includes:
determining recoverable loss capacity Q according to functional relationship between recoverable loss resistance and permanent loss resistance and loss capacity corresponding to the recoverable loss resistanceCan recoverAnd permanent loss capacity QPermanent loss；
The Q is addedCan be recoveredAnd QPermanent lossAs a sample, training a risk prediction model to obtain occurrence probability and tolerance of various risks of the energy storage battery, wherein the risks include: thermal runaway, internal short circuit, overcharge, and overdischarge.
Optionally, Q is determined according to a functional relationship between the recoverable loss resistance and the permanent loss resistance and their corresponding loss capacitiesCan be recoveredAnd QPermanent loss ofThe calculation formula of (2) is as follows:
wherein R isPermanent lossFor permanent loss of resistance, RRecoverable lossTo restore the loss resistance, RDC assemblyLoss of powerAdding a value Delta R to the DC internal resistance DCRDC，QTotal lossIs the total lost capacity value.
Optionally, RPermanent lossAnd RRecoverable lossWhen the charge and the discharge are carried out for n times in a circulating mode, the charge and the discharge are calculated based on data results of a direct current internal resistance test and an alternating current impedance test and the following formula:
△RDC＝Rpermanent loss+RRecoverable loss (3)；
RPermanent loss＝(△Rs+α△Rct)/(△Rs+△Rct+△Rw)*△RDC (4)；
RRecoverable loss＝(△Rw+β△Rct)/(△Rs+△Rct+△Rw)*△RDC (5)；
Wherein, Δ RsIncrease the ohmic internal resistance value,. DELTA.RctIncrease in value for electrochemical internal resistance, and Δ RwFor increasing the diffusion internal resistance, wherein alpha + beta is 1, and alpha and beta are respectively delta RctDistribution coefficients under the conditions of permanent loss internal resistance and recoverable loss internal resistance.
Optionally, the data result processing process of the dc internal resistance test is as follows:
acquiring direct current resistance DCR data of different SOC (state of charge) respectively in a charge-discharge state, and constructing a DCR-SOC fitting curve;
according to the DCR-SOC fitting curve, obtaining DCR data and carrying out normalization processing as a DCR initial value;
acquiring DCR cycle values of different SOC respectively under n-time cyclic charge-discharge states, and determining Delta R based on the DCR initial valuesDC。
Optionally, the data result processing process of the ac impedance test includes:
respectively obtaining ohmic internal resistances R of different SOC in charging and discharging states by an alternating current impedance methodsElectrochemical internal resistance RctAnd internal diffusion resistance RwAnd determining the corresponding RsInitial value, RctInitial value and RwAn initial value;
respectively obtaining R of different SOC under n times of cyclic charge-discharge statesCycle value, RctCycle value and RwThe cycle value is determined and DeltaR is determined based on its corresponding initial values、△RctAnd Δ Rw。
Optionally, the method further includes:
according to historical empirical data, inputting influence factors a, b, c and d into the risk prediction model for learning, wherein a is QPermanent lossB is Q as an influence factor of thermal runaway and internal short circuitCan be recoveredAs the influence factor of thermal runaway and internal short circuit, wherein a > b. c is a radical ofCan be recoveredD is Q as an influence factor of overcharge and overdischargePermanent loss ofAs a factor influencing overcharge and overdischarge, wherein c > d.
Optionally, the sample further comprises: historical risk probability data for various types of energy storage batteries.
In a second aspect, an embodiment of the present invention provides an energy storage battery risk prediction apparatus, where the apparatus includes:
a capacity loss module for determining recoverable loss capacity Q according to the functional relationship between recoverable loss resistance and permanent loss resistance and corresponding loss capacityCan be recoveredAnd permanent loss capacity QPermanent loss；
A risk prediction module to predict the QCan be recoveredAnd QPermanent lossAs a sample, training a risk prediction model to obtain occurrence probabilities and tolerances of various risks of the energy storage battery, wherein the risks include: thermal runaway, internal short circuit, overcharge, and overdischarge.
Optionally, in the capacity loss module, Q is determinedCan recoverAnd QPermanent lossThe calculation formula of (c) is:
wherein R isPermanent loss ofFor permanent loss of resistance, RRecoverable lossTo restore the loss resistance, RTotal loss of DCAdding a value Delta R to the DC internal resistance DCRDC，QTotal lossIs the total lost capacity value.
Optionally, RPermanent lossAnd RRecoverable lossWhen the battery is charged and discharged for n times in a circulating mode, the battery is calculated based on data results of a direct current internal resistance test and an alternating current impedance test and the following formula:
△RDC＝Rpermanent loss+RRecoverable loss (3)；
RPermanent loss＝(△Rs+α△Rct)/(△Rs+△Rct+△Rw)*△RDC (4)；
RRecoverable loss＝(△Rw+β△Rct)/(△Rs+△Rct+△Rw)*△RDC (5)；
Wherein, Δ RsIncrease the ohmic internal resistance value,. DELTA.RctIncrease in electrochemical internal resistance, and Δ RwFor increasing the diffusion internal resistance, wherein alpha + beta is 1, and alpha and beta are respectively delta RctDistribution coefficients under the conditions of permanent loss internal resistance and recoverable loss internal resistance.
Optionally, the data result processing process of the direct current internal resistance test in the capacity loss module is as follows:
respectively acquiring direct current resistance DCR data of different SOC (state of charge) under the charging and discharging states, and constructing a DCR-SOC fitting curve;
according to the DCR-SOC fitting curve, obtaining DCR data and carrying out normalization processing as a DCR initial value;
respectively obtaining DCR circulation values of different SOC under the state of n times of circulation charging and discharging, and determining delta R based on the DCR initial valueDC。
Optionally, the data result processing process of the ac impedance test in the capacity loss module is as follows:
respectively obtaining ohmic internal resistances R of different SOC in charging and discharging states by an alternating current impedance methodsElectrochemical internal resistance RctAnd internal diffusion resistance RwAnd determining the corresponding RsInitial value, RctInitial value and RwAn initial value;
respectively obtaining R of different SOC under n times of cyclic charge-discharge statesCycle value, RctCycle value and RwThe cycle value is determined and DeltaR is determined based on its corresponding initial values、△RctAnd Δ Rw。
Optionally, the method further includes:
an influence factor adding module used for inputting influence factors a, b, c and d into the risk prediction model for learning according to historical empirical data, wherein a is QPermanent lossB is Q as an influence factor of thermal runaway and internal short circuitCan recoverAs the influence factor of thermal runaway and internal short circuit, wherein a > b. c is a radical ofCan be recoveredD is Q as an influence factor of overcharge and overdischargePermanent lossAs a factor influencing overcharge and overdischarge, wherein c > d.
Optionally, the sample further comprises: historical risk probability data for various types of energy storage batteries.
In a third aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the energy storage battery risk prediction method as described above.
In a fourth aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable by the processor, where the processor executes the computer program to implement the energy storage battery risk prediction method as described above.
The embodiment of the invention determines the restorable loss capacity Q according to the functional relation between the restorable loss resistance and the permanent loss resistance and the corresponding loss capacityCan be recoveredAnd permanent loss capacity QPermanent loss(ii) a The Q is addedCan be recoveredAnd QPermanent lossAnd as a sample, training a risk prediction model to obtain the occurrence probability and tolerance of various risks of the energy storage battery, predicting the occurrence of possible risks in advance, early warning in advance, and taking a coping strategy in advance.
Drawings
Fig. 1 is a flowchart of a risk prediction method for an energy storage battery according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a neural network model of an energy storage battery risk prediction method according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an energy storage battery risk prediction apparatus according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some structures related to the present invention are shown in the drawings, not all of them.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently, or simultaneously. In addition, the order of the steps may be rearranged. The process may be terminated when its operations are completed, but could have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, subprograms, and the like.
Firstly, the implementation of the scheme can be based on the following premises:
the network module distinguishes a server side and a client side.
The application program is divided into the server and the client, but different from most application programs needing to be divided into the server and the client, the product does not want to set up a computer as a server separately in consideration of cost control, program starting freedom, convenience and the like.
Therefore, after the program is started, the network module firstly analyzes the information recorded in the configuration file to judge whether the program is a server or not, if the program is the server, the program is a server and a client, and other computers are clients.
And determining a network transmission communication protocol.
According to the network environment of the program, UDP is determined as an underlying network transmission communication protocol, but considering that the UDP protocol is an unreliable protocol, the problems of network data packet loss, no guarantee of the sequence and the like occur, and therefore the scheme of using UDP + KCP is selected to realize reliable UDP transmission. In addition, in the preparation stage of user login, TCP is used as a network transmission communication protocol, so that the reliability of user login is ensured.
Specifying parameter settings in the synchronization logic.
The parameters needed in the synchronization logic are specified so that the parameters set in advance can be conveniently used in the process of realizing the synchronization algorithm, and the method specifically comprises the following steps: the method comprises the steps of determining the IP address of a server, the network port of the server, the IP address of a local client, the frame interval of the server, the frame interval of heartbeat packets, the time for the server to judge the overtime drop of the client, the time for the client to judge the overtime drop of the server and the frame rate multiple of the client.
A synchronization message data protocol is specified.
Firstly, the message type needs to be specified, specifically: synchronous preparation, synchronous start, data tracking, synchronous exit, heartbeat package, and custom message. Then, message data needs to be specified, specifically: message type, player ID of message origin, player ID of message target, tracking data, Ping value timestamp, custom message. Finally, an uplink protocol of data sent by the client to the server and a downlink protocol of data sent by the server to the client need to be specified, wherein the uplink protocol specifically includes: session ID, message list, and the downlink protocol specifically includes frame ID and message list.
Example one
Fig. 1 is a flowchart of a method for predicting risk of an energy storage battery according to an embodiment of the present invention, where the method may be performed by an apparatus for predicting risk of an energy storage battery according to an embodiment of the present invention, and the apparatus may be implemented in a software and/or hardware manner. The method specifically comprises the following steps:
s110, determining recoverable loss capacity Q according to functional relation between recoverable loss resistance and permanent loss resistance and corresponding loss capacityCan recoverAnd permanent loss capacity QPermanent loss。
Specifically, the present embodiment divides the battery capacity loss into two parts, i.e., a recoverable loss capacity and a permanent loss capacity (unrecoverable loss capacity). The permanent loss capacity is mainly caused by the reasons of material structure change, SEI film increase, side reaction, material vacancy reduction and the like. The recoverable lost capacity is mainly caused by polarization, reactivatable lithium and the like.
Therefore, the present embodiment determines the recoverable loss capacity Q by establishing a functional relationship between the recoverable loss resistance and the permanent loss resistance and their corresponding loss capacitiesCan be recoveredAnd permanent loss capacity QPermanent loss。
In this embodiment, Q is determined as a function of recoverable loss resistance, permanent loss resistance, and its corresponding loss capacityCan be recoveredAnd QPermanent lossThe calculation formula of (2) is as follows:
wherein R isPermanent lossFor permanent loss of resistance, RRecoverable lossTo restore the loss resistance, RTotal loss of DCIs the internal resistance D of direct currentCR increment value DeltaRDC，QTotal lossIs the total lost capacity value.
Wherein Q isTotal lossThe current capacity may be subtracted from the initial capacity.
In this embodiment, RPermanent lossAnd RRecoverable lossWhen the charge and the discharge are carried out for n times in a circulating mode, the charge and the discharge are calculated based on data results of a direct current internal resistance test and an alternating current impedance test and the following formula:
△RDC＝Rpermanent loss+RRecoverable loss (3)；
RPermanent loss of＝(△Rs+α△Rct)/(△Rs+△Rct+△Rw)*△RDC (4)；
RRecoverable loss＝(△Rw+β△Rct)/(△Rs+△Rct+△Rw)*△RDC (5)；
Wherein, Δ RsIncrease value of ohmic internal resistance, Delta RctIncrease in electrochemical internal resistance, and Δ RwFor increasing the diffusion internal resistance, wherein alpha + beta is 1, and alpha and beta are respectively delta RctDistribution coefficients under the conditions of permanent loss internal resistance and recoverable loss internal resistance.
Therefore, the present embodiment divides the total DCR increase into two parts, the R permanent loss and the R recoverable loss. And establishing proportion of R permanent loss and R recoverable loss respectively accounting for capacity loss through direct current internal resistance and alternating current impedance measurement results.
In the embodiment of the present invention, the data result processing process of the dc internal resistance test includes: acquiring direct current resistance DCR data of different SOC (state of charge) respectively in a charge-discharge state, and constructing a DCR-SOC fitting curve; according to the DCR-SOC fitting curve, obtaining DCR data and carrying out normalization processing as a DCR initial value; acquiring DCR cycle values of different SOC respectively under n-time cyclic charge-discharge states, and determining Delta R based on the DCR initial valuesDC。
Specifically, the present embodiment can be described by taking a 280Ah lithium iron phosphate square aluminum-shell battery as an example,specifically, according to an HPPC test method, DCR (direct current internal resistance) of a lithium battery in a charging state and a discharging state at 25 +/-3 ℃ under different SOC (10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% and 90%) is tested, and then an interpolation method is used to obtain a DCR-SOC fitting curve between adjacent batteries. And then storing and calling the obtained fitting curve data in an evaluation device, and normalizing the DCR in the charging state and the discharging state to be used as an initial value of the DCR. Finally, after a certain number of charge-discharge cycles, DCR cycle values under different SOC (10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%) are respectively measured, and a DCR increase value delta R is determined based on the DCR initial valueDC。
In an embodiment of the present invention, a data result processing procedure of the ac impedance test includes: respectively obtaining ohmic internal resistances R of different SOC in charging and discharging states by an alternating current impedance methodsElectrochemical internal resistance RctAnd internal diffusion resistance RwAnd determining the corresponding RsInitial value, RctInitial value and RwAn initial value; respectively obtaining R of different SOC under n times of cyclic charge-discharge statesCycle value, RctCycle value and RwThe cycle value is determined and DeltaR is determined based on its corresponding initial values、△RctAnd Δ Rw。
Specifically, in this embodiment, an alternating current impedance method (EIS test method) is adopted, the fitting under SOC of 0% and 100% is determined, the internal resistances under the remaining SOCs (10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90%) are obtained, and the ohmic internal resistance R is obtained through fittingsInitial value, electrochemical internal resistance RctInitial value and diffusion internal resistance RwAn initial value. Then respectively obtaining R of different SOC under the condition of n times of cyclic charge-discharge statesCycle value, RctCycle value and RwThe cycle value is determined and DeltaR is determined based on its corresponding initial values、△RctAnd Δ Rw。
S120, mixing the QCan be recoveredAnd QPermanent lossAs a sample, training a risk prediction model to obtainObtaining the occurrence probability and tolerance of various risks of the energy storage battery, wherein the risks comprise: thermal runaway, internal short circuit, overcharge, and overdischarge.
The risk training model is a BP neural network algorithm established for obtaining various risk predictions of the energy storage battery according to the embodiment of the present invention, as shown in fig. 2. The probability of occurrence of various risks of the energy storage battery comprises the probability of occurrence of thermal runaway, the probability of occurrence of internal short circuit, the probability of occurrence of overcharge and the probability of occurrence of overdischarge. The tolerance refers to the relationship between the occurrence probabilities of the various risks, and overcharge is taken as an example for explanation, for example, when an abnormal cell is charged to 3.5V corresponding to one probability and 3.6V corresponding to another probability, then 100% of the abnormal cell is overcharged, and the embodiment can intervene in advance according to the tolerance relationship to ensure the safe operation of the energy storage battery.
In the embodiment of the present invention, the method further includes: according to historical empirical data, inputting influence factors a, b, c and d into the risk prediction model for learning, wherein a is QPermanent lossB is Q as an influence factor of thermal runaway and internal short circuitCan recoverAs the influence factor of thermal runaway and internal short circuit, wherein a > b. c is a radical ofCan be recoveredD is Q as an influence factor of overcharge and overdischargePermanent lossAs a factor influencing overcharge and overdischarge, wherein c > d.
Specifically, QPermanent lossThe corresponding possible risks are thermal runaway and internal short circuits, and are generally manifested as increased internal resistance and rapid temperature rise. QCan be recoveredCorresponding possible risks are overcharging and overdischarging. Thus Q will bePermanent loss ofAs thermal runaway and internal short circuit influencing factors, a, QCan recoverThe thermal runaway and internal short circuit impact factor is set to b, where a > b. In the same way, QCan be recoveredThe influence factors on overcharge and overdischarge are set to c, QPermanent loss ofThe influence factor on overcharge and overdischarge is set to d, where c > d. In addition, in this embodiment, in order to distinguish the magnitude of the influence, there are strong influence and weak influence, so a > b and c > d are set according to theory and experience. A, b, c,b. And c and d are assigned in advance according to theory and experience, so that the learning amount is reduced, the accuracy requirement can be met in a short time, the subsequent risk prediction model can be conveniently learned, and the calculation amount is effectively reduced.
In an embodiment of the present invention, the sample further comprises: historical risk probability data for various types of energy storage batteries.
Specifically, the energy storage batteries are divided into a plurality of types, so that all historical risk probability data of various energy storage batteries can be used as learning materials for learning of the risk prediction model, and accuracy of risk prediction of the risk prediction model is improved. For example, the present embodiment may learn different risk probabilities of the same type of battery as a sample, and the same type of battery may refer to the same material (positive and negative electrodes, electrolyte), and the same structure (cylinder, square, soft package), such as a lithium iron phosphate square battery and a ternary soft package battery. The different risk probabilities may be data obtained through experimental testing and historical data.
In the embodiment, different risk probabilities of the same type of batteries and the obtained Q are usedCan be recoveredAnd QPermanent lossAs a sample, sampling the BP neural network algorithm to form QCan be recoveredAnd QPermanent lossRespectively influencing the occurrence probability and tolerance of thermal runaway, internal short circuit, overcharge, overdischarge and the like, estimating various influencing factors a, b, c, d and the like, and establishing QPermanent lossAnd QCan be recoveredThe tolerance to the risks such as thermal runaway, internal short circuit, overcharge and overdischarge is respectively related, and R isPermanent lossAnd RRecoverable lossAnd the rate of temperature rise (the temperature of the battery can be measured in real time during the charging process, and the temperature rise of the abnormal battery is fast) as a correction, so that the Q can be correctedPermanent loss ofAnd QCan recoverAnd RPermanent lossAnd RRecoverable lossThe measurement of (3) predicts the probability of occurrence of different risk categories of the battery, and achieves the purpose of predicting in advance.
In the field of lithium batteries, risk prediction is increasingly important, the occurrence of possible risks can be predicted in advance, early warning is performed in advance, and a coping strategy is taken in advance. However, the accuracy of the internal resistance method or the capacity loss method is difficult to grasp, and the method is also influenced by the health state of the lithium battery, and has hysteresis, so that the early warning effect is greatly reduced.
The method and the device for predicting the risk of the energy storage battery can well solve the problems. The method divides the lost capacity into two parts of recoverable lost capacity and permanent lost capacity, which respectively correspond to the SOH of the lithium battery and the corresponding risks, and the different parts form corresponding different risks, so that the occurrence types of the risks can be predicted in advance, and the prevention and control are accurate. In addition, the recoverable loss capacity and the permanent loss capacity correspond to different internal resistances and battery information, and measurement can be facilitated. The input term of the neural network algorithm is only two terms QPermanent lossAnd QCan recoverAnd the middle is filtered, so that a large amount of data does not need to be prepared for training, the training directivity is strong, the model is conveniently and quickly established, and the risk type and the tolerance of the energy storage battery can be effectively and accurately predicted.
Example two
Fig. 3 is a schematic structural diagram of an energy storage battery risk prediction apparatus provided in an embodiment of the present invention, where the apparatus specifically includes:
a capacity loss module 310 for determining a recoverable loss capacity Q according to a functional relationship between the recoverable loss resistance and the permanent loss resistance and the corresponding loss capacityCan recoverAnd permanent loss capacity QPermanent loss；
A risk prediction module 320 for predicting the QCan be recoveredAnd QPermanent lossAs a sample, training a risk prediction model to obtain occurrence probability and tolerance of various risks of the energy storage battery, wherein the risks include: thermal runaway, internal short circuit, overcharge, and overdischarge.
Optionally, in the capacity loss module 310, Q is determinedCan recoverAnd QPermanent lossThe calculation formula of (2) is as follows:
wherein R isPermanent lossFor permanent loss of resistance, RRecoverable lossTo restore the loss resistance, RTotal loss of DCAdding value delta R to DC internal resistance DCRDC，QTotal loss ofIs the total lost capacity value.
Optionally, RPermanent lossAnd RRecoverable lossWhen the battery is charged and discharged for n times in a circulating mode, the battery is calculated based on data results of a direct current internal resistance test and an alternating current impedance test and the following formula:
△RDC＝Rpermanent loss+RRecoverable loss (3)；
RPermanent loss of＝(△Rs+α△Rct)/(△Rs+△Rct+△Rw)*△RDC (4)；
RRecoverable loss＝(△Rw+β△Rct)/(△Rs+△Rct+△Rw)*△RDC (5)；
Wherein, Δ RsIncrease the ohmic internal resistance value,. DELTA.RctIncrease in electrochemical internal resistance, and Δ RwFor increasing the diffusion internal resistance, wherein alpha + beta is 1, and alpha and beta are respectively delta RctDistribution coefficients under the conditions of permanent loss internal resistance and recoverable loss internal resistance.
Optionally, the data result processing process of the dc internal resistance test in the capacity loss module 310 is as follows:
respectively acquiring direct current resistance DCR data of different SOC (state of charge) under the charging and discharging states, and constructing a DCR-SOC fitting curve; according to the DCR-SOC fitting curve, obtaining DCR data and carrying out normalization processing as a DCR initial value; acquiring DCR cycle values of different SOC respectively under n-time cyclic charge-discharge states, and determining Delta R based on the DCR initial valuesDC。
Optionally, the data result processing procedure of the ac impedance test in the capacity loss module 310 is as follows:
respectively obtaining ohmic internal resistances R of different SOC in charging and discharging states by an alternating current impedance methodsElectrochemical internal resistance RctAnd internal diffusion resistance RwAnd determining the corresponding RsInitial value, RctInitial value and RwAn initial value; respectively obtaining R of different SOC under n times of cyclic charge-discharge statesCycle value, RctCycle value and RwThe cycle value is determined and DeltaR is determined based on its corresponding initial values、△RctAnd Δ Rw。
Optionally, the method further includes:
an influence factor adding module used for inputting influence factors a, b, c and d into the risk prediction model for learning according to historical empirical data, wherein a is QPermanent loss ofB is Q as an influence factor of thermal runaway and internal short circuitCan be recoveredAs the influence factor of thermal runaway and internal short circuit, wherein a > b. c is a radical ofCan recoverD is Q as an influence factor of overcharge and overdischargePermanent lossAs a factor influencing overcharge and overdischarge, wherein c > d.
Optionally, the sample further comprises: historical risk probability data for various types of energy storage batteries.
EXAMPLE III
Embodiments of the present application also provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform:
determining recoverable loss capacity Q according to functional relationship between recoverable loss resistance and permanent loss resistance and loss capacity corresponding to the recoverable loss resistanceCan be recoveredAnd permanent loss capacity QPermanent loss；
The Q is addedCan be recoveredAnd QPermanent lossAs a sample, a risk prediction model is trained to obtain the occurrence probability and tolerance of various risks of the energy storage battery, wherein the wind is generatedThe risk includes: thermal runaway, internal short circuit, overcharge, and overdischarge.
Storage medium-any of various types of memory devices or storage devices. The term "storage medium" is intended to include: mounting media such as CD-ROM, floppy disk, or tape devices; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Lanbas (Rambus) RAM, etc.; non-volatile memory, such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in the computer system in which the program is executed, or may be located in a different second computer system connected to the computer system through a network (such as the internet). The second computer system may provide the program instructions to the computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations, such as in different computer systems that are connected by a network. The storage medium may store program instructions (e.g., embodied as a computer program) that are executable by one or more processors.
Of course, the storage medium provided in the embodiments of the present application includes computer-executable instructions, where the computer-executable instructions are not limited to the energy storage battery risk prediction operation described above, and may also perform related operations in the energy storage battery risk prediction method provided in any embodiment of the present application.
Example four
The embodiment of the application provides electronic equipment, and the energy storage battery risk prediction device provided by the embodiment of the application can be integrated in the electronic equipment. Fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present application. As shown in fig. 4, the present embodiment provides an electronic device 400, which includes: one or more processors 420; storage 410 to store one or more programs that, when executed by the one or more processors 420, cause the one or more processors 420 to implement:
determining recoverable loss capacity Q according to functional relationship between recoverable loss resistance and permanent loss resistance and loss capacity corresponding to the recoverable loss resistanceCan be recoveredAnd permanent loss capacity QPermanent loss；
The Q is addedCan be recoveredAnd QPermanent lossAs a sample, training a risk prediction model to obtain occurrence probability and tolerance of various risks of the energy storage battery, wherein the risks include: thermal runaway, internal short circuit, overcharge, and overdischarge.
As shown in fig. 4, the electronic device 400 includes a processor 420, a storage device 410, an input device 430, and an output device 440; the number of the processors 420 in the electronic device may be one or more, and one processor 420 is taken as an example in fig. 4; the processor 420, the storage device 410, the input device 430, and the output device 440 in the electronic apparatus may be connected by a bus or other means, and are exemplified by a bus 450 in fig. 4.
The storage device 410 is a computer-readable storage medium, and can be used to store software programs, computer executable programs, and module units, such as program instructions corresponding to the energy storage battery risk prediction method in the embodiment of the present application.
The storage device 410 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 required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the storage 410 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, storage 410 may further include memory located remotely from processor 420, which may be connected via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input means 430 may be used to receive input numbers, character information, or voice information, and to generate key signal inputs related to user settings and function control of the electronic device. The output device 440 may include a display screen, speakers, etc.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method for predicting risk of an energy storage battery is characterized by comprising the following steps:
determining recoverable loss capacity Q according to functional relationship between recoverable loss resistance and permanent loss resistance and loss capacity corresponding to the recoverable loss resistanceCan be recoveredAnd permanent loss capacity QPermanent loss；
The Q is addedCan be recoveredAnd QPermanent lossAs a sample, training a risk prediction model to obtain occurrence probability and tolerance of various risks of the energy storage battery, wherein the risks include: thermal runaway, internal short circuit, overcharge, and overdischarge.
2. The method of claim 1, wherein Q is determined as a function of recoverable loss resistance, permanent loss resistance, and their corresponding loss capacitiesCan be recoveredAnd QPermanent lossThe calculation formula of (c) is:
wherein R isPermanent loss ofFor permanent loss of resistance, RRecoverable lossTo restore the loss resistance, RTotal loss of DCAdding a value Delta R to the DC internal resistance DCRDC，QTotal lossIs the total lost capacity value.
3. The method of claim 2, wherein RPermanent loss ofAnd RRecoverable lossWhen the battery is charged and discharged for n times in a circulating mode, the battery is calculated based on data results of a direct current internal resistance test and an alternating current impedance test and the following formula:
△RDC＝Rpermanent loss+RRecoverable loss(3)；
RPermanent loss＝(△Rs+α△Rct)/(△Rs+△Rct+△Rw)*△RDC(4)；
RRecoverable loss＝(△Rw+β△Rct)/(△Rs+△Rct+△Rw)*△RDC(5)；
Wherein, Δ RsIncrease the ohmic internal resistance value,. DELTA.RctIncrease in electrochemical internal resistance, and Δ RwFor increasing the diffusion internal resistance, wherein alpha + beta is 1, and alpha and beta are respectively delta RctThe distribution coefficient under the conditions of permanent loss internal resistance and recoverable loss internal resistance.
4. The method according to claim 3, wherein the data result processing procedure of the DC internal resistance test is as follows:
acquiring direct current resistance DCR data of different SOC (state of charge) respectively in a charge-discharge state, and constructing a DCR-SOC fitting curve;
according to the DCR-SOC fitting curve, obtaining DCR data and carrying out normalization processing as a DCR initial value;
acquiring DCR cycle values of different SOC respectively under n-time cyclic charge-discharge states, and determining Delta R based on the DCR initial valuesDC。
5. The method of claim 4, wherein the data result processing procedure of the AC impedance test is as follows:
respectively obtaining ohmic internal resistances R of different SOC in charging and discharging states by an alternating current impedance methodsElectrochemical internal resistance RctAnd internal diffusion resistance RwAnd determining the corresponding RsInitial value, RctInitial value and RwAn initial value;
respectively obtaining R of different SOC under n times of cyclic charge-discharge statesCycle value, RctCycle value and RwThe cycle value is determined and DeltaR is determined based on its corresponding initial values、△RctAnd Δ Rw。
6. The method of claim 5, further comprising:
according to historical empirical data, inputting influence factors a, b, c and d into the risk prediction model for learning, wherein a is QPermanent lossB is Q as an influence factor of thermal runaway and internal short circuitCan be recoveredAs the influence factor of thermal runaway and internal short circuit, wherein a > b. c is a radical ofCan be recoveredD is Q as an influence factor of overcharge and overdischargePermanent lossAs a factor influencing overcharge and overdischarge, wherein c > d.
7. The method of claim 6, wherein the sampling further comprises: historical risk probability data for various types of energy storage batteries.
8. An energy storage battery risk prediction apparatus, comprising:
determining recoverable loss capacity Q according to functional relationship between recoverable loss resistance and permanent loss resistance and loss capacity corresponding to the recoverable loss resistanceCan be recoveredAnd permanent loss capacity QPermanent loss；
The Q is addedCan be recoveredAnd QPermanent lossAs a sample, training a risk prediction model to obtain occurrence probability and tolerance of various risks of the energy storage battery, wherein the risks include: thermal runaway, internal short circuit, overcharge, and overdischarge.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the energy storage battery risk prediction method according to any one of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the energy storage battery risk prediction method according to any one of claims 1-7 when executing the computer program.
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| CN117709273B (en) * | 2024-02-05 | 2024-06-07 | 宁德时代新能源科技股份有限公司 | Battery risk prediction method, device, computer equipment and storage medium |
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