CN114089204A - 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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- 230000009189 diving Effects 0.000 title claims abstract description 73
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- 238000012360 testing method Methods 0.000 claims abstract description 264
- 238000002474 experimental method Methods 0.000 claims abstract description 26
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- 238000012545 processing Methods 0.000 claims description 20
- 238000007599 discharging Methods 0.000 claims description 12
- 230000000875 corresponding effect Effects 0.000 description 50
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 10
- 229910001416 lithium ion Inorganic materials 0.000 description 10
- 230000006870 function Effects 0.000 description 7
- 238000005562 fading Methods 0.000 description 6
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- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 4
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- 239000011159 matrix material Substances 0.000 description 3
- 238000004458 analytical method 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
- 238000004088 simulation Methods 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 239000010926 waste battery Substances 0.000 description 2
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
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- G—PHYSICS
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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Abstract
The invention provides a method and a device for predicting a battery capacity diving inflection point, 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 the initial battery capacity, the battery capacity after the aging test, the corresponding initial electrode capacity and the electrode capacity after the aging test of the target battery under different battery capacity influence parameters; respectively calculating corresponding model parameters; and respectively substituting the current battery capacity influence parameter, the initial battery capacity and the initial electrode capacity into the model, calculating corresponding aging time nodes when the aged electrode capacity is equal to the aged battery capacity, and further determining the battery capacity diving inflection point of the target battery. By carrying out a small amount of aging test experiments, the battery capacity diving 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 method and a device for predicting a battery capacity diving inflection point.
Background
In recent years, with the rapid development of mobile vehicles such as new energy automobiles and electric (bicycle) vehicles, the performance of lithium ion batteries is more and more emphasized, and especially the service life of the lithium ion batteries is directly related to the use cost of users, the user experience, the recycling of waste batteries and the like. The most ideal situation is that the service life of the lithium ion battery can be infinitely long, thereby not only saving the use cost of users, but also reducing the environmental pressure of the recovery treatment of the waste batteries. However, in a lithium ion battery in reality, not only a capacity fade phenomenon but also a capacity jump phenomenon occurs. The capacity jump means that the battery capacity suddenly deteriorates at a high rate after the battery capacity has deteriorated to a certain extent, and the capacity declines to the end of its life in a short period of time. The volume diving presents unpredictability, which not only seriously influences the user experience, but also causes a certain degree of potential safety hazard, and is a problem which needs to be solved urgently in the industry.
The existing method for predicting the volume diving mainly comprises two methods, namely experimental test and experience estimation. 1. And (3) recording the total cycle number or the accumulated ampere hours of the battery when the capacity diving occurs through an accelerated aging experiment test, and converting the result into the driving mileage through a simulation model, thereby predicting the time of the occurrence of the capacity diving of the battery. The disadvantages of this method are: a large amount of accelerated aging tests are required for each type of battery cell product, the test period is long, a large amount of test resources are occupied, and the test cost and the time cost are high. Most importantly, the capacity fading mechanism under the accelerated aging test working condition is different from the capacity fading mechanism under the actual working condition, which leads to inaccurate prediction results. 2. The second method is usually estimated by experience. For example, it is empirically determined that the capacity diving inflection point of most lithium ion battery systems is about 80% of SOH, and the driving mileage corresponding to the battery life decay rule is calculated, so as to estimate the time of occurrence of the capacity diving. Although the method reduces experimental tests, the deviation between the actual situation and the estimated result is large.
Therefore, how to accurately predict the battery capacity diving inflection point is a problem which needs to be solved urgently.
Disclosure of Invention
In view of this, the embodiment of the invention provides a method and a device for predicting a battery capacity diving inflection point, so as to solve the problems that an estimated result obtained by a method for predicting capacity diving in the prior art has a large deviation from an actual working condition, and the accuracy of the predicted result is low.
The embodiment of the invention provides a method for predicting a battery capacity diving inflection point, 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 steps: a positive electrode capacity loss model and/or a negative electrode capacity loss model, the battery capacity influencing parameters comprising: charging current of the battery, discharging current of the battery, state of charge, ambient temperature;
performing aging test experiments on the target battery under different battery capacity influence parameters, and calculating to obtain the initial battery capacity, the battery capacity after the aging test, the corresponding initial electrode capacity and the electrode capacity after the aging test of the target battery under different battery capacity influence parameters;
respectively calculating model parameters corresponding to the battery capacity loss model and 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;
acquiring current battery capacity influence parameters of the target battery;
respectively 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, and calculating corresponding aging time nodes when the aged electrode capacity is equal to the aged battery capacity;
and determining a battery capacity diving inflection point of the target battery based on the aging time node.
Optionally, the calculating an aging time node corresponding to the aged electrode capacity and the aged battery capacity being equal to each other includes:
calculating a first aging time node corresponding to the aged positive electrode capacity and the aged battery capacity which are equal;
and/or calculating a second aging time node corresponding to the situation that the aged negative electrode capacity is equal to the aged battery capacity.
Optionally, the determining a battery capacity diving 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 diving 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 diving inflection point of the target battery, wherein the first aging time node and the second aging time node are the charge and discharge cycle number or the battery aging time.
Optionally, the performing, by the aging test experiment performed on the target battery under different battery capacity influence parameters, an initial battery capacity, a battery capacity after the aging test, an initial electrode capacity, and an electrode capacity after the aging test of the target battery under different battery capacity influence parameters includes:
determining a battery electromotive force curve of the target battery before and after an aging test based on an aging test experiment of the target battery under the current battery capacity influence parameter;
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;
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 curve 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 the battery electromotive force curves of the target battery before and after aging test;
and/or calculating initial cathode capacity and cathode 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 anode capacity and 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 cathode capacity and the cathode capacity after the aging test.
Optionally, the calculating an initial negative electrode capacity and a negative electrode capacity after the aging test based on the battery electromotive force curve 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 second preset voltage platform of the target battery on the voltage differential curve based on the voltage differential curve and the aging voltage differential curve;
and calculating the initial negative electrode capacity and the negative electrode capacity after the aging test based on the first capacity value and the second capacity value and the relation between the preset negative electrode capacity and the capacity value of the target battery, which is preset on a voltage differential curve, of a second voltage platform.
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 negative electrode electromotive force curve of the target battery before and after aging based on the negative electrode capacity and the negative electrode standard electromotive force curve of the target battery before and after aging test;
respectively calculating the voltage intervals of the target battery, which are actually used by the anode before and after the aging test, based on the initial battery capacity, the battery capacity after the aging test, the initial cathode capacity, the cathode capacity after the aging test, the battery electromotive force curve of the target battery before and after the aging test and the cathode electromotive force curve;
respectively calculating the relation between the anode 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 and the standard electromotive force curve of the anode of the target battery;
and respectively calculating the initial anode capacity and the anode capacity after the aging test based on the initial battery capacity, the battery capacity after the aging test and the relationship between the anode capacity of the target battery before and after the aging test and the battery capacity.
Optionally, the calculating, 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, a voltage interval in which a positive electrode of the target battery is actually used before and after the aging test, respectively includes:
obtaining a positive electrode electromotive force curve of the target battery before and after the aging test based on the battery electromotive force curve and the negative electrode electromotive force curve of the target battery before and after the aging test;
determining corresponding initial positive voltage when the positive electrode 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 the positive electrode electromotive force curve to obtain a corresponding cut-off positive electrode voltage of the target battery before and after the aging test;
and respectively calculating the voltage intervals of the target battery, which are actually used by the anode before and after the aging test, based on the initial anode voltage corresponding to the target battery when the anode capacity is zero before and after the aging test and the cut-off anode voltage corresponding to the target battery before and after the aging test.
Optionally, the calculating, based on the voltage interval of the target battery before and after the aging test in which the positive electrode is actually used and the positive electrode standard electromotive force curve of the target battery, a relationship between the positive electrode capacity and the battery capacity of the target battery before and after the aging test respectively includes:
respectively acquiring a first capacity and a second capacity corresponding to voltage intervals actually used by the anode before and after the aging test on the standard electromotive force curves of the anode 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 anode 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 standard electromotive force curve of the anode after the aging test.
The embodiment of the invention also provides a device for predicting the battery capacity diving inflection point, which comprises the following components:
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, and the electrode capacity loss model comprises the following steps: a positive electrode capacity loss model and/or a negative electrode capacity loss model, the battery capacity influencing parameters comprising: charging current of the battery, discharging current of the battery, state of charge, ambient temperature;
the second processing module is used for performing aging test experiments on the target battery under different battery capacity influence parameters, and calculating to obtain the initial battery capacity, the battery capacity after the aging test, the corresponding initial electrode capacity and the electrode capacity after the aging test of the target battery under different battery capacity influence parameters;
the third processing module is used for respectively calculating model parameters corresponding to the battery capacity loss model and 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;
the fourth processing module is used for acquiring current battery capacity influence parameters of the target battery;
a fifth processing module, configured to substitute the current battery capacity influence parameter and the initial battery capacity, and the initial electrode capacity into the battery capacity loss model and the electrode capacity loss model, respectively, and calculate an aging time node corresponding to the aged electrode capacity and the aged battery capacity being equal to each other;
and the sixth processing module is used for determining a battery capacity diving inflection point of the target battery based on the aging time node.
An embodiment of the present invention further provides an electronic device, including: the device comprises a memory and a processor, wherein the memory and the processor are connected with each other in a communication mode, computer instructions are stored in the memory, 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 enabling a 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 method and a device for predicting a battery capacity diving inflection point, wherein a battery capacity loss model of a target battery and an electrode capacity loss model of the target battery are respectively established based on battery capacity influence parameters, and the electrode capacity loss model comprises the following steps: a positive electrode capacity loss model and/or a negative electrode capacity loss model, the battery capacity influencing parameters comprising: charging current of the battery, discharging current of the battery, state of charge, ambient temperature; performing aging test experiments on the target battery under different battery capacity influence parameters, and calculating to obtain the initial battery capacity, the battery capacity after the aging test, the corresponding initial electrode capacity and the electrode capacity after the aging test of the target battery under different battery capacity influence parameters; respectively calculating model parameters corresponding to the battery capacity loss model and 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; acquiring current battery capacity influence parameters of a target battery; respectively 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, and calculating corresponding aging time nodes when the aged electrode capacity is equal to the aged battery capacity; and determining a battery capacity diving inflection point of the target battery based on the aging time node. Therefore, capacity loss models of the battery and the electrode are established, a small amount of aging test experiments are carried out, the capacity corresponding to the battery and the electrode before and after the aging test is obtained, and the calibration of model parameters is completed, so that aging time nodes with the same capacity value of the capacity loss models of the battery and the electrode can be determined according to actual working conditions, battery capacity diving inflection points of the battery can be accurately predicted, excessive testing resources are not needed, the testing period is shortened, the testing 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 the 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 used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a battery capacity diving inflection point prediction method in an embodiment of the present invention;
FIG. 2 is a schematic diagram of a differential curve obtained by differentiating a battery electromotive force curve according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a working process of the battery capacity diving inflection point prediction in the embodiment of the invention;
FIG. 4 is a diagram illustrating a battery capacity diving inflection point in an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a battery capacity diving inflection point predicting device in an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The technical features mentioned in the different embodiments of the invention described below can be combined with each other as long as they do not conflict with each other.
The existing method for predicting the volume diving mainly comprises two methods, namely experimental test and experience estimation. 1. And (3) recording the total cycle number or the accumulated ampere hours of the battery when the capacity diving occurs through an accelerated aging experiment test, and converting the result into the driving mileage through a simulation model, thereby predicting the time of the occurrence of the capacity diving of the battery. The disadvantages of this method are: a large amount of accelerated aging tests are required for each type of battery cell product, the test period is long, a large amount of test resources are occupied, and the test cost and the time cost are high. Most importantly, the capacity fading mechanism under the accelerated aging test working condition is different from the capacity fading mechanism under the actual working condition, which leads to inaccurate prediction results. 2. The second method is usually estimated by experience. For example, it is empirically determined that the capacity diving inflection point of most lithium ion battery systems is about 80% of SOH, and the driving mileage corresponding to the battery life decay rule is calculated, so as to estimate the time of occurrence of the capacity diving. Although the method reduces experimental tests, the deviation between the actual situation and the estimated result is large.
Based on the above problem, an embodiment of the present invention provides a method for predicting a battery capacity diving inflection point, as shown in fig. 1, the method 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: a positive electrode capacity loss model and/or a negative electrode capacity loss model, the battery capacity influencing parameters comprising: charging current of the battery, discharging current of the battery, state of charge, ambient temperature. In practical application, the battery capacity influence parameter can be adaptively adjusted according to the requirement of the battery capacity diving inflection point prediction accuracy and the influence factor of the battery capacity, and the invention is not limited to this. Each of the above capacity loss models describes a model of the relationship between capacity and the number of charge and discharge cycles or the aging time of the battery.
Step S102: and carrying out aging test experiments on the target battery under different battery capacity influence parameters, and calculating to obtain the initial battery capacity, the battery capacity after the aging test, the corresponding initial electrode capacity and the electrode capacity after the aging test of the target battery under different battery capacity influence parameters.
Specifically, in the embodiment of the present invention, assuming that the influence of each battery capacity influence parameter on each capacity loss model is independent, three different sets of aging test experiments are performed by respectively changing one parameter of the charge/discharge current, the state of charge, and the ambient temperature of the battery and fixing the other two parameters of the charge/discharge current, the state of charge, and the ambient temperature of the target battery under the condition of a fixed number of charge/discharge cycles and a fixed aging time of the battery. In an embodiment of the invention, the initial electrode capacity comprises: an initial positive electrode capacity and/or an initial negative electrode capacity, the electrode capacity after the aging test comprising: positive electrode capacity after aging test and/or negative electrode capacity after aging test.
Step S103: and respectively calculating model parameters corresponding to the battery capacity loss model and 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.
Specifically, each of the capacity loss models may be expressed as a combination of a capacity and a function relationship of each battery capacity influence parameter, i.e., 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 function relationship of each battery capacity influence parameter.
Step S104: and acquiring current battery capacity influence parameters of the target battery.
Specifically, in the embodiment of the present invention, the current battery capacity influence parameter includes: the current charging current, the current state of charge and the current ambient temperature of the target battery are constant. In practical application, the current battery capacity influence parameter may also be adaptively adjusted according to the requirement of the battery capacity diving inflection point prediction accuracy and the influence factor of the battery capacity, which is not limited in the present invention.
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 corresponding aging time nodes when the aged electrode capacity is equal to the aged battery capacity.
Specifically, a first aging time node corresponding to the aged positive electrode capacity and the aged battery capacity which are equal is calculated; and/or calculating a second aging time node corresponding to the situation that the aged negative electrode capacity is equal to the aged battery capacity. The model parameters of each capacity loss model are determined, so that the capacity values corresponding to the capacity loss models with different charging and discharging cycle times or battery aging time can be calculated according to the actual battery capacity influence parameters.
Step S106: and determining a battery capacity diving 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 diving 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 diving inflection point of the target battery, wherein the first aging time node and the second aging time node are the charge-discharge cycle number or the battery aging time.
In order to further improve the accuracy of the final battery capacity diving inflection point prediction result, in the embodiment of the present invention, the electrode capacity loss model is used as a positive electrode capacity loss model and a negative electrode capacity loss model, and the initial electrode capacity includes: initial positive electrode capacity and initial negative electrode capacity, the electrode capacity after aging test including: the positive electrode capacity after the aging test and the negative electrode capacity after the aging test are taken as examples for explanation, in practical application, only a positive electrode capacity loss model or a negative electrode capacity loss model may be adopted, and correspondingly, the initial electrode capacity and the electrode capacity after the aging test may also be selected corresponding to the models, which is not limited in the present invention.
In the actual working condition, when the battery normally runs, the positive electrode capacity and the negative electrode capacity of the battery are larger than the battery capacity, and after the battery generates the capacity diving phenomenon, the positive electrode capacity and the negative electrode capacity of the battery are smaller than the battery capacity, therefore, the aging time node when the battery capacity is equal to the positive electrode capacity or the negative electrode capacity is determined as the battery capacity diving inflection point, so that the actual running working condition of the battery is better met.
When the battery is in an initial state, the capacities of the positive electrode and the negative electrode are both larger than the lithium ion capacity of the battery, but in the subsequent aging process, the fading rate of the capacities of the positive electrode and the negative electrode exceeds the loss rate of the lithium ions, so that the inflection point of capacity jump is obtained. In order to avoid various potential safety hazards caused by capacity diving, in the embodiment of the invention, the aging time node with the higher capacity fading rate in the positive electrode and the negative electrode is determined as the battery capacity diving inflection point. In practical application, the aging time node with the slow capacity decline rate can be determined as the battery capacity diving inflection point without considering the hidden trouble, or the battery capacity diving inflection point is determined by taking an average value of two different aging time nodes and the like, and the difference value of the prediction results of the different battery capacity diving 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 by the method.
By executing the steps, according to the method for predicting the battery capacity diving inflection point, the capacity loss models of the battery and the electrode are established, the capacities corresponding to the battery and the electrode before and after the aging test are obtained by performing a small amount of aging test experiments, and the calibration of the model parameters is completed, so that the aging time nodes with the capacity values equal to each other of the capacity loss models of the battery and the electrode can be determined according to the actual working conditions, the battery capacity diving inflection point of the battery can be accurately predicted, excessive testing resources are not needed, the testing period is shortened, the testing 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.
Specifically, in an embodiment, the step S102 specifically includes:
step S201: and 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.
Specifically, taking the electromotive force curve determination process before the aging test as an example, the target battery can be set to be at a constant current I under the current battery capacity influence parameterchCharging to voltage
Wherein
An upper cutoff voltage for charging the battery system; then switching to constant current 0.05C for charging
Standing for 1 hr after charging, discharging at constant current of 0.1 deg.C to discharge cut-off voltage
And left to stand for 1 hour. The above charging and standing steps were repeated, but the discharge current was once set to 0.2C, 0.3C, 0.5C, 1C. After a series of voltage curves under different discharge multiplying powers are obtained, a voltage curve when the discharge current is constantly equal to 0 is calculated by adopting a regression algorithm, and the voltage curve at the moment is an initial electromotive force curve before the aging test of the battery. The determination process of the electromotive force curve after the aging test is similar to the determination process of the initial electromotive force curve, and details are not repeated here.
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 cut-off voltage on the battery initial electromotive force curve is the initial battery capacity
After aging test experiments, the following results are shown: after calendar aging for 1 month or the like after any storage time or cycle aging for 300 circles or the like after any charge-discharge cycle number, the capacity corresponding to the discharge cut-off voltage on the electromotive force curve corresponding to the battery is the battery capacity after aging test
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, the above step S203 calculates the initial negative electrode capacity and the negative electrode capacity after the aging test based on the battery electromotive force curve 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.
Illustratively, an initial voltage differential curve obtained by performing a differential analysis on an initial electromotive force curve of the battery is shown in fig. 2.
And respectively calculating a first capacity value and a second capacity value of a second preset voltage platform of the target battery on the voltage differential curve based on the voltage differential curve and the aging voltage differential curve.
Exemplarily, fig. 2 illustrates a lithium iron phosphate battery, which shows basic information of a differential curve. Roughly speaking, the differential curve can be divided into 3 regions according to the capacity jump situation, as shown in fig. 2, and labeled as I, II, and III, respectively. Wherein the region II is the predetermined second voltage level.
And calculating the initial negative electrode capacity and the negative electrode capacity after the aging test based on the first capacity value and the second capacity value and the relationship between the preset negative electrode capacity and the capacity value of the target battery, which is preset on the voltage differential curve, of the second voltage platform.
Specifically, as shown in FIG. 2, the width of region II
The capacity of the graphite cathode is positively correlated, and the following conditions are met: initial negative electrode capacity
Wherein δ is the proportion of the second voltage platform of the graphite cathode in the total capacity, δ is a fixed value determined by the material of the target battery, and δ is 0.25 in the case of a lithium iron phosphate battery. Similarly, the capacity of the cathode after the aging test can be calculated by carrying out differential analysis on the electromotive force curve of any aging state and after the aging test
Specifically, in another alternative embodiment, after the initial negative electrode capacity and the negative electrode capacity after the aging test are calculated, the step S203 further calculates the initial positive electrode capacity and the positive electrode capacity after the aging test by respectively calculating the initial positive electrode capacity and the negative 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.
Specifically, a negative electrode electromotive force curve of the target battery before and after aging is calculated 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, which is actually used by the anode before and after the aging test, based on the initial battery capacity, the battery capacity after the aging test, the initial cathode capacity, the cathode capacity after the aging test, the battery electromotive force curve of the target battery before and after the aging test and the cathode electromotive force curve.
Further, obtaining a positive electrode electromotive force curve of the target battery before and after the aging test by using the battery electromotive force curve and the negative electrode electromotive force curve of the target battery before and after the aging test; determining the corresponding initial positive voltage of the target battery when the positive electrode capacity 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 corresponding cut-off positive electrode voltage of the target battery before and after the aging test; and respectively calculating the voltage intervals of the target battery, which are actually used by the anode before and after the aging test, based on the initial anode voltage corresponding to the target battery when the anode capacity is zero before and after the aging test and the cut-off anode voltage corresponding to the target battery before and after the aging test.
Exemplarily, the calculation is obtained in the above steps
And
and then, combining the standard potential curve (obtained by half-cell test) of the graphite cathode, and calculating to obtain the real electrode potential curve of the graphite cathode in the cell
(initial state before burn-in test) and
(any aged state after aging test). According to formulas (1), (2):
calculating to obtain the positive electrode electromotive force curve in the battery
(initial state) and
(any aged state). In the above formulas (1) and (2)
And
showing the electromotive force curves of the battery in the initial state and the aging state, respectively.
Obtaining the potential curve of the anode electrode
(initial state) and
after (any aging state), calculating the voltage interval of the anode actually used in the battery according to the following formulas (3) to (6) by combining the voltage window actually used by the battery and the potential value corresponding to the cathode under the voltage window:
thereby composed of
And
determining the voltage interval of the target battery actually used by the anode before the aging test
And
and determining the voltage interval of the target battery actually used by the anode after the aging test.
And respectively calculating the relation between the anode 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 and the standard electromotive force curve of the anode of the target battery.
Further, respectively acquiring a first capacity and a second capacity corresponding to voltage intervals actually used by the anode before and after the aging test on the standard electromotive force curves of the anode before and after the aging test; calculating the relation between the anode 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 standard electromotive force curve of the anode before the aging test; and calculating the relation between the anode 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 standard electromotive force curve of the anode 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 relationship between the positive electrode capacity of the target battery and the battery capacity before and after the aging test.
Illustratively, taking the initial state as an example, the cut is made on the standard potential curve (which can be obtained by half-cell test) of the positive electrode
Capacity corresponding to voltage interval
Computing
Ratio to total capacity of positive standard potential curve:
initial anode capacity, which is the actual maximum capacity of the anode in the initial state of the battery
Calculated by the following equation (8):
similarly, the actual maximum capacity of the positive electrode in any aging state, i.e., the capacity of the positive electrode after the aging test, can also be calculated
The method for predicting the battery capacity diving inflection point provided by the embodiment of the invention will be described in detail with reference to specific application examples.
Taking the target battery as the lithium ion battery as an example, the aging conditions of the battery include temperature (T), current (I), state of charge soc (x), number of charge and discharge cycles (n), and aging time (T). Wherein the current (I) comprises: a charging current and a discharging current. Provided with lithium ionsThe loss equation is fQ＝f(I,T,x,n,t)，fQThe battery capacity loss model can be expressed by equation (9) as a function of current (I), temperature (T), soc (x), number of cycles (n), and time (T):
assuming that the effects of current (I), temperature (T), soc (x) on capacity loss are independent, equation (9) above can be written as:
f1(I,n,t)、f2(T,n,t)、f3(x, n, t) can be specifically calibrated by designing a matrix aging test.
Let the positive electrode capacity decline equation of the battery be FQ＝F(I,T,x,n,t)，FQThe positive electrode capacity loss model can be expressed by equation (11) as a function of current (I), temperature (T), soc (x), number of cycles (n), and time (T):
assuming that the effects of current (I), temperature (T), soc (x) on capacity loss are independent, equation (11) above can be written as:
F1(I,n,t)、F2(T,n,t)、F3(x, n, t) can be specifically calibrated by designing a matrix aging test.
Let the negative electrode capacity decline equation of the battery be gammaQ＝F(I,T,x,n,t)，ΓQThe negative electrode capacity fade model can be expressed as a function of current (I), temperature (T), soc (x), number of cycles (n), and time (T) 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:
Γ1(I,n,t)、Γ2(T,n,t)、Γ3(x, n, t) can be specifically calibrated by designing a matrix aging test.
The following accelerated aging test experiments were designed, respectively: two cells maintained temperature (T) in the first set of experiments0) And SOC interval (x)0) The same, and the charging and discharging currents are respectively set as I1And I2In which I1And I2The corresponding discharging currents in (1) are the same, and the charging currents are different; two cells maintained temperature (T) in the second set of experiments0) And current (I)0) The same, and the cycle intervals are set to x respectively1And x2(ii) a Two cells held current (I) in the third set of experiments0) And SOC interval (x)0) Same, and the temperatures are respectively set to T1And T2. The test period is set to 300 cycles, and as shown in FIG. 3, three sets of tests are calculated according to the method of the above embodiment
And
and
and
the calculated parameters are shown in table 1.
The parameters in the functions of the above equations (10), (12), (14) are calibrated by using the capacity results in table 1, such as: according to
And
calibrating f in equation (10)1Parameters of (I, n, t), and the like.
Then, the actual aging conditions T, I and x and the basic parameters of the battery cell
Substituting into the formulas (10), (12) and (14) after parameter calibration, and calculating
Or
The number of cycles n or the aging time t, respectively, as shown in fig. 4.
And finally, determining n with smaller cycle number or t with smaller aging time as the capacity diving 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 diving based on the battery capacity loss model, the anode capacity loss model and the cathode capacity loss model, and can accurately predict the time of the capacity diving only by a small amount of cell test data. The method and the device can save testing resources and time cost and improve the accuracy of capacity diving prediction. Meanwhile, compared with an experimental calibration method, the method does not occupy too many test resources, has a short test period, and can save a large amount of test resources and time cost.
By executing the steps, according to the method for predicting the battery capacity diving inflection point, the capacity loss models of the battery and the electrode are established, the capacities corresponding to the battery and the electrode before and after the aging test are obtained by performing a small amount of aging test experiments, and the calibration of the model parameters is completed, so that the aging time nodes with the capacity values equal to each other of the capacity loss models of the battery and the electrode can be determined according to the actual working conditions, the battery capacity diving inflection point of the battery can be accurately predicted, excessive testing resources are not needed, the testing period is shortened, the testing 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.
An embodiment of the present invention further provides a device for predicting a battery capacity diving inflection point, as shown in fig. 5, the device for predicting a battery capacity diving inflection point includes:
a first processing module 101, configured to respectively establish a battery capacity loss model of a 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: a positive electrode capacity loss model and/or a negative electrode capacity loss model, the battery capacity influencing parameters comprising: 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 provided 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 initial battery capacity, battery capacity after the aging test, corresponding initial electrode capacity, and electrode capacity after the aging test of the target battery under different battery capacity influence parameters. For details, refer to the related description of step S102 in the above method embodiment, and no further description is provided here.
And the third processing module 103 is configured to calculate model parameters corresponding to the battery capacity loss model and 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. For details, refer to the related description of step S103 in the above method embodiment, and no further description is provided here.
And the fourth processing module 104 is configured to obtain a current battery capacity influence 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 provided here.
And a fifth processing module 105, configured to substitute 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 calculate an aging time node corresponding to a case where the aged electrode capacity is equal to the aged battery capacity. For details, refer to the related description of step S105 in the above method embodiment, and no further description is provided here.
And a sixth processing module 106, configured to determine a battery capacity diving 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 provided here.
Through the cooperative cooperation of the components, the battery capacity diving 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 models of the battery and the electrode and performing a small amount of aging test experiments, and completes the calibration of model parameters, so that the aging time nodes with equal capacity values of the capacity loss models of the battery and the electrode can be determined according to the actual working conditions, the battery capacity diving 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 diving.
Further functional descriptions of the modules are the same as those of the corresponding method embodiments, and are not repeated herein.
There is also provided an electronic device according to an embodiment of the present invention, as shown in fig. 6, the electronic device may include a processor 901 and a memory 902, where the processor 901 and the memory 902 may be connected by a bus or in another manner, and fig. 6 illustrates an example of a connection by a bus.
The memory 902, which is a non-transitory computer readable storage medium, may be used 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 and data processing of the processor by executing non-transitory software programs, instructions and modules stored in the memory 902, that is, implements the methods in the above-described method embodiments.
The memory 902 may include a storage program area and a storage data area, wherein the storage program area may store an application program required for operating the device, at least one function; the storage data area may store data created by the processor 901, and the like. Further, 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, the memory 902 may optionally include memory located remotely from the processor 901, which may be connected to the 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, which when executed by the processor 901 performs the methods in the above-described method embodiments.
The specific details of the electronic device may be understood by referring to the corresponding related descriptions and effects in the above method embodiments, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, and the program can be stored in a computer readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (10)
1. A battery capacity diving inflection point prediction method is characterized by comprising 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 steps: a positive electrode capacity loss model and/or a negative electrode capacity loss model, the battery capacity influencing parameters comprising: charging current of the battery, discharging current of the battery, state of charge, ambient temperature;
performing aging test experiments on the target battery under different battery capacity influence parameters, and calculating to obtain the initial battery capacity, the battery capacity after the aging test, the corresponding initial electrode capacity and the electrode capacity after the aging test of the target battery under different battery capacity influence parameters;
respectively calculating model parameters corresponding to the battery capacity loss model and 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;
acquiring current battery capacity influence parameters of the target battery;
respectively 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, and calculating corresponding aging time nodes when the aged electrode capacity is equal to the aged battery capacity;
and determining a battery capacity diving inflection point of the target battery based on the aging time node.
2. The method of claim 1, wherein calculating an aging time node corresponding to the aged electrode capacity being 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 which are equal;
and/or calculating a second aging time node corresponding to the situation that the aged cathode capacity is equal to the aged battery capacity;
the determining a battery capacity diving 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 diving 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 diving inflection point of the target battery, wherein the first aging time node and the second aging time node are the charge and discharge cycle number or the battery aging time.
3. The method according to claim 1, wherein the performing an aging test experiment on the target battery under different battery capacity influence parameters to calculate initial battery capacity, battery capacity after the aging test, initial electrode capacity, and electrode capacity after the aging test of the target battery under different battery capacity influence parameters comprises:
determining a battery electromotive force curve of the target battery before and after an aging test based on an aging test experiment of the target battery under the current battery capacity influence parameter;
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;
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;
the calculating the initial electrode capacity and the electrode capacity after the aging test based on the battery electromotive force curve of the target battery before and after the aging test comprises:
calculating initial negative electrode capacity and negative electrode capacity after aging test based on the battery electromotive force curves of the target battery before and after aging test;
and/or calculating initial cathode capacity and cathode 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 anode capacity and 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 cathode capacity and the cathode capacity after the aging test.
4. The method of claim 3, wherein calculating the initial negative electrode capacity and the negative electrode capacity after the aging test based on the battery electromotive force curve 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;
respectively calculating a first capacity value and a second capacity value of a second preset voltage platform of the target battery on the voltage differential curve based on the voltage differential curve and the aging voltage differential curve;
and calculating the initial negative electrode capacity and the negative electrode capacity after the aging test based on the first capacity value and the second capacity value and the relation between the preset negative electrode capacity and the capacity value of the target battery, which is preset on a voltage differential curve, of a second voltage platform.
5. The method of claim 4, wherein the 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 battery capacity after the aging test, the initial negative electrode capacity, and the negative electrode capacity after the aging test, respectively, comprises:
calculating a negative electrode electromotive force curve of the target battery before and after aging based on the negative electrode capacity and the negative electrode standard electromotive force curve of the target battery before and after aging test;
respectively calculating the voltage intervals of the target battery, which are actually used by the anode before and after the aging test, based on the initial battery capacity, the battery capacity after the aging test, the initial cathode capacity, the cathode capacity after the aging test, the battery electromotive force curve of the target battery before and after the aging test and the cathode electromotive force curve;
respectively calculating the relation between the anode 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 and the standard electromotive force curve of the anode of the target battery;
and respectively calculating the initial anode capacity and the anode capacity after the aging test based on the initial battery capacity, the battery capacity after the aging test and the relationship between the anode capacity of the target battery before and after the aging test and the battery capacity.
6. The method according to claim 5, wherein the step of calculating the voltage intervals 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 respectively comprises:
obtaining a positive electrode electromotive force curve of the target battery before and after the aging test based on the battery electromotive force curve and the negative electrode electromotive force curve of the target battery before and after the aging test;
determining corresponding initial positive voltage when the positive electrode 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 the positive electrode electromotive force curve to obtain a corresponding cut-off positive electrode voltage of the target battery before and after the aging test;
and respectively calculating the voltage intervals of the target battery, which are actually used by the anode before and after the aging test, based on the initial anode voltage corresponding to the target battery when the anode capacity is zero before and after the aging test and the cut-off anode voltage corresponding to the target battery before and after the aging test.
7. The method according to claim 6, wherein 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 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 voltage intervals actually used by the anode before and after the aging test on the standard electromotive force curves of the anode 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 anode 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 standard electromotive force curve of the anode after the aging test.
8. A battery capacity diving inflection point predicting device, 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, and the electrode capacity loss model comprises the following steps: a positive electrode capacity loss model and/or a negative electrode capacity loss model, the battery capacity influencing parameters comprising: charging current of the battery, discharging current of the battery, state of charge, ambient temperature;
the second processing module is used for performing aging test experiments on the target battery under different battery capacity influence parameters, and calculating to obtain the initial battery capacity, the battery capacity after the aging test, the corresponding initial electrode capacity and the electrode capacity after the aging test of the target battery under different battery capacity influence parameters;
the third processing module is used for respectively calculating model parameters corresponding to the battery capacity loss model and 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;
the fourth processing module is used for acquiring current battery capacity influence parameters of the target battery;
a fifth processing module, configured to substitute the current battery capacity influence parameter and the initial battery capacity, and the initial electrode capacity into the battery capacity loss model and the electrode capacity loss model, respectively, and calculate an aging time node corresponding to the aged electrode capacity and the aged battery capacity being equal to each other;
and the sixth processing module is used for determining a battery capacity diving 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 performing the method of any of claims 1-7 by executing the computer instructions.
10. A computer-readable storage medium having stored thereon computer instructions for causing a computer to thereby perform the method of any one of claims 1-7.
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