CN115906370A - Model generation method, and lithium ion battery storage life prediction method and system - Google Patents
Model generation method, and lithium ion battery storage life prediction method and system Download PDFInfo
- Publication number
- CN115906370A CN115906370A CN202111113358.1A CN202111113358A CN115906370A CN 115906370 A CN115906370 A CN 115906370A CN 202111113358 A CN202111113358 A CN 202111113358A CN 115906370 A CN115906370 A CN 115906370A
- Authority
- CN
- China
- Prior art keywords
- storage
- lithium ion
- battery
- ion battery
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 title claims abstract description 263
- 229910001416 lithium ion Inorganic materials 0.000 title claims abstract description 263
- 238000000034 method Methods 0.000 title claims abstract description 48
- 238000005562 fading Methods 0.000 claims abstract description 94
- 238000002474 experimental method Methods 0.000 claims abstract description 49
- 238000012360 testing method Methods 0.000 claims description 34
- 238000012545 processing Methods 0.000 claims description 27
- 238000004590 computer program Methods 0.000 claims description 13
- 230000002159 abnormal effect Effects 0.000 claims description 6
- 230000002238 attenuated effect Effects 0.000 claims description 5
- 238000007781 pre-processing Methods 0.000 claims description 5
- 239000013589 supplement Substances 0.000 claims description 5
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 claims description 3
- 229910052744 lithium Inorganic materials 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 9
- 230000006870 function Effects 0.000 description 7
- 238000010586 diagram Methods 0.000 description 5
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000003491 array Methods 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000004146 energy storage Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000003446 memory effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 239000000047 product Substances 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Landscapes
- Secondary Cells (AREA)
Abstract
The invention discloses a model generation method, a lithium ion battery storage life prediction method and a lithium ion battery storage life prediction system, wherein the generation method comprises the following steps: establishing a capacity attenuation model of the lithium ion battery; acquiring storage experiment data of a lithium ion battery with a specific battery type; and determining unknown model parameters of the capacity fading model of the lithium ion battery according to the storage experiment data so as to obtain the storage life capacity fading model of the lithium ion battery with a specific battery type. The lithium ion battery capacity attenuation model has better universality by considering the influence of storage temperature and storage charge state, and the exponential term of storage time in the lithium ion battery capacity attenuation model is associated with temperature, and particularly the inverse power law form of temperature is adopted, so that the influence of temperature on the storage life of the lithium ion battery can be reflected, the fitting effect is better, the storage life of the lithium ion battery of the same type at a certain storage temperature and in the storage charge state can be accurately predicted, and the prediction accuracy is improved.
Description
Technical Field
The invention relates to the technical field of battery storage life prediction, in particular to a model generation method, a lithium ion battery storage life prediction method and a lithium ion battery storage life prediction system.
Background
The lithium ion battery has the advantages of high working voltage, high energy density, no memory effect, low self-discharge rate and the like, is widely applied to portable electronic products, and is widely applied to the fields of electric automobiles and energy storage in recent years.
In the energy storage field, the storage life of a battery is of primary concern to users, and typically the end of the storage life of a battery is defined as 80% of its initial capacity. However, the test period of the storage life of the battery is very long, and how to predict the storage life of the battery quickly and accurately is an important issue for basic research of lithium ion batteries.
At present, the method of experimental test, mathematical fitting and combination of the two is mainly adopted for predicting the storage life of the lithium ion battery. Most storage life models use a power function to establish the relationship between the capacity decay rate and the storage time, wherein the exponential term of the power function is a fitting coefficient. However, most battery systems have relatively sensitive capacity fading to temperature, and the exponential term only adopts one fitting coefficient, so that the defects of unobvious capacity fading, poor fitting effect and low prediction accuracy exist.
Disclosure of Invention
The invention aims to overcome the defects of unobvious capacity attenuation, poor fitting effect and low prediction accuracy of a lithium ion battery storage life prediction method in the prior art, and provides a model generation method, a lithium ion battery storage life prediction method and a lithium ion battery storage life prediction system.
The invention solves the technical problems through the following technical scheme:
the first aspect of the present invention provides a model generation method, including:
the capacity attenuation model of the lithium ion battery is established by adopting the following formula:
wherein Q is loss Expressing a capacity decay rate, T expressing a storage temperature, SOC expressing a storage charge state, T expressing storage days, and alpha, beta, gamma, lambda and k are unknown model parameters of a capacity decay model of the lithium ion battery;
acquiring storage experiment data of a lithium ion battery with a specific battery type;
and determining unknown model parameters of the capacity fading model of the lithium ion battery according to the storage experiment data so as to obtain a storage life capacity fading model of the lithium ion battery with the specific battery type.
Preferably, the step of determining unknown model parameters of the capacity fading model of the lithium ion battery according to the storage experimental data to obtain the storage life capacity fading model of the lithium ion battery of the specific battery type includes:
s1, determining a battery storage temperature range of the lithium ion battery with the specific battery type, selecting the lowest storage temperature, the highest storage temperature and at least one other storage temperature in the battery storage temperature range, selecting a plurality of storage charge states at each storage temperature, and setting a storage period;
s2, determining storage experiment conditions according to the specific battery type and the battery storage temperature range;
s3, selecting a new lithium ion battery which is consistent with the initial state of the lithium ion battery with the specific battery type as an experimental battery;
s4, carrying out constant volume test on the experimental battery under the storage experimental condition, and recording storage experimental data;
s5, adjusting the experimental battery to a storage charge state set for the experimental battery through power supplement;
s6, placing the experimental battery into a thermostat set for the experimental battery, and storing a set storage period;
s7, taking the experimental battery out of the thermostat, and ensuring that the experimental battery is recovered to room temperature;
repeating the steps S4-S7 until the capacity of the experimental battery is attenuated to a preset capacity threshold, and then executing a step S8;
and S8, processing the storage experiment data, and determining unknown model parameters of the capacity fading model of the lithium ion battery according to the processed storage experiment data so as to obtain the storage life capacity fading model of the lithium ion battery with the specific battery type.
Preferably, the step of processing the storage experimental data, and determining an unknown model parameter of the capacity fading model of the lithium ion battery according to the processed storage experimental data to obtain the storage life capacity fading model of the lithium ion battery with the specific battery type includes:
preprocessing the stored experimental data and eliminating abnormal data;
calculating the capacity fading rate of the experimental battery after the ith storage period by adopting the following formula:
wherein Q is i Represents the discharge capacity, Q, of the experimental cell after i cycles of storage 0 Represents the initial capacity of the experimental battery, 1 is a natural number, Q iloss Representing the capacity decay rate of the experimental battery after storing i periods;
storing the number of days t corresponding to the capacity fade rate of each of the experimental batteries i Storage temperature T i And storing the state of charge SOC i Fitting according to the capacity attenuation model of the lithium ion battery to determine the primary values of the unknown model parameters alpha, beta, gamma, lambda and k of the capacity attenuation model of the lithium ion battery and the primary capacity attenuation model of the lithium ion battery;
calculating the capacity attenuation rate of the experimental battery by using the preliminary capacity attenuation model of the lithium ion battery, comparing the capacity attenuation rate with real stored experimental data to optimize the value of the unknown model parameter of each capacity attenuation model of the lithium ion battery, and determining the final value of the unknown model parameter alpha, beta, gamma, lambda and k of the capacity attenuation model of the lithium ion battery to obtain the storage life capacity attenuation model of the lithium ion battery with the specific battery type;
and/or the presence of a gas in the atmosphere,
the battery storage temperature range comprises the normal storage temperature and the storage temperature threshold of the lithium ion battery;
and/or the presence of a gas in the atmosphere,
the storage experiment data includes at least one of a capacity fade rate, a storage temperature, a storage state of charge, and a storage number of days of the lithium ion battery of the specific battery type.
The invention provides a model generation system in a second aspect, which comprises an establishing module, an obtaining module and a determining module;
the establishing module is used for establishing a capacity attenuation model of the lithium ion battery by adopting the following formula:
wherein Q is loss Expressing a capacity decay rate, T expressing a storage temperature, SOC expressing a storage charge state, T expressing storage days, and alpha, beta, gamma, lambda and k being unknown model parameters of a capacity decay model of the lithium ion battery;
the acquisition module is used for acquiring the storage experiment data of the lithium ion battery with a specific battery type;
and the determining module is used for determining unknown model parameters of the capacity fading model of the lithium ion battery according to the storage experiment data so as to obtain the storage life capacity fading model of the lithium ion battery with the specific battery type.
Preferably, the determining module comprises a first determining unit, a second determining unit, a selecting unit, a testing unit and a processing unit;
the first determining unit is used for determining a battery storage temperature range of the lithium ion battery with the specific battery type, selecting the lowest storage temperature, the highest storage temperature and at least one other storage temperature in the battery storage temperature range, selecting a plurality of storage charge states at each storage temperature, and setting a storage period;
the second determining unit is used for determining storage experiment conditions according to the specific battery type and the battery storage temperature range;
the selection unit is used for selecting a new lithium ion battery which is consistent with the initial state of the lithium ion battery with the specific battery type as an experimental battery;
the testing unit is used for repeatedly executing testing operation until the capacity of the experimental battery is attenuated to a preset capacity threshold value, and then calling the processing unit, wherein the testing operation comprises the following steps: carrying out constant volume test on the experimental battery under the storage experimental condition, and recording storage experimental data; adjusting the experimental battery to a storage charge state set for the experimental battery experiment through power supplement; putting the experimental battery into a thermostat set for the experimental battery, and storing for a set storage period; taking the experimental battery out of the incubator, and ensuring that the experimental battery is recovered to room temperature;
and the processing unit is used for processing the storage experiment data, and determining unknown model parameters of the capacity fading model of the lithium ion battery according to the processed storage experiment data so as to obtain the storage life capacity fading model of the lithium ion battery with the specific battery type.
Preferably, the processing unit comprises a processing subunit, a first calculating subunit, a fitting subunit and a second calculating subunit;
the processing subunit is used for preprocessing the stored experimental data and eliminating abnormal data;
the first calculating subunit is configured to calculate a capacity fading rate of the experimental battery after the ith storage period by using the following formula:
wherein Q is i Represents the discharge capacity, Q, of the experimental cell after i cycles of storage 0 Represents the initial capacity of the experimental battery, 1 is a natural number, Q iloss Representing the capacity fading rate of the experimental battery after storing i periods;
the fitting subunit is used for storing the storage days t corresponding to the capacity decay rate of each experimental battery i Storage temperature T i And storing the state of charge SOC i Fitting according to the capacity attenuation model of the lithium ion battery to determine the primary values of the unknown model parameters alpha, beta, gamma, lambda and k of the capacity attenuation model of the lithium ion battery and the primary capacity attenuation model of the lithium ion battery;
the second calculating subunit is configured to calculate a capacity fading rate of the experimental battery by using the preliminary capacity fading model of the lithium ion battery, compare the capacity fading rate with real stored experimental data, optimize a value of an unknown model parameter of each capacity fading model of the lithium ion battery, and determine a final value of an unknown model parameter α, β, γ, λ, k of the capacity fading model of the lithium ion battery, so as to obtain a storage life capacity fading model of the lithium ion battery of the specific battery type;
and/or the presence of a gas in the atmosphere,
the battery storage temperature range comprises the normal storage temperature and the storage temperature threshold of the lithium ion battery;
and/or the presence of a gas in the gas,
the storage experiment data comprises at least one of the capacity fade rate, the storage temperature, the storage state of charge and the storage days of the lithium ion battery of the specific battery type.
The third aspect of the present invention provides a method for predicting the storage life of a lithium ion battery, where the method includes:
setting a preset condition;
predicting the storage life of the lithium ion batteries of the same type under a preset condition by using a storage life capacity attenuation model of the lithium ion batteries of the specific battery type;
wherein the storage life capacity decay model of the lithium ion battery of the specific battery type is obtained by using the model generation method of the first aspect.
Preferably, the preset conditions include a set storage temperature and a set storage state of charge.
The invention provides a system for predicting the storage life of a lithium ion battery, which comprises a setting module and a predicting module;
the setting module is used for setting preset conditions;
the prediction module is used for predicting the storage life of the lithium ion batteries of the same type under a preset condition by using the storage life capacity attenuation model of the lithium ion batteries of the specific battery type;
wherein the storage life capacity decay model of the lithium ion battery of the specific battery type is obtained by using the model generation system of the second aspect.
Preferably, the preset conditions include a set storage temperature and a set storage state of charge.
A fifth aspect of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the model generation method according to the first aspect, or to implement the method for predicting the storage life of a lithium ion battery according to the third aspect.
A sixth aspect of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, implements the model generation method according to the first aspect or the method for predicting the storage life of a lithium ion battery according to the third aspect.
On the basis of the common knowledge in the field, the preferred conditions can be combined randomly to obtain the preferred embodiments of the invention.
The positive progress effects of the invention are as follows:
the lithium ion battery capacity attenuation model has better universality by considering the influence of storage temperature and storage charge state, and the exponential term of storage time in the lithium ion battery capacity attenuation model is associated with the temperature, and particularly adopts the inverse power law form of the temperature, so that the influence of the temperature on the storage life of the lithium ion battery can be reflected, the fitting effect is better, the storage life of the lithium ion batteries of the same type at a certain storage temperature and a certain storage charge state can be accurately predicted, and the prediction accuracy is improved.
Drawings
Fig. 1 is a flowchart of a model generation method according to embodiment 1 of the present invention.
Fig. 2 is a flowchart of step 103 of the model generation method according to embodiment 1 of the present invention.
Fig. 3 is a flowchart of step 1035 of the model generation method according to embodiment 1 of the present invention.
Fig. 4 is a schematic block diagram of a model generation system according to embodiment 2 of the present invention.
Fig. 5 is a schematic structural diagram of an electronic device according to embodiment 3 of the present invention.
Fig. 6 is a flowchart of a method for predicting the storage life of a lithium ion battery according to embodiment 5 of the present invention.
FIG. 7 is a graph comparing a curve fitted to a capacity-decay model of a lithium ion battery cell having 30% SOC at 25 ℃ in example 5 of the present invention with a real experimental data curve.
FIG. 8 is a graph comparing a curve fitted to a capacity-decay model of a lithium ion battery cell having an SOC of 100% at 25 ℃ in example 5 of the present invention with a real experimental data curve.
FIG. 9 is a graph comparing a curve fitted to a capacity-decay model of a lithium ion battery cell having 30% SOC at 45 ℃ in example 5 of the present invention with a real experimental data curve.
FIG. 10 is a graph comparing a curve fitted to a capacity-decay model of a lithium ion battery cell having an SOC of 100% at 45 ℃ in example 5 of the present invention with a real experimental data curve.
Fig. 11 is a schematic block diagram of a system for predicting the storage life of a lithium ion battery according to embodiment 6 of the present invention.
Detailed Description
The invention is further illustrated by the following examples, which are not intended to limit the invention thereto.
Example 1
As shown in fig. 1, the present embodiment provides a model generation method, including:
wherein Q is loss Expressing a capacity decay rate, T expressing a storage temperature, SOC expressing a storage charge state, T expressing storage days, and alpha, beta, gamma, lambda and k being unknown model parameters of a capacity decay model of the lithium ion battery;
in the embodiment, since most of the capacity fading of the battery system is sensitive to the temperature, the capacity fading model of the lithium ion battery established by the formula associates the exponential term of the storage time with the temperature, and specifically adopts the inverse power law form of the temperature, so that the influence of the temperature on the storage life can be reflected, the fitting effect is better, and the prediction accuracy is higher. Particularly, the storage life of a battery system with capacity attenuation sensitive to temperature is more accurately predicted, so that the storage life of the same type of battery at a certain storage temperature and a certain SOC (storage charge state) can be more accurately predicted, technical support and theoretical basis are provided for replacement of the lithium ion battery when the lithium ion battery reaches the end of the life, the test cost can be reduced, the test period can be shortened, and the experimental resources can be saved.
102, acquiring storage experiment data of a lithium ion battery with a specific battery type;
in this embodiment, the stored experimental data includes at least one of a capacity fade rate, a storage temperature, a storage state of charge, and a storage number of days of the lithium ion battery of the specific battery type.
And 103, determining unknown model parameters of the capacity fading model of the lithium ion battery according to the storage experiment data to obtain a storage life capacity fading model of the lithium ion battery with a specific battery type.
In this embodiment, the unknown model parameters α, β, γ, λ, and k of the capacity fading model of the lithium ion battery are determined based on the capacity fading rate, the storage temperature, the storage state of charge, and the storage days of the lithium ion battery of the specific battery type in combination with the above formula of the capacity fading model of the lithium ion battery, so as to obtain the storage life capacity fading model of the lithium ion battery of the specific battery type.
It should be noted that the model parameters of the established capacity fading model of the lithium ion battery are unknown, and when the model parameters of the capacity fading model of the lithium ion battery have specific values, the model parameters are the storage life capacity fading model of the lithium ion battery of the specific battery type.
In one embodiment, as shown in FIG. 2, step 103 comprises:
in this embodiment, the at least one other storage temperature includes a plurality of normal storage temperatures, for example, the number of the normal storage temperatures may be not less than 2, and specifically, the plurality of normal storage temperatures may be selected according to a battery type and an application scenario, where the plurality of normal storage temperatures, a lowest storage temperature, and a highest storage temperature may be distributed in an arithmetic progression, and the plurality of normal storage temperatures may also be selected according to an actual storage temperature probability of the lithium battery.
In this embodiment, the battery storage temperature range includes a normal storage temperature and a storage temperature threshold of the lithium ion battery. Wherein the storage temperature threshold comprises a lowest storage temperature and a highest storage temperature.
It should be noted that, in general, the normal storage temperature of the lithium ion battery is 25 ℃, and the storage temperature threshold is set to other values according to actual situations.
The storage period is set to be one month, and may be set to other values according to actual conditions. And is not particularly limited herein.
in this embodiment, different battery types correspond to different experimental conditions, and storage test is performed on a certain type of lithium ion battery under different experimental conditions, so that storage experimental data of the type of lithium ion battery can be obtained.
in this embodiment, the initial state includes an initial voltage and an internal resistance.
It should be noted that the new lithium ion battery is a completely new battery after formation and capacity grading, and the initial voltage and the internal resistance of the new lithium ion battery in the initial state are the same, so that the accuracy and the stability of the prediction result can be further improved.
1034, performing constant volume test on the experimental battery under the storage experiment condition, and recording the storage experiment data;
in this embodiment, the storage experiment condition for performing the constant volume test on the experimental battery is preferably 25 ℃, that is, the constant volume test on the experimental battery is preferably performed at 25 ℃, and other temperatures may be selected as the storage experiment condition according to the actual situation, which is not specifically limited herein.
in this embodiment, the preset capacity threshold may be in a range of 70% to 80% of the initial capacity, and preferably 80% of the initial capacity.
And 1038, processing the stored experimental data, and determining unknown model parameters of the capacity fading model of the lithium ion battery according to the processed stored experimental data so as to obtain a storage life capacity fading model of the lithium ion battery with a specific battery type.
In one possible implementation, as shown in fig. 3, step 1038 includes:
wherein Q is i Represents the discharge capacity, Q, of the experimental cell after i cycles of storage 0 Represents the initial capacity of the experimental battery, 1 is a natural number, Q iloss Representing the capacity fading rate of the experimental battery after storing i periods;
10383 storing days t corresponding to the capacity fade rate of each experimental battery i Storage temperature T i And storing the state of charge SOC i Fitting according to the capacity fading model of the lithium ion battery to determine the primary values of the unknown model parameters alpha, beta, gamma, lambda and k of the capacity fading model of the lithium ion battery and the primary capacity fading model of the lithium ion battery;
In this embodiment, the optimizing the values of the unknown model parameters of the capacity fading models of the lithium ion batteries in step 10384 may further include: and finely adjusting the value of each unknown model parameter to ensure that the goodness-of-fit difference value of the fitting curve of each experimental battery and the real experimental data curve is within a preset threshold range, so that the universality and the accuracy of the storage life capacity decay model of the lithium ion battery can be further improved, wherein the preset threshold range of the goodness-of-fit difference value can be further reduced to ensure that the finally determined storage life capacity decay model of the lithium ion battery with the specific battery type is more accurate. It should be noted that the preset threshold range is set according to actual situations, and is not specifically limited herein.
The lithium ion battery capacity attenuation model adopted by the embodiment considers the influence of storage temperature and storage charge state, has better universality, the exponential term of storage time in the lithium ion battery capacity attenuation model is associated with the temperature, and the inverse power law form of the temperature is specifically adopted, so that the influence of the temperature on the storage life of the lithium ion battery can be reflected, the fitting effect is better, the storage life of the lithium ion battery of the same type at a certain storage temperature and a certain storage charge state can be accurately predicted, and the prediction accuracy is improved.
Example 2
As shown in fig. 4, the present embodiment provides a model generation system, which includes an establishing module 1, an obtaining module 2, and a determining module 3;
the establishing module 1 is used for establishing a capacity fading model of the lithium ion battery by adopting the following formula:
wherein Q is loss Represents the capacity decay rate, T represents the storage temperature, SOC represents the storage charge state, T represents the storage days, and alpha, beta, gamma, lambda and k are all the non-capacity decay models of the lithium ion batteryKnowing model parameters;
in the embodiment, since most of the capacity fading of the battery system is sensitive to the temperature, the capacity fading model of the lithium ion battery established by the formula associates the exponential term of the storage time with the temperature, and specifically adopts the inverse power law form of the temperature, so that the influence of the temperature on the storage life can be reflected, the fitting effect is better, and the prediction accuracy is higher. Particularly, the storage life of a battery system with capacity attenuation sensitive to temperature is more accurately predicted, so that the storage life of the same type of battery at a certain storage temperature and under a certain SOC can be more accurately predicted, technical support and theoretical basis are provided for replacement of the lithium ion battery when the lithium ion battery reaches the end of the life, the test cost can be reduced, the test period can be shortened, and the experiment resources can be saved.
The acquisition module 2 is used for acquiring storage experiment data of the lithium ion battery with a specific battery type;
in this embodiment, the storage experiment data includes at least one of a capacity fade rate, a storage temperature, a storage state of charge, and a storage number of days of the lithium ion battery of the specific battery type.
And the determining module 3 is used for determining unknown model parameters of the capacity fading model of the lithium ion battery according to the storage experiment data so as to obtain the storage life capacity fading model of the lithium ion battery with the specific battery type.
In this embodiment, the unknown model parameters α, β, γ, λ, and k of the capacity fading model of the lithium ion battery are determined based on the capacity fading rate, the storage temperature, the storage state of charge, and the storage days of the lithium ion battery of the specific battery type in combination with the above formula of the capacity fading model of the lithium ion battery, so as to obtain the storage life capacity fading model of the lithium ion battery of the specific battery type.
It should be noted that the model parameters of the established capacity fading model of the lithium ion battery are unknown, and when the model parameters of the capacity fading model of the lithium ion battery have specific values, the model parameters are the storage life capacity fading model of the lithium ion battery of the specific battery type.
In an implementable scenario, as shown in fig. 4, the determination module 3 includes a first determination unit 311, a second determination unit 312, a selection unit 313, a test unit 314, and a processing unit 315;
the first determining unit 311 is configured to determine a battery storage temperature range of a lithium ion battery of a specific battery type, select a lowest storage temperature, a highest storage temperature, and at least one other storage temperature within the battery storage temperature range, select a plurality of storage states of charge at each storage temperature, and set a storage period;
in this embodiment, the other at least one storage temperature includes a plurality of normal storage temperatures, for example, the number of the normal storage temperatures may be not less than 2, and specifically, the plurality of normal storage temperatures may be selected according to a battery type and an application scenario, where the plurality of normal storage temperatures, the lowest storage temperature and the highest storage temperature may be distributed in an arithmetic progression, and the plurality of normal storage temperatures may also be selected according to an actual storage temperature probability of the lithium battery.
In this embodiment, the battery storage temperature range includes a normal storage temperature and a storage temperature threshold of the lithium ion battery. Wherein the storage temperature threshold comprises a lowest storage temperature and a highest storage temperature.
It should be noted that, in general, the normal storage temperature of the lithium ion battery is 25 ℃, and the storage temperature threshold is set to other values according to actual situations.
The storage period is set to be one month, and may be set to other values according to actual conditions. And is not particularly limited herein.
A second determination unit 312 for determining a storage experiment condition according to the specific battery type and the battery storage temperature range;
in this embodiment, different battery types correspond to different experimental conditions, and storage test is performed on a certain type of lithium ion battery under different experimental conditions, so that storage experimental data of the type of lithium ion battery can be obtained.
A selecting unit 313 for selecting a new lithium ion battery as an experimental battery, the new lithium ion battery being in accordance with the initial state of the lithium ion battery of the specific battery type;
in this embodiment, the initial state includes an initial voltage and an internal resistance.
It should be noted that the new lithium ion battery is a completely new battery after formation and capacity grading, and the initial voltage and the internal resistance of the new lithium ion battery in the initial state are the same, so that the accuracy and the stability of the prediction result can be further improved.
A testing unit 314, configured to repeatedly perform a testing operation until the capacity of the experimental battery decays to a preset capacity threshold, and then invoke a processing unit 315, where the testing operation includes: carrying out constant volume test on the experimental battery under the condition of a storage experiment, and recording storage experiment data; adjusting the experimental battery to a storage charge state set for the experimental battery through power supplement; putting the experimental battery into a thermostat set for the experiment of the experimental battery, and storing a set storage period; taking the experimental battery out of the thermostat, and ensuring that the experimental battery is recovered to room temperature;
in this embodiment, the storage experiment condition for performing the constant volume test on the experimental battery is preferably 25 ℃, that is, the constant volume test on the experimental battery is preferably performed at 25 ℃, and other temperatures may be selected as the storage experiment condition according to the actual situation, which is not specifically limited herein.
In this embodiment, the preset capacity threshold may be in a range of 70% to 80% of the initial capacity, and preferably 80% of the initial capacity.
And the processing unit 315 is configured to process the storage experiment data, and determine an unknown model parameter of the capacity fading model of the lithium ion battery according to the processed storage experiment data, so as to obtain a storage life capacity fading model of the lithium ion battery of the specific battery type.
In an implementable approach, as shown in fig. 4, the processing unit 315 includes a processing subunit 3151, a first computation subunit 3152, a fitting subunit 3153, and a second computation subunit 3154;
the processing subunit 3151 is configured to preprocess the stored experimental data and reject abnormal data;
a first calculating subunit 3152, configured to calculate a capacity fading rate of the experimental battery after the ith storage period by using the following formula:
wherein Q i Represents the discharge capacity, Q, of the experimental cell after i cycles of storage 0 Represents the initial capacity of the experimental battery, 1 is a natural number, Q iloss Representing the capacity fading rate of the experimental battery after storing i periods;
a fitting subunit 3153 for storing the number of days t of storage corresponding to the rate of capacity fade of each experimental battery i Storage temperature T i And storing the state of charge SOC i Fitting according to the capacity fading model of the lithium ion battery to determine the primary values of the unknown model parameters alpha, beta, gamma, lambda and k of the capacity fading model of the lithium ion battery and the primary capacity fading model of the lithium ion battery;
and the second calculating subunit 3154 is configured to calculate a capacity fading rate of the experimental battery by using the preliminary capacity fading model of the lithium ion battery, and compare the calculated capacity fading rate with real stored experimental data to optimize the value of the unknown model parameter of the capacity fading model of each lithium ion battery, determine the final value of the unknown model parameter α, β, γ, λ, k of the capacity fading model of the lithium ion battery, and obtain a storage life capacity fading model of the lithium ion battery of a specific battery type. Therefore, the universality and the accuracy of the storage life capacity decay model of the lithium ion battery can be improved.
In this embodiment, the optimizing the values of the unknown model parameters of the capacity fading model of each lithium ion battery may further include: and finely adjusting the value of each unknown model parameter to ensure that the goodness-of-fit difference value of the fitting curve of each experimental battery and the real experimental data curve is within a preset threshold range, so that the universality and the accuracy of the storage life capacity decay model of the lithium ion battery can be further improved, wherein the preset threshold range of the goodness-of-fit difference value can be further reduced to ensure that the finally determined storage life capacity decay model of the lithium ion battery with the specific battery type is more accurate. It should be noted that the preset threshold range is set according to actual situations, and is not specifically limited here.
The lithium ion battery capacity attenuation model adopted by the embodiment considers the influence of storage temperature and storage charge state, has better universality, the exponential term of storage time in the lithium ion battery capacity attenuation model is associated with the temperature, and the inverse power law form of the temperature is specifically adopted, so that the influence of the temperature on the storage life of the lithium ion battery can be reflected, the fitting effect is better, the storage life of the lithium ion battery of the same type at a certain storage temperature and a certain storage charge state can be accurately predicted, and the prediction accuracy is improved.
Example 3
Fig. 5 is a schematic structural diagram of an electronic device according to embodiment 3 of the present invention. The electronic device comprises a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the model generation method of embodiment 1 when executing the program. The electronic device 30 shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 5, the electronic device 30 may be embodied in the form of a general purpose computing device, which may be, for example, a server device. The components of the electronic device 30 may include, but are not limited to: the at least one processor 31, the at least one memory 32, and a bus 33 connecting the various system components (including the memory 32 and the processor 31).
The bus 33 includes a data bus, an address bus, and a control bus.
The memory 32 may include volatile memory, such as Random Access Memory (RAM) 321 and/or cache memory 322, and may further include Read Only Memory (ROM) 323.
The processor 31 executes various functional applications and data processing, such as the model generation method of embodiment 1 of the present invention, by executing the computer program stored in the memory 32.
The electronic device 30 may also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). Such communication may be through input/output (I/O) interfaces 35. Also, model-generating device 30 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via network adapter 36. As shown in FIG. 5, network adapter 36 communicates with the other modules of model-generating device 30 via bus 33. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the model-generating device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, to name a few.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of the electronic device are mentioned, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the units/modules described above may be embodied in one unit/module according to embodiments of the invention. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.
Example 4
The present embodiment provides a computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the model generation method provided in embodiment 1.
More specific examples that may be employed by the readable storage medium include, but are not limited to: a portable disk, hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible implementation, the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the method for generating a model as described in embodiment 1 when the program product is run on the terminal device.
Where program code for carrying out the invention is written in any combination of one or more programming languages, the program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on a remote device or entirely on the remote device.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Example 5
As shown in fig. 6, the present embodiment provides a method for predicting the storage life of a lithium ion battery, where the method includes:
in this embodiment, the preset conditions include a set storage temperature and a set storage state of charge.
wherein the storage life capacity decay model of the lithium ion battery of the specific battery type is obtained by using the model generation method of embodiment 1.
It should be noted that, the present embodiment can predict the storage life of the lithium ion battery at various storage temperatures and storage states of charge.
The following is illustrated with reference to specific examples:
for example, the values of the unknown model parameters α, β, γ, λ, k of the capacity fading model of a lithium ion battery shown in table 1, the prediction results of the storage life of the lithium ion battery of the model shown in table 2, and the comparison between the fitted curves of the capacity fading model of the lithium ion battery of the model shown in fig. 7 to 10 of the capacity fading model of the lithium ion battery of the model shown in fig. 7, 25 ℃ 30 soc, 25 ℃ 100 soc, 45 ℃ 30 soc, 45 ℃ 100 soc and the real experimental data curves are obtained by fitting the storage experimental data of 30 soc and 100 soc of a lithium ion battery of a certain model at 25 ℃ and 45 ℃.
TABLE 1
| α | β | γ | λ | k |
| 0.003452 | -1063.18 | 0.747549 | -419.297 | 1.913164 |
TABLE 2
Based on table 1, table 2 and fig. 7 to 10, it can be known that the change of the storage temperature has a significant influence on the capacity attenuation and the storage life of the lithium ion battery, and as the capacity attenuation rate increases, the fit degree between the fitting curve of the capacity attenuation model of the lithium ion battery and the real experimental data curve becomes higher and higher, and when the capacity attenuation rate reaches a certain value, the fitting curve of the capacity attenuation model of the lithium ion battery almost completely coincides with the real experimental data curve. In fact, the storage life of the lithium ion battery mainly depends on the trend of the fitting curve at the later stage, that is, the prediction accuracy of the capacity fading model of the lithium ion battery with a specific battery type mainly depends on the goodness of fit of the fitting curve and the real experimental data curve, the higher the goodness of fit of the fitting curve and the real experimental data curve is, the higher the prediction accuracy of the capacity fading model of the lithium ion battery with the specific battery type is, and the more accurate the predicted storage life of the lithium ion battery with the same type is.
According to the embodiment, the storage life of the lithium ion batteries of the same type under a certain storage temperature and a certain storage charge state can be accurately predicted by using the storage life capacity decay model of the lithium ion batteries of the specific battery type, so that the prediction accuracy is improved, meanwhile, technical support and theoretical basis are provided for replacement of the lithium ion batteries when the lithium ion batteries reach the end of the life, the test cost is reduced, the test period is shortened, and the experimental resources are saved.
Example 6
As shown in fig. 11, the present embodiment provides a prediction system for the storage life of a lithium ion battery, which includes a setting module 61 and a prediction module 62;
a setting module 61 for setting a preset condition;
in this embodiment, the preset conditions include a set storage temperature and a set storage state of charge.
The prediction module 62 is configured to predict the storage life of lithium ion batteries of a specific battery type under a preset condition by using a storage life capacity decay model of the lithium ion batteries of the same type;
wherein the storage life capacity decay model of the lithium ion battery of the specific battery type is obtained using the model generation system of embodiment 2.
It should be noted that, the present embodiment can predict the storage life of the lithium ion battery at various storage temperatures and storage states of charge.
The following is illustrated with reference to specific examples:
for example, the equation for establishing the capacity-decay model of a lithium ion battery according to example 1 is used to fit 30% soc and 100% soc of a certain model of the lithium ion battery at 25 ℃ and 45 ℃ to obtain the values of the unknown model parameters α, β, γ, λ, k of the capacity-decay model of the lithium ion battery shown in table 1, the prediction results of the storage life of the model of the lithium ion battery shown in table 2, and a comparison graph of the fitted curve of the model of the lithium ion battery with the actual experimental data curve of the 25 ℃ 30% soc, 25 ℃ 100% soc, 45 ℃ 30% soc, 45 ℃ 100% soc shown in fig. 7-10.
TABLE 1
| α | β | γ | λ | k |
| 0.003452 | -1063.18 | 0.747549 | -419.297 | 1.913164 |
TABLE 2
Based on table 1, table 2 and fig. 7 to 10, it can be known that the change of the storage temperature has a significant influence on the capacity attenuation and the storage life of the lithium ion battery, and as the capacity attenuation rate increases, the fit degree between the fitting curve of the capacity attenuation model of the lithium ion battery and the real experimental data curve becomes higher and higher, and when the capacity attenuation rate reaches a certain value, the fitting curve of the capacity attenuation model of the lithium ion battery almost completely coincides with the real experimental data curve. In fact, the storage life of the lithium ion battery mainly depends on the trend of the fitting curve at the later stage, that is, the prediction accuracy of the capacity fading model of the lithium ion battery with a specific battery type mainly depends on the goodness of fit of the fitting curve and the real experimental data curve, the higher the goodness of fit of the fitting curve and the real experimental data curve is, the higher the prediction accuracy of the capacity fading model of the lithium ion battery with the specific battery type is, and the more accurate the predicted storage life of the lithium ion battery with the same type is.
According to the method, the storage life of the lithium ion batteries of the same type at a certain storage temperature and a certain storage charge state can be accurately predicted by using the storage life capacity attenuation model of the lithium ion batteries of the specific battery type, the prediction accuracy is improved, meanwhile, technical support and theoretical basis are provided for replacement when the lithium ion batteries reach the end of the life, the test cost is reduced, the test period is shortened, and the experiment resources are saved.
Example 7
Fig. 5 is a schematic structural diagram of an electronic device according to embodiment 3 of the present invention. The schematic structure of the electronic device in this embodiment is the same as that of fig. 5. The electronic device comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, and the processor executes the computer program to implement the method for predicting the storage life of the lithium ion battery of embodiment 5. The electronic device 30 shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 5, the electronic device 30 may be embodied in the form of a general purpose computing device, which may be, for example, a server device. The components of the electronic device 30 may include, but are not limited to: the at least one processor 31, the at least one memory 32, and a bus 33 connecting the various system components (including the memory 32 and the processor 31).
The bus 33 includes a data bus, an address bus, and a control bus.
The memory 32 may include volatile memory, such as Random Access Memory (RAM) 321 and/or cache memory 322, and may further include Read Only Memory (ROM) 323.
The processor 31 executes various functional applications and data processing, such as a method for predicting the storage life of a lithium ion battery in embodiment 5 of the present invention, by running a computer program stored in the memory 32.
The electronic device 30 may also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). Such communication may be through input/output (I/O) interfaces 35. Also, model-generating device 30 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via network adapter 36. As shown in FIG. 5, network adapter 36 communicates with the other modules of model-generating device 30 via bus 33. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the model-generating device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of the electronic device are mentioned, such a division is merely exemplary and not mandatory. Indeed, the features and functions of two or more of the units/modules described above may be embodied in one unit/module according to embodiments of the invention. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.
Example 8
The present embodiment provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the method for predicting the storage life of a lithium ion battery provided in embodiment 5 is implemented.
More specific examples, among others, that the readable storage medium may employ may include, but are not limited to: a portable disk, a hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible implementation manner, the present invention can also be implemented in the form of a program product, which includes program code for causing a terminal device to execute the method for predicting the storage life of a lithium ion battery described in implementation example 5 when the program product runs on the terminal device.
Where program code for carrying out the invention is written in any combination of one or more programming languages, the program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on a remote device or entirely on the remote device.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Claims (10)
1. A method of model generation, comprising:
the capacity attenuation model of the lithium ion battery is established by adopting the following formula:
wherein Q is loss Expressing a capacity decay rate, T expressing a storage temperature, SOC expressing a storage charge state, T expressing storage days, and alpha, beta, gamma, lambda and k being unknown model parameters of a capacity decay model of the lithium ion battery;
acquiring storage experiment data of a lithium ion battery with a specific battery type;
and determining unknown model parameters of the capacity fading model of the lithium ion battery according to the storage experiment data so as to obtain a storage life capacity fading model of the lithium ion battery with the specific battery type.
2. The model generation method of claim 1, wherein the step of determining unknown model parameters of the capacity fade model of the lithium ion battery from the stored experimental data to derive the storage life capacity fade model of the lithium ion battery of the particular battery type comprises:
s1, determining a battery storage temperature range of the lithium ion battery with the specific battery type, selecting the lowest storage temperature, the highest storage temperature and at least one other storage temperature in the battery storage temperature range, selecting a plurality of storage charge states at each storage temperature, and setting a storage period;
s2, determining storage experiment conditions according to the specific battery type and the battery storage temperature range;
s3, selecting a new lithium ion battery which is consistent with the initial state of the lithium ion battery with the specific battery type as an experimental battery;
s4, carrying out constant volume test on the experimental battery under the storage experimental condition, and recording storage experimental data;
s5, adjusting the experimental battery to a storage charge state set for the experimental battery through power supplement;
s6, placing the experimental battery into a thermostat set for the experimental battery, and storing a set storage period;
s7, taking the experimental battery out of the thermostat, and ensuring that the experimental battery is recovered to room temperature;
repeating the steps S4-S7 until the capacity of the experimental battery is attenuated to a preset capacity threshold, and then executing a step S8;
and S8, processing the storage experiment data, and determining unknown model parameters of the capacity fading model of the lithium ion battery according to the processed storage experiment data so as to obtain the storage life capacity fading model of the lithium ion battery with the specific battery type.
3. The model generation method of claim 2, wherein the step of processing the stored experimental data to determine unknown model parameters of the capacity fade model of the lithium ion battery based on the processed stored experimental data to obtain the storage life capacity fade model of the lithium ion battery of the particular battery type comprises:
preprocessing the stored experimental data and eliminating abnormal data;
calculating the capacity fading rate of the experimental battery after the ith storage period by adopting the following formula:
wherein Q is i Represents the discharge capacity, Q, of the experimental cell after i cycles of storage 0 Represents the initial capacity of the experimental battery, 1 is a natural number, Q iloss Indicating the time after storing i cyclesCapacity fade rate of the experimental cell;
storing the number of days t corresponding to the capacity fade rate of each of the experimental batteries i Storage temperature T i And storing the state of charge SOC i Fitting according to the capacity attenuation model of the lithium ion battery to determine the primary values of the unknown model parameters alpha, beta, gamma, lambda and k of the capacity attenuation model of the lithium ion battery and the primary capacity attenuation model of the lithium ion battery;
calculating the capacity attenuation rate of the experimental battery by using the preliminary capacity attenuation model of the lithium ion battery, comparing the capacity attenuation rate with real stored experimental data to optimize the value of the unknown model parameter of each capacity attenuation model of the lithium ion battery, and determining the final value of the unknown model parameter alpha, beta, gamma, lambda and k of the capacity attenuation model of the lithium ion battery to obtain the storage life capacity attenuation model of the lithium ion battery with the specific battery type;
and/or the presence of a gas in the gas,
the battery storage temperature range comprises the normal storage temperature and the storage temperature threshold of the lithium ion battery;
and/or the presence of a gas in the gas,
the storage experiment data includes at least one of a capacity fade rate, a storage temperature, a storage state of charge, and a storage number of days of the lithium ion battery of the specific battery type.
4. A model generation system is characterized by comprising an establishing module, an obtaining module and a determining module;
the establishing module is used for establishing a capacity attenuation model of the lithium ion battery by adopting the following formula:
wherein Q is loss Represents the capacity fade rate, T represents the storage temperature, SOC represents the storage state of charge, T represents the number of storage days, alpha, beta, gamma, lambda and k are all the lithium ionsUnknown model parameters of a capacity fade model of the battery;
the acquisition module is used for acquiring the storage experiment data of the lithium ion battery with a specific battery type;
and the determining module is used for determining unknown model parameters of the capacity fading model of the lithium ion battery according to the storage experiment data so as to obtain the storage life capacity fading model of the lithium ion battery with the specific battery type.
5. The model generation system of claim 4, wherein the determination module comprises a first determination unit, a second determination unit, a selection unit, a test unit, and a processing unit;
the first determining unit is used for determining the battery storage temperature range of the lithium ion battery with the specific battery type, selecting the lowest storage temperature, the highest storage temperature and at least one other storage temperature in the battery storage temperature range, selecting a plurality of storage charge states at each storage temperature, and setting a storage period;
the second determining unit is used for determining storage experiment conditions according to the specific battery type and the battery storage temperature range;
the selection unit is used for selecting a new lithium ion battery which is consistent with the initial state of the lithium ion battery with the specific battery type as an experimental battery;
the testing unit is used for repeatedly executing testing operation until the capacity of the experimental battery is attenuated to a preset capacity threshold value, and then calling the processing unit, wherein the testing operation comprises the following steps: carrying out constant volume test on the experimental battery under the storage experimental condition, and recording storage experimental data; adjusting the experimental battery to a storage charge state set for the experimental battery through power supplement; putting the experimental battery into a thermostat set for the experimental battery experiment, and storing for a set storage period; taking the experimental battery out of the incubator, and ensuring that the experimental battery is restored to the room temperature;
and the processing unit is used for processing the storage experiment data and determining unknown model parameters of the capacity fading model of the lithium ion battery according to the processed storage experiment data so as to obtain the storage life capacity fading model of the lithium ion battery with the specific battery type.
6. The model generation system of claim 5, wherein the processing unit comprises a processing subunit, a first computation subunit, a fitting subunit, and a second computation subunit;
the processing subunit is used for preprocessing the stored experimental data and eliminating abnormal data;
the first calculating subunit is configured to calculate a capacity fading rate of the experimental battery after the ith storage period by using the following formula:
wherein Q is i Represents the discharge capacity, Q, of the experimental cell after i cycles of storage 0 Represents the initial capacity of the experimental battery, 1 is a natural number, Q iloss Representing the capacity fading rate of the experimental battery after storing i periods;
the fitting subunit is used for storing the storage days t corresponding to the capacity decay rate of each experimental battery i Storage temperature T i And storing the state of charge SOC i Fitting according to the capacity attenuation model of the lithium ion battery to determine the primary values of the unknown model parameters alpha, beta, gamma, lambda and k of the capacity attenuation model of the lithium ion battery and the primary capacity attenuation model of the lithium ion battery;
the second calculating subunit is configured to calculate a capacity fading rate of the experimental battery by using the preliminary capacity fading model of the lithium ion battery, compare the capacity fading rate with real stored experimental data, optimize a value of an unknown model parameter of each capacity fading model of the lithium ion battery, and determine a final value of an unknown model parameter α, β, γ, λ, k of the capacity fading model of the lithium ion battery, so as to obtain a storage life capacity fading model of the lithium ion battery of the specific battery type;
and/or the presence of a gas in the gas,
the battery storage temperature range comprises the normal storage temperature and the storage temperature threshold of the lithium ion battery;
and/or the presence of a gas in the gas,
the storage experiment data comprises at least one of the capacity fade rate, the storage temperature, the storage state of charge and the storage days of the lithium ion battery of the specific battery type.
7. A method for predicting the storage life of a lithium ion battery is characterized by comprising the following steps:
setting a preset condition;
predicting the storage life of the lithium ion batteries of the same type under the preset condition by using a storage life capacity attenuation model of the lithium ion batteries of the specific battery type;
wherein the storage life capacity decay model of the lithium ion battery of the specific battery type is obtained by using the model generation method of any one of the preceding claims 1 to 3.
8. The system for predicting the storage life of the lithium ion battery is characterized by comprising a setting module and a predicting module;
the setting module is used for setting preset conditions;
the prediction module is used for predicting the storage life of the lithium ion batteries of the same type under the preset condition by using a storage life capacity attenuation model of the lithium ion batteries of the specific battery type;
wherein the storage life capacity decay model of the lithium ion battery of the specific battery type is obtained by using the model generation system of any one of the preceding claims 4-6.
9. 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 model generation method according to any one of claims 1 to 3 or the method for predicting the storage life of a lithium ion battery according to claim 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the model generation method according to any one of claims 1 to 3, or carries out the method of predicting the storage life of a lithium-ion battery according to claim 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111113358.1A CN115906370A (en) | 2021-09-23 | 2021-09-23 | Model generation method, and lithium ion battery storage life prediction method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111113358.1A CN115906370A (en) | 2021-09-23 | 2021-09-23 | Model generation method, and lithium ion battery storage life prediction method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115906370A true CN115906370A (en) | 2023-04-04 |
Family
ID=86494262
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111113358.1A Pending CN115906370A (en) | 2021-09-23 | 2021-09-23 | Model generation method, and lithium ion battery storage life prediction method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115906370A (en) |
-
2021
- 2021-09-23 CN CN202111113358.1A patent/CN115906370A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11408942B2 (en) | Method for predicting service life of retired power battery | |
| CN111695301A (en) | Method and device for predicting battery charge state, storage medium and equipment | |
| CN114371409B (en) | Training method of battery state prediction model, battery state prediction method and device | |
| CN112068003B (en) | Method and device for predicting service life of cadmium-nickel storage battery based on linear wiener process | |
| CN114705990B (en) | Method and system for estimating state of charge of battery cluster, electronic device and storage medium | |
| CN111289910B (en) | Grading method and device for echelon batteries, computer equipment and medium | |
| CN111025041A (en) | Electric vehicle charging pile monitoring method and system, computer equipment and medium | |
| CN115840144A (en) | Battery simulation calculation method and device, computer equipment and storage medium | |
| CN113743661A (en) | Method, system, equipment and storage medium for predicting online capacity of lithium ion battery | |
| CN111525202A (en) | Method, system, equipment and medium for monitoring DCR in lithium ion battery cycle | |
| CN117741442A (en) | Cell temperature prediction method, device, apparatus, storage medium and program product | |
| CN116992274A (en) | Short-term wind speed prediction method and system based on improved principal component regression model | |
| CN115906370A (en) | Model generation method, and lithium ion battery storage life prediction method and system | |
| CN115481767A (en) | Operation data processing method and device for power distribution network maintenance and computer equipment | |
| CN116125279A (en) | Method, device, equipment and storage medium for determining battery health state | |
| CN115267556A (en) | Battery life degradation analysis method, storage medium, and electronic device | |
| CN112345954B (en) | Battery life prediction method, device, electronic equipment and medium | |
| CN114076899B (en) | Method, device, equipment, system and medium for estimating battery life in steps | |
| CN112240987A (en) | Power testing method, system, computer device and storage medium | |
| CN113805084B (en) | Method and device for calculating battery capacity attenuation, computer equipment and storage medium | |
| CN114970307B (en) | General reverse calculation method applied to material design optimization of high-end equipment | |
| CN116705210B (en) | Construction method of battery cell aging model and battery cell full life cycle performance prediction method | |
| CN114841457B (en) | Power load estimation method and system, electronic device, and storage medium | |
| CN116227127A (en) | Method and device for determining performance of transformer, computer equipment and storage medium | |
| Sausen et al. | PROPOSITION OF AN EXTENSION TO THE PEUKERT’S LAWMODEL APPLIED TO THE PREDICTION OF THE BATTERIESLIFETIME |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination |