CN114089204B - Battery capacity diving inflection point prediction method and device - Google Patents
Battery capacity diving inflection point prediction method and device Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 57
- 230000009189 diving Effects 0.000 title description 12
- 230000032683 aging Effects 0.000 claims abstract description 327
- 238000012360 testing method Methods 0.000 claims abstract description 262
- 238000002474 experimental method Methods 0.000 claims abstract description 25
- 238000000418 atomic force spectrum Methods 0.000 claims description 82
- 238000012545 processing Methods 0.000 claims description 21
- 230000015654 memory Effects 0.000 claims description 20
- 238000007599 discharging Methods 0.000 claims description 10
- 230000000875 corresponding effect Effects 0.000 description 49
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 11
- 229910001416 lithium ion Inorganic materials 0.000 description 11
- 238000010586 diagram Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 5
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 4
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- 239000010439 graphite Substances 0.000 description 4
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 238000009825 accumulation Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 2
- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000011084 recovery Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 239000010926 waste battery Substances 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
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- G—PHYSICS
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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Abstract
The invention provides a battery capacity jump inflection point prediction method and device, wherein the method comprises the following steps: respectively establishing a battery capacity loss model of the target battery and an electrode capacity loss model of the target battery based on the battery capacity influence parameters; performing aging test experiments on the target battery under different battery capacity influence parameters, and calculating to obtain initial battery capacity, battery capacity after aging test, initial electrode capacity and electrode capacity after aging test corresponding to the target battery under different battery capacity influence parameters; respectively calculating corresponding model parameters; and substituting the current battery capacity influence parameter, the initial battery capacity and the initial electrode capacity into the model respectively, calculating an aging time node corresponding to the aged electrode capacity and the aged battery capacity, and further determining a battery capacity jump inflection point of the target battery. By carrying out a small amount of aging test experiments, the battery capacity jump inflection point of the battery is accurately predicted, and the prediction efficiency is improved.
Description
Technical Field
The invention relates to the technical field of batteries, in particular to a battery capacity jump inflection point prediction method and device.
Background
In recent years, with the rapid development of mobile vehicles such as new energy automobiles and electric (self-propelled) vehicles, the performance of lithium ion batteries is more and more emphasized, and particularly, the service life of lithium ion batteries is directly related to the use cost, user experience, recovery of waste batteries and the like of users. The optimal condition is that the service life of the lithium ion battery can be infinitely long, so that the use cost of a user is saved, and the environmental pressure of the recovery treatment of the waste battery is reduced. However, in reality, the lithium ion battery may not only have capacity fade phenomenon, but may even have capacity jump phenomenon. The capacity jump means that the battery capacity is suddenly accelerated to decline after a certain degree, and the capacity declines to the end of life in a short time. Capacity diving now presents unpredictability, not only seriously affects user experience, but also causes potential safety hazard to a certain extent, and is a problem which needs to be solved urgently in the industry.
The current method for predicting the capacity diving mainly comprises two methods of experimental test and experience prediction. 1. Through an accelerated aging test, the total cycle number or accumulation An Shishu of the battery when the capacity jump occurs is recorded, and the result is converted into the driving mileage through a simulation model, so that the time when the capacity jump of the battery occurs is predicted. The disadvantages of this method are: a large number of accelerated aging tests are required to be carried out on each battery cell product, the test period is long, a large number of test resources are occupied, and the test cost and the time cost are high. Most importantly, the capacity fade mechanism under the accelerated aging test working condition is different from the capacity fade mechanism under the actual working condition, which leads to inaccurate prediction results. 2. The second common approach is through empirical prediction. For example, according to experience, the capacity jump inflection point of most lithium ion battery systems is judged to be about soh=80%, and the corresponding driving mileage at the moment is calculated according to the battery life decay law, so that the occurrence time of capacity jump is estimated. Although the method reduces experimental tests, the deviation between the actual situation and the estimated result is larger.
Therefore, how to accurately predict the battery capacity jump inflection point is an urgent problem to be solved.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a battery capacity jump inflection point prediction method and device, so as to solve the problems that the prediction result obtained by the method for predicting capacity jump in the prior art has large deviation from the actual working condition and the accuracy of the prediction result is low.
The embodiment of the invention provides a battery capacity jump inflection point prediction method, which comprises the following steps:
respectively establishing a battery capacity loss model of a target battery and an electrode capacity loss model of the target battery based on battery capacity influence parameters, wherein the electrode capacity loss model comprises the following components: a positive electrode capacity loss model and/or a negative electrode capacity loss model, the battery capacity influencing parameters including: charging current of the battery, discharging current of the battery, state of charge, and ambient temperature;
performing aging test experiments on the target battery under different battery capacity influence parameters, and calculating to obtain initial battery capacity, battery capacity after aging test, initial electrode capacity and electrode capacity after aging test corresponding to the target battery under different battery capacity influence parameters;
Calculating the battery capacity loss model and model parameters corresponding to the electrode capacity loss model respectively based on the initial battery capacity, the battery capacity after the aging test, the initial electrode capacity and the electrode capacity after the aging test;
acquiring a current battery capacity influence parameter of the target battery;
substituting the current battery capacity influence parameter, the initial battery capacity and the initial electrode capacity into the battery capacity loss model and the electrode capacity loss model respectively, and calculating an aging time node corresponding to the aged electrode capacity and the aged battery capacity;
and determining a battery capacity jump inflection point of the target battery based on the aging time node.
Optionally, the calculating the aging time node corresponding to the case that the aged electrode capacity is equal to the aged battery capacity includes:
calculating a first aging time node corresponding to the aged positive electrode capacity and the aged battery capacity;
and/or calculating a second aging time node corresponding to the case that the capacity of the aged negative electrode is equal to the capacity of the aged battery.
Optionally, the determining a battery capacity jump inflection point of the target battery based on the aging time node includes:
When the aging time node is the first aging time node or the second aging time node, determining the first aging time node or the second aging time node as a battery capacity jump inflection point of the target battery;
when the aging time node includes: and when the first aging time node and the second aging time node are used, determining the minimum aging time node in the first aging time node and the second aging time node as a battery capacity jump inflection point of the target battery, wherein the first aging time node and the second aging time node are charge and discharge cycle times or battery aging time.
Optionally, performing an aging test experiment on the target battery under different battery capacity influence parameters, and calculating to obtain an initial battery capacity, a battery capacity after an aging test, an initial electrode capacity, and an electrode capacity after an aging test, which correspond to the target battery under different battery capacity influence parameters, where the aging test comprises:
determining a battery electromotive force curve of the target battery before and after the aging test based on the aging test experiment of the target battery under the current battery capacity influence parameter;
Based on the discharge cut-off voltage of the target battery and the battery electromotive force curves of the target battery before and after the aging test, respectively determining the initial battery capacity and the battery capacity after the aging test;
and calculating the initial electrode capacity and the electrode capacity after the aging test based on the battery electromotive force curves of the target battery before and after the aging test.
Optionally, the calculating the initial electrode capacity and the electrode capacity after the aging test based on the battery electromotive force curves of the target battery before and after the aging test includes:
calculating initial negative electrode capacity and negative electrode capacity after aging test based on battery electromotive force curves of the target battery before and after aging test;
and/or calculating initial anode capacity and anode capacity after the aging test based on the battery electromotive force curve of the target battery before and after the aging test, and respectively calculating initial cathode capacity and anode capacity after the aging test based on the battery electromotive force curve of the target battery before and after the aging test, the initial battery capacity, the battery capacity after the aging test, the initial anode capacity and the anode capacity after the aging test.
Optionally, the calculating the initial anode capacity and the anode capacity after the aging test based on the battery electromotive force curves of the target battery before and after the aging test includes:
differentiating the battery electromotive force curves of the target battery before and after the aging test to obtain an initial voltage differential curve and an aging voltage differential curve;
respectively calculating a first capacity value and a second capacity value of a preset second voltage platform of the target battery on a voltage differential curve based on the voltage differential curve and the aging voltage differential curve;
and calculating the initial anode capacity and the anode capacity after the aging test based on the first capacity value, the second capacity value and the relation between the preset anode capacity and the capacity value of a preset second voltage platform of the target battery on a voltage differential curve.
Optionally, the calculating the initial positive electrode capacity and the positive electrode capacity after the aging test based on the battery electromotive force curve of the target battery before and after the aging test, the initial battery capacity, the battery capacity after the aging test, the initial negative electrode capacity and the negative electrode capacity after the aging test respectively includes:
calculating a cathode electromotive force curve of the target battery before and after aging based on the cathode capacity of the target battery before and after aging test and a cathode standard electromotive force curve;
Respectively calculating the voltage interval actually used by the anode of the target battery before and after the aging test based on the initial battery capacity, the battery capacity after the aging test, the initial anode capacity, the anode capacity after the aging test, the battery electromotive force curve of the target battery before and after the aging test and the anode electromotive force curve;
based on a voltage interval of the target battery, which is actually used by the positive electrode before and after the aging test, and a positive electrode standard electromotive force curve of the target battery, respectively calculating the relation between the positive electrode capacity and the battery capacity of the target battery before and after the aging test;
and respectively calculating the initial positive electrode capacity and the positive electrode capacity after the aging test based on the initial battery capacity, the battery capacity after the aging test and the relation between the positive electrode capacity and the battery capacity of the target battery before and after the aging test.
Optionally, the calculating the voltage interval actually used by the positive electrode of the target battery before and after the aging test based on the initial battery capacity, the battery capacity after the aging test, the initial negative electrode capacity after the aging test, the battery electromotive force curve of the target battery before and after the aging test, and the negative electrode electromotive force curve respectively includes:
Based on a battery electromotive force curve and a negative electrode electromotive force curve of the target battery before and after the aging test, obtaining a positive electrode electromotive force curve of the target battery before and after the aging test;
determining initial positive voltage corresponding to zero positive capacity of the target battery before and after the aging test based on positive electromotive force curves of the target battery before and after the aging test;
inputting the initial battery capacity and the battery capacity after the aging test into the positive electrode electromotive force curve to obtain the corresponding cut-off positive electrode voltage of the target battery before and after the aging test;
and respectively calculating the voltage interval of the target battery in which the positive electrode is actually used before and after the aging test based on the initial positive electrode voltage corresponding to the target battery before and after the aging test when the positive electrode capacity is zero and the cut-off positive electrode voltage corresponding to the target battery before and after the aging test.
Optionally, the calculating the relationship between the positive electrode capacity and the battery capacity of the target battery before and after the aging test based on the voltage interval of the positive electrode actually used by the target battery before and after the aging test and the positive electrode standard electromotive force curve of the target battery includes:
respectively acquiring a first capacity and a second capacity corresponding to a voltage interval actually used by the anode before and after the aging test on an anode standard electromotive force curve before and after the aging test;
Calculating the relation between the positive electrode capacity and the battery capacity of the target battery before the aging test based on the relation between the first capacity and the total capacity corresponding to the positive electrode standard electromotive force curve before the aging test;
and calculating the relation between the positive electrode capacity and the battery capacity of the target battery after the aging test based on the relation between the second capacity and the corresponding total capacity on the positive electrode standard electromotive force curve after the aging test.
The embodiment of the invention also provides a battery capacity diving inflection point prediction device, which comprises:
the first processing module is used for respectively establishing a battery capacity loss model of a target battery and an electrode capacity loss model of the target battery based on battery capacity influence parameters, wherein the electrode capacity loss model comprises the following components: a positive electrode capacity loss model and/or a negative electrode capacity loss model, the battery capacity influencing parameters including: charging current of the battery, discharging current of the battery, state of charge, and ambient temperature;
the second processing module is used for carrying out aging test experiments on the target battery under different battery capacity influence parameters, and calculating to obtain initial battery capacity and battery capacity after aging test, and initial electrode capacity and electrode capacity after aging test corresponding to the target battery under different battery capacity influence parameters;
The third processing module is used for respectively calculating the battery capacity loss model and model parameters corresponding to the electrode capacity loss model based on the initial battery capacity, the battery capacity after the aging test, the initial electrode capacity and the electrode capacity after the aging test;
a fourth processing module, configured to obtain a current battery capacity influence parameter of the target battery;
a fifth processing module, configured to replace the current battery capacity influencing parameter and the initial battery capacity, the initial electrode capacity, and the battery capacity loss model and the electrode capacity loss model respectively, and calculate an aging time node corresponding to when the aged electrode capacity is equal to the aged battery capacity;
and a sixth processing module, configured to determine a battery capacity jump inflection point of the target battery based on the aging time node.
The embodiment of the invention also provides electronic equipment, which comprises: the device comprises a memory and a processor, wherein the memory and the processor are in communication connection, the memory stores computer instructions, and the processor executes the computer instructions so as to execute the method provided by the embodiment of the invention.
The embodiment of the invention also provides a computer readable storage medium, which stores computer instructions for causing the computer to execute the method provided by the embodiment of the invention.
The technical scheme of the invention has the following advantages:
the embodiment of the invention provides a battery capacity jump inflection point prediction method and a device, which respectively establish a battery capacity loss model of a target battery and an electrode capacity loss model of the target battery based on battery capacity influence parameters, wherein the electrode capacity loss model comprises the following steps: the positive electrode capacity loss model and/or the negative electrode capacity loss model, and the battery capacity influence parameters include: charging current of the battery, discharging current of the battery, state of charge, and ambient temperature; performing aging test experiments on the target battery under different battery capacity influence parameters, and calculating to obtain initial battery capacity, battery capacity after aging test, initial electrode capacity and electrode capacity after aging test corresponding to the target battery under different battery capacity influence parameters; calculating model parameters corresponding to a battery capacity loss model and an electrode capacity loss model respectively based on the initial battery capacity, the battery capacity after the aging test, the initial electrode capacity and the electrode capacity after the aging test; acquiring a current battery capacity influence parameter of a target battery; substituting the current battery capacity influence parameter, the initial battery capacity and the initial electrode capacity into a battery capacity loss model and an electrode capacity loss model respectively, and calculating an aging time node corresponding to the aged electrode capacity and the aged battery capacity which are equal; and determining a battery capacity jump inflection point of the target battery based on the aging time node. The capacity loss model of the battery and the electrode is built, the capacities of the battery and the electrode before and after the aging test are obtained through a small amount of aging test experiments, and the calibration of model parameters is completed, so that aging time nodes with equal capacity values can be determined according to actual working conditions, and further, the battery capacity diving inflection point of the battery can be accurately predicted, excessive test resources are not needed, the test period is shortened, the test resources and the time cost are saved, the prediction efficiency is improved, and an accurate data basis is provided for solving various potential safety hazards caused by capacity diving.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a battery capacity jump inflection point prediction method in an embodiment of the present invention;
FIG. 2 is a schematic diagram of differentiating a battery electromotive force curve to obtain a differentiated curve according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a working process of battery capacity jump inflection point prediction in an embodiment of the present invention;
FIG. 4 is a schematic diagram of a battery capacity jump inflection point in an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a battery capacity jump inflection point predicting device according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention.
The technical features of the different embodiments of the invention described below may be combined with one another as long as they do not conflict with one another.
The current method for predicting the capacity diving mainly comprises two methods of experimental test and experience prediction. 1. Through an accelerated aging test, the total cycle number or accumulation An Shishu of the battery when the capacity jump occurs is recorded, and the result is converted into the driving mileage through a simulation model, so that the time when the capacity jump of the battery occurs is predicted. The disadvantages of this method are: a large number of accelerated aging tests are required to be carried out on each battery cell product, the test period is long, a large number of test resources are occupied, and the test cost and the time cost are high. Most importantly, the capacity fade mechanism under the accelerated aging test working condition is different from the capacity fade mechanism under the actual working condition, which leads to inaccurate prediction results. 2. The second common approach is through empirical prediction. For example, according to experience, the capacity jump inflection point of most lithium ion battery systems is judged to be about soh=80%, and the corresponding driving mileage at the moment is calculated according to the battery life decay law, so that the occurrence time of capacity jump is estimated. Although the method reduces experimental tests, the deviation between the actual situation and the estimated result is larger.
Based on the above problems, the embodiment of the present invention provides a battery capacity jump inflection point prediction method, as shown in fig. 1, which specifically includes the following steps:
step S101: and respectively establishing a battery capacity loss model of the target battery and an electrode capacity loss model of the target battery based on the battery capacity influence parameters.
Specifically, in an embodiment of the present invention, the electrode capacity loss model includes: the positive electrode capacity loss model and/or the negative electrode capacity loss model, and the battery capacity influence parameters include: charging current of the battery, discharging current of the battery, state of charge, ambient temperature. In practical application, the battery capacity influence parameters can be adaptively adjusted according to the requirement of the battery capacity jump inflection point prediction accuracy and the influence factors of the battery capacity, and the invention is not limited to the method. Each of the capacity loss models described above describes a relationship model of capacity with the number of charge-discharge cycles or the battery aging time.
Step S102: and carrying out aging test experiments on the target battery under different battery capacity influence parameters, and calculating to obtain the corresponding initial battery capacity, the battery capacity after aging test, the corresponding initial electrode capacity and the electrode capacity after aging test of the target battery under different battery capacity influence parameters.
Specifically, in the embodiment of the invention, assuming that the influence of each battery capacity influence parameter on each capacity loss model is independent, three different aging test experiments are performed by respectively changing one parameter in charge/discharge current, charge state and ambient temperature of a target battery under the conditions of fixed charge/discharge cycle times and battery aging time, and fixing the other two parameters. In an embodiment of the present invention, the initial electrode capacity includes: the initial positive electrode capacity and/or initial negative electrode capacity, the electrode capacity after the aging test comprises: the positive electrode capacity after the aging test and/or the negative electrode capacity after the aging test.
Step S103: and calculating a battery capacity loss model and model parameters corresponding to the electrode capacity loss model respectively based on the initial battery capacity, the battery capacity after the aging test, the initial electrode capacity and the electrode capacity after the aging test.
Specifically, each capacity loss model may be expressed as a combination of a capacity and each battery capacity influence parameter, that is, a functional relationship between a charge/discharge current, a state of charge, and an ambient temperature of the battery, and the model parameter is a fixed coefficient in the functional relationship between each battery capacity influence parameter.
Step S104: the current battery capacity influencing parameter of the target battery is obtained.
Specifically, in the embodiment of the present invention, the current battery capacity influence parameters include: the current charging current, the current state of charge and the current ambient temperature of the target battery are the same, and the current discharging current of the target battery is constant. In practical application, the current battery capacity influence parameter can also be adaptively adjusted according to the requirement of the battery capacity jump inflection point prediction accuracy and the influence factor of the battery capacity, and the invention is not limited to this.
Step S105: and substituting the current battery capacity influence parameter, the initial battery capacity and the initial electrode capacity into a battery capacity loss model and an electrode capacity loss model respectively, and calculating an aging time node corresponding to the aged electrode capacity and the aged battery capacity.
Specifically, calculating a first aging time node corresponding to the case that the capacity of the aged positive electrode is equal to the capacity of the aged battery; and/or calculating a second aging time node corresponding to the case that the capacity of the aged negative electrode is equal to the capacity of the aged battery. Because the model parameters of the capacity loss models are already determined, the capacity values corresponding to the capacity loss models of different charge and discharge cycle times or battery aging time can be calculated according to the actual battery capacity influence parameters.
Step S106: and determining a battery capacity jump inflection point of the target battery based on the aging time node.
Specifically, when the aging time node is a first aging time node or a second aging time node, determining the first aging time node or the second aging time node as a battery capacity jump inflection point of the target battery; when the aging time node comprises: and when the first aging time node and the second aging time node are used, determining the minimum aging time node in the first aging time node and the second aging time node as a battery capacity jump inflection point of the target battery, wherein the first aging time node and the second aging time node are charge and discharge cycle times or battery aging time.
In order to further improve accuracy of a final battery capacity jump inflection point prediction result in the embodiment of the present invention, an electrode capacity loss model is used as a positive electrode capacity loss model and a negative electrode capacity loss model, and an initial electrode capacity includes: initial positive electrode capacity and initial negative electrode capacity, the electrode capacity after the aging test includes: in the description of the positive electrode capacity after the aging test and the negative electrode capacity after the aging test as examples, in practical application, only the positive electrode capacity loss model or the negative electrode capacity loss model may be adopted, and the initial electrode capacity and the electrode capacity after the aging test may be selected corresponding to the models, which is not limited to the present invention.
In the actual working condition, when the battery normally operates, the positive electrode capacity and the negative electrode capacity of the battery are larger than the battery capacity, and after the battery has capacity jump phenomenon, the positive electrode capacity and the negative electrode capacity of the battery are smaller than the battery capacity, so that the embodiment of the invention determines the aging time node when the battery capacity is equal to the positive electrode capacity or the negative electrode capacity as the battery capacity jump inflection point, and the invention more accords with the actual operating condition of the battery.
When the battery is in an initial state, the capacities of the positive electrode and the negative electrode are larger than the lithium ion capacity of the battery, but in the subsequent aging process, the decay rates of the capacities of the positive electrode and the negative electrode exceed the rate of lithium ion loss, so that the inflection point of capacity jump can be obtained. In order to avoid various potential safety hazards caused by capacity diving, in the embodiment of the invention, an aging time node with a higher capacity fading rate in the anode and the cathode is determined as a battery capacity diving inflection point. In practical application, the aging time node with the slow degradation rate of the capacity can be determined as the battery capacity jump inflection point without considering the hidden trouble, or the battery capacity jump inflection point is determined according to the modes of taking the average value of two different aging time nodes and the like, and the difference value of the prediction results of the battery capacity jump inflection points can be ignored compared with the service life of the whole battery, so that the accurate prediction of the battery capacity inflection point can be realized through the mode.
By executing the steps, the battery capacity jump inflection point prediction method provided by the embodiment of the invention is used for obtaining the capacities of the battery and the electrode before and after the aging test by establishing the capacity loss model of the battery and the electrode and performing a small amount of aging test experiments, and completing the calibration of model parameters, so that the aging time nodes with equal capacity values of the capacity loss model of the battery and the electrode can be determined according to actual working conditions, further, the battery capacity jump inflection point of the battery can be accurately predicted, excessive test resources are not needed, the test period is shortened, the test resources and the time cost are saved, the prediction efficiency is improved, and an accurate data basis is provided for solving various potential safety hazards caused by capacity jump.
Specifically, in an embodiment, the step S102 specifically includes:
step S201: and determining the battery electromotive force curves of the target battery before and after the aging test based on the aging test experiment of the target battery under the current battery capacity influence parameters.
Specifically, taking the electromotive force curve determination process before the aging test as an example, the target battery can be subjected to constant current I under the current battery capacity influence parameters ch Charged to a voltage Wherein->An upper cutoff voltage for charging the battery system; then switch to constant current 0.05C charge to +.>Standing for 1 hr after charging, discharging to discharge cut-off voltage +.>And stand stillAnd 1 hour. The above charging and standing steps were repeated, but the discharge current was set to 0.2c,0.3c,0.5c,1c at a time. After a series of voltage curves under different discharge multiplying powers are obtained, a regression algorithm is adopted to calculate a voltage curve when the discharge current is constant equal to 0, and the voltage curve at the moment is the initial electromotive force curve before the aging test of the battery. The electromotive force curve determination process after the aging test is similar to the initial electromotive force curve determination process, and a detailed description thereof will be omitted.
Step S202: and respectively determining the initial battery capacity and the battery capacity after the aging test based on the discharge cut-off voltage of the target battery and the battery electromotive force curves of the target battery before and after the aging test.
Specifically, the capacity corresponding to the charge cutoff voltage on the initial electromotive force curve of the battery is the initial battery capacityAfter the aging test experiment, for example: calendar aging after any storage time, such as 1 month, or cyclic aging after any charge and discharge cycles, such as 300 cycles, wherein the capacity corresponding to the discharge cutoff voltage on the electromotive force curve corresponding to the battery is the capacity +. >
Step S203: and calculating the initial electrode capacity and the electrode capacity after the aging test based on the battery electromotive force curves of the target battery before and after the aging test.
Specifically, step S203 calculates the initial negative electrode capacity and the negative electrode capacity after the aging test based on the battery electromotive force curves of the target battery before and after the aging test.
Further, an initial voltage differential curve and an aging voltage differential curve are obtained by differentiating the battery electromotive force curves of the target battery before and after the aging test.
An initial voltage differential curve obtained by differential analysis of the initial electromotive force curve of the battery is shown in fig. 2, for example.
And respectively calculating a first capacity value and a second capacity value of a preset second voltage platform of the target battery on the voltage differential curve based on the voltage differential curve and the aging voltage differential curve.
Fig. 2 is a diagram illustrating basic information of a differential curve using a lithium iron phosphate battery as an example. In general, the differential curve may be divided into 3 regions, labeled I, II, III, respectively, as shown in fig. 2, according to the capacity mutation. The region II is a preset second voltage platform.
And calculating the initial anode capacity and the anode capacity after the aging test based on the first capacity value and the second capacity value and the relation between the preset anode capacity and the capacity value of the preset second voltage platform of the target battery on the voltage differential curve.
Specifically, as shown in FIG. 2, the width of region IIThe capacity of the graphite anode is positively correlated, and the following conditions are satisfied: initial negative electrode Capacity->Wherein delta is the proportion of the second voltage platform of the graphite cathode to the total capacity, and delta is a fixed value determined by the material of the target battery, and is 0.25 for example of a lithium iron phosphate battery. Similarly, by differential analysis of any aging state and electromotive force curve after aging test, the capacity of the anode after aging test can be calculated>Specifically, in another alternative embodiment, after calculating the initial anode capacity and the anode capacity after the aging test, the step S203 further calculates the initial cathode capacity and the cathode capacity after the aging test based on the battery electromotive force curve of the target battery before and after the aging test, the initial battery capacity, the battery capacity after the aging test, the initial anode capacity and the anode capacity after the aging test, respectively.
Specifically, calculating a negative electrode electromotive force curve of the target battery before and after aging based on the negative electrode capacity of the target battery before and after aging test and a negative electrode standard electromotive force curve; and respectively calculating the voltage interval of the target battery in which the positive electrode is actually used before and after the aging test based on the initial battery capacity, the battery capacity after the aging test, the initial negative electrode capacity, the negative electrode capacity after the aging test, the battery electromotive force curve of the target battery before and after the aging test and the negative electrode electromotive force curve.
Further, a positive electrode electromotive force curve of the target battery before and after the aging test is obtained based on a battery electromotive force curve and a negative electrode electromotive force curve of the target battery before and after the aging test; determining initial positive voltage corresponding to the target battery when the positive capacity of the target battery is zero before and after the aging test based on the positive electromotive force curves of the target battery before and after the aging test; inputting the initial battery capacity and the battery capacity after the aging test into a positive electrode electromotive force curve to obtain the corresponding cut-off positive electrode voltage of the target battery before and after the aging test; and respectively calculating the voltage interval of the target battery for actually using the positive electrode before and after the aging test based on the initial positive electrode voltage corresponding to the target battery before and after the aging test when the positive electrode capacity is zero and the cut-off positive electrode voltage corresponding to the target battery before and after the aging test.
Illustratively, the steps are calculated as described aboveAnd->Then, combining with standard potential curve of graphite cathode (which can be obtained by half-cell test), calculating to obtain true electrode potential curve of graphite cathode in cell>(initial state before burn-in test) and +.>(any state of aging after aging test). According to formulas (1), (2):
calculating to obtain the electromotive force curve of the positive electrode in the battery (initial state) and->(any state of aging). In the above formulas (1), (2)>And->The electromotive force curves of the battery in the initial state and the aged state are respectively shown.
Obtaining a positive electrode potential curve(initial state) and->After (any aging state), the voltage interval of the positive electrode in the battery is calculated according to the following formulas (3) to (6) by combining the actual use voltage window of the battery and the potential value corresponding to the negative electrode under the voltage window:
thereby byAnd->Determining the voltage interval of the target battery in which the positive electrode is actually used before the aging test, wherein the voltage interval is defined by +.>And->And determining the voltage interval of the target battery, which is actually used by the positive electrode after the aging test.
And respectively calculating the relation between the positive electrode capacity and the battery capacity of the target battery before and after the aging test based on the voltage interval of the target battery before and after the aging test, which is actually used by the positive electrode, and the positive electrode standard electromotive force curve of the target battery.
Further, respectively acquiring a first capacity and a second capacity corresponding to a voltage interval actually used by the anode before and after the aging test on an anode standard electromotive force curve before and after the aging test; calculating the relation between the positive electrode capacity and the battery capacity of the target battery before the aging test based on the relation between the first capacity and the corresponding total capacity on the positive electrode standard electromotive force curve before the aging test; and calculating the relation between the positive electrode capacity and the battery capacity of the target battery after the aging test based on the relation between the second capacity and the corresponding total capacity on the positive electrode standard electromotive force curve after the aging test.
Based on the relation between the initial battery capacity, the battery capacity after the aging test and the positive electrode capacity and the battery capacity of the target battery before and after the aging test, the initial positive electrode capacity and the positive electrode capacity after the aging test are calculated respectively.
Illustratively, taking the initial state as an example, the sample is taken on a positive standard potential curve (obtainable by half-cell testing)Capacity corresponding to voltage interval>Calculate->Ratio to total capacity of the positive standard potential curve:
the actual maximum capacity of the positive electrode in the initial state of the battery, namely the initial positive electrode capacityCalculated by the following formula (8):
similarly, the actual maximum capacity of the positive electrode in any aging state, namely the positive electrode capacity after aging test, can be calculated
The battery capacity jump inflection point prediction method provided by the embodiment of the invention is described in detail below with reference to a specific application example.
Taking a target battery as a lithium ion battery as an example, the battery aging conditions comprise temperature (T), current (I), state of charge (SOC) (x), charge-discharge cycle number (n) and battery aging time (T). Wherein the current (I) Comprising the following steps: a charging current and a discharging current. Let lithium ion loss equation be f Q ＝f(I,T,x,n,t)，f Q The battery capacity loss model as a function of current (I), temperature (T), SOC (x), number of cycles (n), time (T) can be expressed by equation (9):
Assuming that the effects of current (I), temperature (T), SOC (x) on capacity loss are independent, equation (9) above can be written as:
f 1 (I,n,t)、f 2 (T,n,t)、f 3 (x, n, t) can specifically calibrate its analytical formula by designing a matrix burn-in test.
Let the positive capacity fade equation of the battery be F Q ＝F(I,T,x,n,t)，F Q The positive electrode capacity loss model as a function of current (I), temperature (T), SOC (x), number of cycles (n), time (T) can be expressed by equation (11):
assuming that the effects of current (I), temperature (T), SOC (x) on capacity loss are independent, equation (11) above can be written as:
F 1 (I,n,t)、F 2 (T,n,t)、F 3 (x, n, t) can specifically calibrate its analytical formula by designing a matrix burn-in test.
Let the negative capacity fade equation of the battery be Γ Q ＝F(I,T,x,n,t)，Γ Q As a function of current (I), temperature (T), SOC (x), number of cycles (n), time (T), negative electrode capacityThe decay model can be represented by equation (13):
assuming that the effects of current (I), temperature (T), SOC (x) on capacity loss are independent, equation (13) above can be written as:
Γ 1 (I,n,t)、Γ 2 (T,n,t)、Γ 3 (x, n, t) can specifically calibrate its analytical formula by designing a matrix burn-in test.
The following accelerated aging test experiments were designed separately: in the first set of experiments two cells were kept at temperature (T 0 ) And SOC interval (x) 0 ) The same, the charge and discharge currents are respectively I 1 And I 2 Wherein I 1 And I 2 The corresponding discharge currents are the same, and the charging currents are different; in the second set of experiments two cells were maintained at temperature (T 0 ) And current (I) 0 ) The same, and the circulation intervals are respectively set as x 1 And x 2 The method comprises the steps of carrying out a first treatment on the surface of the Two cells in the third set of experiments held current (I 0 ) And SOC interval (x) 0 ) The same, and the temperatures are respectively set as T 1 And T 2 . The test period was set to 300 cycles, as shown in FIG. 3, and the three test sets were each calculated according to the method of the above-described embodimentAndand->And->The calculated parameters are shown in table 1. />
The parameters in the functions in the formulas (10), (12) and (14) are calibrated by using the capacity results in table 1, for example: according toAndf in calibration formula (10) 1 Parameters of (I, n, t), etc.
The actual aging conditions T, I, x, and the basic parameters of the battery cell are then determinedSubstituting the parameters into formulas (10), (12) and (14) after parameter calibration to calculate the equivalent +.>Or->The corresponding number of cycles n or aging time t is shown in fig. 4. />
And finally, determining n with smaller cycle number or t with smaller aging time as the capacity jump inflection point of the lithium ion battery through comparison.
The technical scheme provided by the embodiment of the invention comprehensively judges the inflection point of the capacity jump based on the battery capacity loss model, the positive electrode capacity loss model and the negative electrode capacity loss model, and can accurately estimate the time of the capacity jump by only a small amount of battery cell test data. The scheme can save test resources and time cost and improve the accuracy of capacity diving prediction. Meanwhile, compared with an experiment calibration method, the scheme does not occupy too much test resources, the test period is shorter, and a large amount of test resources and time cost can be saved.
By executing the steps, the battery capacity jump inflection point prediction method provided by the embodiment of the invention is used for obtaining the capacities of the battery and the electrode before and after the aging test by establishing the capacity loss model of the battery and the electrode and performing a small amount of aging test experiments, and completing the calibration of model parameters, so that the aging time nodes with equal capacity values of the capacity loss model of the battery and the electrode can be determined according to actual working conditions, further, the battery capacity jump inflection point of the battery can be accurately predicted, excessive test resources are not needed, the test period is shortened, the test resources and the time cost are saved, the prediction efficiency is improved, and an accurate data basis is provided for solving various potential safety hazards caused by capacity jump.
The embodiment of the invention also provides a battery capacity diving inflection point prediction device, as shown in fig. 5, which comprises:
the first processing module 101 is configured to establish a battery capacity loss model of the target battery and an electrode capacity loss model of the target battery based on the battery capacity influence parameters, where the electrode capacity loss model includes: the positive electrode capacity loss model and/or the negative electrode capacity loss model, and the battery capacity influence parameters include: charging current of the battery, discharging current of the battery, state of charge, ambient temperature. For details, refer to the related description of step S101 in the above method embodiment, and no further description is given here.
The second processing module 102 is configured to perform an aging test experiment on the target battery under different battery capacity influence parameters, and calculate to obtain an initial battery capacity, a battery capacity after the aging test, a corresponding initial electrode capacity, and an electrode capacity after the aging test, where the initial battery capacity and the electrode capacity correspond to each other. For details, refer to the related description of step S102 in the above method embodiment, and no further description is given here.
And a third processing module 103, configured to calculate a battery capacity loss model and model parameters corresponding to the electrode capacity loss model based on the initial battery capacity, the battery capacity after the aging test, the initial electrode capacity, and the electrode capacity after the aging test, respectively. For details, see the description of step S103 in the above method embodiment, and the details are not repeated here.
A fourth processing module 104 is configured to obtain a current battery capacity influencing parameter of the target battery. For details, refer to the related description of step S104 in the above method embodiment, and no further description is given here.
And a fifth processing module 105, configured to calculate an aging time node corresponding to when the aged electrode capacity is equal to the aged battery capacity by substituting the current battery capacity influence parameter, the initial battery capacity, the initial electrode capacity, and the current battery capacity into the battery capacity loss model and the electrode capacity loss model, respectively. For details, see the description of step S105 in the above method embodiment, and the details are not repeated here.
A sixth processing module 106 is configured to determine a battery capacity jump inflection point of the target battery based on the aging time node. For details, refer to the related description of step S106 in the above method embodiment, and no further description is given here.
Through the cooperative cooperation of the components, the battery capacity water jump inflection point prediction device provided by the embodiment of the invention obtains the capacities of the battery and the electrode before and after the aging test by establishing the capacity loss model of the battery and the electrode and performing a small amount of aging test experiments, and finishes the calibration of model parameters according to the capacities, so that the aging time nodes with equal capacity values of the battery and the electrode capacity loss model can be determined according to actual working conditions, and further, the battery capacity water jump inflection point of the battery can be accurately predicted, excessive test resources are not needed, the test period is reduced, the test resources and the time cost are saved, the prediction efficiency is improved, and an accurate data basis is provided for solving various potential safety hazards caused by capacity water jump.
Further functional descriptions of the above respective modules are the same as those of the above corresponding method embodiments, and are not repeated here.
There is also provided in accordance with an embodiment of the present invention, an electronic device, as shown in fig. 6, which may include a processor 901 and a memory 902, wherein the processor 901 and the memory 902 may be connected via a bus or otherwise, as exemplified by the bus connection in fig. 6.
The processor 901 may be a central processing unit (Central Processing Unit, CPU). The processor 901 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination thereof.
The memory 902 is used as a non-transitory computer readable storage medium for storing non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the methods in the method embodiments of the present invention. The processor 901 executes various functional applications of the processor and data processing, i.e., implements the methods in the above-described method embodiments, by running non-transitory software programs, instructions, and modules stored in the memory 902.
The memory 902 may include a storage program area and a storage data area, wherein the storage program area may store an operating device, at least one application program required for a function; the storage data area may store data created by the processor 901, and the like. In addition, the memory 902 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 902 optionally includes memory remotely located relative to processor 901, which may be connected to processor 901 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.
One or more modules are stored in the memory 902 that, when executed by the processor 901, perform the methods of the method embodiments described above.
The specific details of the electronic device may be correspondingly understood by referring to the corresponding related descriptions and effects in the above method embodiments, which are not repeated herein.
It will be appreciated by those skilled in the art that implementing all or part of the above-described embodiment method may be implemented by a computer program to instruct related hardware, and the program may be stored in a computer readable storage medium, and the program may include the above-described embodiment method when executed. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a Flash Memory (Flash Memory), a Hard Disk (HDD), or a Solid State Drive (SSD); the storage medium may also comprise a combination of memories of the kind described above.
Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations are within the scope of the invention as defined by the appended claims.
Claims (10)
1. A battery capacity jump inflection point prediction method, comprising:
respectively establishing a battery capacity loss model of a target battery and an electrode capacity loss model of the target battery based on battery capacity influence parameters, wherein the electrode capacity loss model comprises the following components: a positive electrode capacity loss model and/or a negative electrode capacity loss model, the battery capacity influencing parameters including: charging current of the battery, discharging current of the battery, state of charge, and ambient temperature;
performing aging test experiments on the target battery under different battery capacity influence parameters, and calculating to obtain initial battery capacity, battery capacity after aging test, initial electrode capacity and electrode capacity after aging test corresponding to the target battery under different battery capacity influence parameters;
calculating the battery capacity loss model and model parameters corresponding to the electrode capacity loss model respectively based on the initial battery capacity, the battery capacity after the aging test, the initial electrode capacity and the electrode capacity after the aging test;
acquiring a current battery capacity influence parameter of the target battery;
substituting the current battery capacity influence parameter, the initial battery capacity and the initial electrode capacity into the battery capacity loss model and the electrode capacity loss model respectively, and calculating an aging time node corresponding to the aged electrode capacity and the aged battery capacity;
And determining a battery capacity jump inflection point of the target battery based on the aging time node.
2. The method of claim 1, wherein calculating the corresponding aging time node when the aged electrode capacity is equal to the aged battery capacity comprises:
calculating a first aging time node corresponding to the aged positive electrode capacity and the aged battery capacity;
and/or calculating a second aging time node corresponding to the aged negative electrode capacity being equal to the aged battery capacity;
the determining a battery capacity jump inflection point of the target battery based on the aging time node includes:
when the aging time node is the first aging time node or the second aging time node, determining the first aging time node or the second aging time node as a battery capacity jump inflection point of the target battery;
when the aging time node includes: and when the first aging time node and the second aging time node are used, determining the minimum aging time node in the first aging time node and the second aging time node as a battery capacity jump inflection point of the target battery, wherein the first aging time node and the second aging time node are charge and discharge cycle times or battery aging time.
3. The method according to claim 1, wherein the performing the aging test experiment on the target battery under the different battery capacity influence parameters, calculating the initial battery capacity, the battery capacity after the aging test, the initial electrode capacity, the electrode capacity after the aging test, which correspond to the target battery under the different battery capacity influence parameters, includes:
determining a battery electromotive force curve of the target battery before and after the aging test based on the aging test experiment of the target battery under the current battery capacity influence parameter;
based on the discharge cut-off voltage of the target battery and the battery electromotive force curves of the target battery before and after the aging test, respectively determining the initial battery capacity and the battery capacity after the aging test;
calculating the initial electrode capacity and the electrode capacity after the aging test based on battery electromotive force curves of the target battery before and after the aging test;
the calculating the initial electrode capacity and the electrode capacity after the aging test based on the battery electromotive force curves of the target battery before and after the aging test comprises the following steps:
calculating initial negative electrode capacity and negative electrode capacity after aging test based on battery electromotive force curves of the target battery before and after aging test;
And/or calculating initial anode capacity and anode capacity after the aging test based on the battery electromotive force curve of the target battery before and after the aging test, and respectively calculating initial cathode capacity and anode capacity after the aging test based on the battery electromotive force curve of the target battery before and after the aging test, the initial battery capacity, the battery capacity after the aging test, the initial anode capacity and the anode capacity after the aging test.
4. The method of claim 3, wherein calculating the initial anode capacity and the anode capacity after the aging test based on the battery electromotive force curves of the target battery before and after the aging test comprises:
differentiating the battery electromotive force curves of the target battery before and after the aging test to obtain an initial voltage differential curve and an aging voltage differential curve;
calculating a first capacity value of a second voltage platform preset on the initial voltage differential curve of the target battery based on the initial voltage differential curve, and calculating a second capacity value of the second voltage platform preset on the aging voltage differential curve of the target battery based on the aging voltage differential curve;
and calculating the initial anode capacity and the anode capacity after the aging test based on the first capacity value, the second capacity value and the relation between the preset anode capacity and the capacity value of a preset second voltage platform of the target battery on a voltage differential curve.
5. The method of claim 4, wherein calculating the initial positive electrode capacity and the post-aging positive electrode capacity based on the battery electromotive force curve of the target battery before and after the aging test, the initial battery capacity, the post-aging battery capacity, the initial negative electrode capacity, and the post-aging negative electrode capacity, respectively, comprises:
calculating the negative electrode electromotive force curve of the target battery before and after the aging test based on the negative electrode capacity of the target battery before and after the aging test and the negative electrode standard electromotive force curve;
respectively calculating the voltage interval actually used by the anode of the target battery before and after the aging test based on the initial battery capacity, the battery capacity after the aging test, the initial anode capacity, the anode capacity after the aging test, the battery electromotive force curve of the target battery before and after the aging test and the anode electromotive force curve;
based on a voltage interval of the target battery, which is actually used by the positive electrode before and after the aging test, and a positive electrode standard electromotive force curve of the target battery, respectively calculating the relation between the positive electrode capacity and the battery capacity of the target battery before and after the aging test;
and respectively calculating the initial positive electrode capacity and the positive electrode capacity after the aging test based on the initial battery capacity, the battery capacity after the aging test and the relation between the positive electrode capacity and the battery capacity of the target battery before and after the aging test.
6. The method according to claim 5, wherein calculating the voltage interval actually used by the positive electrode of the target battery before and after the aging test based on the initial battery capacity, the battery capacity after the aging test, the initial negative electrode capacity, the negative electrode capacity after the aging test, the battery electromotive force curve of the target battery before and after the aging test, and the negative electrode electromotive force curve, respectively, comprises:
based on a battery electromotive force curve and a negative electrode electromotive force curve of the target battery before and after the aging test, obtaining a positive electrode electromotive force curve of the target battery before and after the aging test;
determining initial positive voltage corresponding to zero positive capacity of the target battery before and after the aging test based on positive electromotive force curves of the target battery before and after the aging test;
inputting the initial battery capacity and the battery capacity after the aging test into the positive electrode electromotive force curve to obtain the corresponding cut-off positive electrode voltage of the target battery before and after the aging test;
and respectively calculating the voltage interval of the target battery in which the positive electrode is actually used before and after the aging test based on the initial positive electrode voltage corresponding to the target battery before and after the aging test when the positive electrode capacity is zero and the cut-off positive electrode voltage corresponding to the target battery before and after the aging test.
7. The method of claim 6, wherein calculating the relationship between the positive electrode capacity and the battery capacity of the target battery before and after the aging test based on the voltage interval actually used by the positive electrode of the target battery before and after the aging test and the positive electrode standard electromotive force curve of the target battery, respectively, comprises:
respectively acquiring a first capacity and a second capacity corresponding to a voltage interval actually used by the anode before and after the aging test on an anode standard electromotive force curve before and after the aging test;
calculating the relation between the positive electrode capacity and the battery capacity of the target battery before the aging test based on the relation between the first capacity and the total capacity corresponding to the positive electrode standard electromotive force curve before the aging test;
and calculating the relation between the positive electrode capacity and the battery capacity of the target battery after the aging test based on the relation between the second capacity and the corresponding total capacity on the positive electrode standard electromotive force curve after the aging test.
8. A battery capacity jump inflection point prediction apparatus, comprising:
the first processing module is used for respectively establishing a battery capacity loss model of a target battery and an electrode capacity loss model of the target battery based on battery capacity influence parameters, wherein the electrode capacity loss model comprises the following components: a positive electrode capacity loss model and/or a negative electrode capacity loss model, the battery capacity influencing parameters including: charging current of the battery, discharging current of the battery, state of charge, and ambient temperature;
The second processing module is used for carrying out aging test experiments on the target battery under different battery capacity influence parameters, and calculating to obtain initial battery capacity and battery capacity after aging test, and initial electrode capacity and electrode capacity after aging test corresponding to the target battery under different battery capacity influence parameters;
the third processing module is used for respectively calculating the battery capacity loss model and model parameters corresponding to the electrode capacity loss model based on the initial battery capacity, the battery capacity after the aging test, the initial electrode capacity and the electrode capacity after the aging test;
a fourth processing module, configured to obtain a current battery capacity influence parameter of the target battery;
a fifth processing module, configured to replace the current battery capacity influencing parameter and the initial battery capacity, the initial electrode capacity, and the battery capacity loss model and the electrode capacity loss model respectively, and calculate an aging time node corresponding to when the aged electrode capacity is equal to the aged battery capacity;
and a sixth processing module, configured to determine a battery capacity jump inflection point of the target battery based on the aging time node.
9. An electronic device, comprising:
a memory and a processor, the memory and the processor being communicatively coupled to each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the method of any of claims 1-7.
10. A computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-7.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102844931A (en) * | 2010-04-13 | 2012-12-26 | 丰田自动车株式会社 | Degradation determination device and degradation determination method for lithium ion secondary battery |
| WO2019165796A1 (en) * | 2018-02-28 | 2019-09-06 | 宁德时代新能源科技股份有限公司 | Battery and method for testing remaining active lithium capacity in negative electrode piece after battery discharging |
| KR20200009920A (en) * | 2018-07-20 | 2020-01-30 | 주식회사 엘지화학 | Apparatus for diagnosing battery |
| CN112327188A (en) * | 2020-09-30 | 2021-02-05 | 北京交通大学 | Model-data hybrid-driven lithium ion battery residual life prediction method |
| CN112327193A (en) * | 2020-10-21 | 2021-02-05 | 北京航空航天大学 | Lithium battery capacity diving early warning method |
| CN112327167A (en) * | 2020-10-21 | 2021-02-05 | 北京航空航天大学 | Battery capacity diving risk assessment method and system |
| CN113030758A (en) * | 2021-03-17 | 2021-06-25 | 重庆长安新能源汽车科技有限公司 | Aging early warning method and system based on lithium ion battery capacity water jump point, automobile and computer storage medium |
| WO2021135921A1 (en) * | 2019-12-31 | 2021-07-08 | 深圳新宙邦科技股份有限公司 | Lithium ion battery |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013247003A (en) * | 2012-05-28 | 2013-12-09 | Sony Corp | Charge control device for secondary battery, charge control method for secondary battery, charged state estimation device for secondary battery, charged state estimation method for secondary battery, deterioration degree estimation device for secondary battery, deterioration degree estimation method for secondary battery, and secondary battery device |
| JP2015230193A (en) * | 2014-06-04 | 2015-12-21 | ソニー株式会社 | Deterioration state estimation device, charge state estimation device, ocv curve calculation/generation device, and electricity storage device |
| EP3690461B1 (en) * | 2018-04-10 | 2022-06-29 | LG Energy Solution, Ltd. | Apparatus and method for diagnosing a battery |
| KR102259415B1 (en) * | 2018-08-29 | 2021-06-01 | 주식회사 엘지에너지솔루션 | Battery management apparatus, battery management method, battery pack and electric vehicle |
-
2021
- 2021-11-30 CN CN202111448034.3A patent/CN114089204B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102844931A (en) * | 2010-04-13 | 2012-12-26 | 丰田自动车株式会社 | Degradation determination device and degradation determination method for lithium ion secondary battery |
| WO2019165796A1 (en) * | 2018-02-28 | 2019-09-06 | 宁德时代新能源科技股份有限公司 | Battery and method for testing remaining active lithium capacity in negative electrode piece after battery discharging |
| KR20200009920A (en) * | 2018-07-20 | 2020-01-30 | 주식회사 엘지화학 | Apparatus for diagnosing battery |
| WO2021135921A1 (en) * | 2019-12-31 | 2021-07-08 | 深圳新宙邦科技股份有限公司 | Lithium ion battery |
| CN112327188A (en) * | 2020-09-30 | 2021-02-05 | 北京交通大学 | Model-data hybrid-driven lithium ion battery residual life prediction method |
| CN112327193A (en) * | 2020-10-21 | 2021-02-05 | 北京航空航天大学 | Lithium battery capacity diving early warning method |
| CN112327167A (en) * | 2020-10-21 | 2021-02-05 | 北京航空航天大学 | Battery capacity diving risk assessment method and system |
| CN113030758A (en) * | 2021-03-17 | 2021-06-25 | 重庆长安新能源汽车科技有限公司 | Aging early warning method and system based on lithium ion battery capacity water jump point, automobile and computer storage medium |
Non-Patent Citations (4)
| Title |
|---|
| An Improved Unscented Particle Filter Method for Remaining Useful Life Prognostic of Lithium-ion Batteries With Li(NiMnCo)O2 Cathode With Capacity Diving;XINWEI CONG;《IEEE Access》;全文 * |
| Use of Effective Capacitance Variation as a Measure of State-of-Health in a Series-Connected Automotive Battery Pack;Peter Leijen;《IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY》;全文 * |
| 三元材料锂离子电池老化诊断、评估与建模方法;高洋;《中国博士学位论文全文数据库》;全文 * |
| 三元电池在储能应用下衰退机理分析;李守涛;《中国优秀硕士学位论文全文数据库》;全文 * |
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