CN116008847A - Online lithium ion battery lithium separation detection method based on vehicle cloud cooperation - Google Patents
Online lithium ion battery lithium separation detection method based on vehicle cloud cooperation Download PDFInfo
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- CN116008847A CN116008847A CN202310082766.8A CN202310082766A CN116008847A CN 116008847 A CN116008847 A CN 116008847A CN 202310082766 A CN202310082766 A CN 202310082766A CN 116008847 A CN116008847 A CN 116008847A
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- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 40
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 40
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 title claims abstract description 35
- 229910001416 lithium ion Inorganic materials 0.000 title claims abstract description 35
- 238000001514 detection method Methods 0.000 title claims abstract description 24
- 238000000926 separation method Methods 0.000 title description 7
- 230000008859 change Effects 0.000 claims abstract description 40
- 238000012360 testing method Methods 0.000 claims abstract description 37
- 238000001556 precipitation Methods 0.000 claims abstract description 25
- 238000010278 pulse charging Methods 0.000 claims abstract description 16
- 238000010606 normalization Methods 0.000 claims abstract description 11
- 238000000034 method Methods 0.000 claims abstract description 10
- 238000012545 processing Methods 0.000 claims abstract description 9
- 238000007600 charging Methods 0.000 claims description 10
- 230000008901 benefit Effects 0.000 abstract description 7
- 230000008569 process Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000002360 explosive Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000003797 telogen phase Effects 0.000 description 1
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
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Abstract
The invention discloses an online lithium ion battery lithium analysis detection method based on vehicle-cloud cooperation, which comprises the following steps: s1, setting a pulse charging working condition for a vehicle-end battery, and collecting a voltage variation and a current variation to obtain a pulse internal resistance curve of the battery to be tested; s2, uploading pulse resistance values and corresponding state of charge data of the test battery to the cloud end by the vehicle end; s3, the cloud end performs normalization processing on the received pulse internal resistance data to obtain a first change curve, and further calculates the slow charge working condition data of the set pulse, and the cloud end performs normalization to obtain a second change curve; and S4, the cloud calculates an average difference according to the relative pulse resistance values of the first change curve and the second change curve, further calculates a root mean square error corresponding to the curve, judges the average difference, and obtains whether lithium precipitation does not occur in the battery. According to the invention, the high efficiency of calculation under the cooperation of vehicle and cloud is utilized, and the method has the advantage of rapid nondestructive online negative electrode lithium precipitation detection on various batteries.
Description
Technical Field
The invention relates to the technical field of lithium ion battery detection, in particular to an online lithium precipitation detection method of a lithium ion battery based on vehicle-cloud cooperation.
Background
With the increasing prominence of national energy and environmental problems, the explosive development of new energy automobiles has become a necessary trend. As a power source of a new energy automobile, a lithium ion battery is widely applied to the industries of electric automobiles and energy storage due to the advantages of the lithium ion battery in energy density, power density, self-discharge rate and the like. In order to meet the rapid charging requirement of an electric automobile, the lithium ion battery needs to ensure the charging safety as much as possible under the condition of ensuring high charging multiplying power. In the application process of the lithium ion battery, some failure phenomena such as shortened cycle life, increased self-discharge rate, poor thermal stability and the like often occur, and even safety problems occur. The lithium separation of the negative electrode is one of the main factors causing the failure and even the safety problems, and in order to improve the safety of the electric automobile, how to accurately and rapidly detect the occurrence of the lithium separation of the battery is very important. However, the existing lithium separation detection method mostly uses naked eyes to observe through disassembling the battery cell, the requirement of online rapid detection cannot be met, and the existing nondestructive online lithium separation detection method cannot be deployed at a vehicle end independently due to complex calculation.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention aims to provide the online lithium-ion battery lithium-ion analysis detection method based on vehicle-cloud cooperation, which utilizes the high calculation efficiency under the vehicle-cloud cooperation and has the advantage of rapid nondestructive online negative electrode lithium-ion detection on various batteries. In order to achieve the above object and other advantages, according to the present invention, there is provided an on-line lithium analysis detection method for a lithium ion battery based on vehicle cloud cooperation, comprising:
s1, setting a pulse charging working condition for a vehicle-end battery, and collecting a voltage variation and a current variation to obtain a pulse internal resistance curve of the battery to be tested;
s2, uploading pulse resistance values and corresponding state of charge data of the test battery to the cloud end by the vehicle end;
s3, the cloud end performs normalization processing on the received pulse internal resistance data to obtain a first change curve, and further calculates the slow charge working condition data of the set pulse, and the cloud end performs normalization to obtain a second change curve;
and S4, the cloud calculates an average difference according to the relative pulse resistance values of the first change curve and the second change curve, further calculates a root mean square error corresponding to the curve, judges the average difference, and obtains whether lithium precipitation does not occur in the battery.
Preferably, in step S4, when the average difference is greater than 0, it is determined that lithium precipitation does not occur inside the lithium ion battery; when the average difference is smaller than 0, the lithium precipitation degree in the battery is obtained according to the comparison between the actual root mean square difference and a preset threshold value; and when the root mean square deviation is smaller than a preset threshold value, lithium precipitation occurs in the test battery, and when the root mean square deviation is larger than the preset threshold value, lithium precipitation occurs in the test battery.
Preferably, the predetermined threshold is any value from 0 to 0.3.
Preferably, each time the battery reaches a preset state of charge, the battery will undergo a rest phase for a predetermined time, after the preset rest time is reached, charging is continued until the next preset state of charge is reached or the voltage reaches an upper limit cut-off voltage.
Preferably, the preset time is an arbitrary value between 1s and 25s, and the variation between the preset states of charge is an arbitrary value between 0.5% and 10%.
Preferably, in step S1, during each intermittent period between charges, the voltage variation dU1, the current variation dI1 and the corresponding battery state of charge SOC1 of the test battery are collected, and the relationship between the pulse internal resistance and the state of charge of the test battery during pulse charging is obtained by using ohm' S law (dU 1/dI 1).
Preferably, in step S3, for the vehicle end data received by the cloud end, normalization processing is performed according to the pulse internal resistance value of the test battery acquired during each pulse charging period, with the resistance value R10 during the first pulse period as a reference, and a first change curve is drawn, where the first change curve is a curve that the relative pulse internal resistance of the test battery changes along with the state of charge.
Compared with the prior art, the invention has the beneficial effects that:
(1) According to the invention, the advantages of real-time performance of vehicle-end data and high cloud computing efficiency are effectively fused by cooperating with the advantages of the two ends of the vehicle cloud, the pulse working condition is set on the vehicle end and the vehicle-end data is further transmitted to the cloud, the speech section utilizes the high computing efficiency to obtain a first change curve and a second change curve, the relative pulse internal resistance curve of the reference battery is calculated under the working condition of testing the battery and slow charging without lithium precipitation, the possible degree of lithium precipitation in the test battery is graded, and the nondestructive online lithium precipitation detection of the lithium ion battery under the cooperation of the vehicle cloud is realized.
(2) According to the invention, the advantages of strong real-time performance of the vehicle end and high cloud computing speed can be fully exerted by the nondestructive online lithium analysis detection of the lithium ion battery under the cooperation of the vehicle cloud, and the charging scheme of the vehicle-end lithium ion battery is optimized through cloud response, so that the service life of the battery is further prolonged.
Drawings
Fig. 1 is a flow chart of a method for detecting online lithium precipitation of a lithium ion battery based on vehicle-cloud cooperation according to the invention;
fig. 2 is a diagram of a vehicle-end pulse charging test result of the on-line lithium precipitation detection method of the lithium ion battery based on vehicle-cloud cooperation according to the invention;
fig. 3 is a graph of a cloud cooperation lithium analysis detection result of the online lithium analysis detection method of the lithium ion battery based on the vehicle cloud cooperation according to the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. 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 be within the scope of the invention.
Referring to fig. 1-3, an online lithium separation detection method of a lithium ion battery based on vehicle cloud cooperation comprises the following steps:
example 1
As shown in FIG. 1, the invention achieves the purpose of detecting lithium precipitation in a battery by comparing the relative pulse internal resistance change curves under two ends of a vehicle cloud, and the specific implementation process comprises steps S1, S2, S3 and S4.
S1 in the embodiment, three lithium ion test batteries with different pulse charging working conditions are arranged in total. Collecting the voltage variation dU of the test battery during each intermittent charging period i Current variation dI i SOC (state of charge) of battery i Using ohm's law dU i /dI i To obtain the relationship between the pulse internal resistance and the state of charge of the test battery during pulse charging.
The step S1 further includes steps S11 to S14, which specifically include the following steps:
s11, setting a rest stage of presetting a preset time every fixed preset state of charge from the preset initial state of charge of the battery, and collecting a voltage variation dU of the test battery during a pulse period i Current variation dI i State of charge SOC i ,
S12 utilizes ohm' S law dU i /dI i Calculating to obtain the relation between the pulse internal resistance and the state of charge of the test battery during the pulse charging period;
s13, recording the charging resistance value of the test battery in each pulse period, and drawing a pulse internal resistance curve of the test battery;
s14, transmitting the pulse internal resistance value of each section of the test battery and the corresponding battery charge state to the cloud.
And S2, uploading the pulse resistance value and the corresponding state of charge data of the test battery to the cloud end by the vehicle end.
S3 in the embodiment, three lithium ion test batteries with different pulse charging working conditions are arranged in total, wherein the pulse working condition of one test battery is shown in FIG. 2. And the cloud performs normalization processing on the received vehicle-end test battery data to obtain a first change curve, a third change curve and a fourth change curve, and calculates slow-charge reference battery pulse data without lithium precipitation to obtain a second change curve.
The step S3 further includes steps S31 to S34, which are specifically as follows:
s31, using the resistance value R of the test battery data received by the vehicle end in the first pulse period i0 Performing normalization processing on pulse internal resistance curves of the test batteries as a reference, and drawing a first change curve, a third change curve and a fourth change curve, wherein the first change curve, the third change curve and the fourth change curve are curves of the relative pulse internal resistances of the different test batteries along with the change of the state of charge;
s32, for the reference battery, starting from a preset initial state of charge, setting a rest stage of preset time every fixed preset state of charge, and collecting the voltage variation dU of the test battery during the pulse 2 Current variation dI 2 State of charge SOC 2 ；
S33 utilizes ohm' S law dU 2 /dI 2 Calculating to obtain the relation between the pulse internal resistance of the reference battery and the state of charge of the battery in the pulse charging period, and drawing a pulse internal resistance curve of the reference battery;
s34 referring to the resistance R during the first pulse of the battery 20 And carrying out normalization processing on the pulse internal resistance curve of the reference battery as a reference, and drawing a second change curve, wherein the second change curve is a curve of the pulse internal resistance of the reference battery changing along with the state of charge.
And S4, the cloud end carries out related calculation on the first change curve, the third change curve, the fourth change curve and the second change curve to obtain the possible degree of lithium precipitation inside the battery of the test battery.
The step S4 further includes steps S41 to S43, which are specifically as follows:
s41 cloud end averages the relative pulse resistance values of the first change curve to obtain x 1 Taking the average value of the relative pulse resistance values of the test batteries to obtain xi, and calculating to obtain the average difference MD i (x 1 -x i )。
S42 by comparing the first change curve with the same charge stateCalculating the resistance of the second variation curve to obtain the Root Mean Square Error (RMSE) of each test battery variation curve and the second variation curve i )。
S43 the average difference MD i And when the lithium ion battery is larger than 0, judging that the first change curve and the second change curve do not generate lithium precipitation in the lithium ion battery. The average difference MD i When the root mean square error RMSE of the fourth variation curve is smaller than 0, judging the root mean square error RMSE of the fourth variation curve 4 And judging that lithium is separated from the lithium ion battery when the lithium ion battery is larger than a preset threshold value.
According to the embodiment of the invention, the pulse charging mode is not limited to constant current pulse charging and intermittent pulse charging based on the nondestructive online lithium analysis detection method under the cooperation of vehicle clouds.
The number of devices and the scale of processing described herein are intended to simplify the description of the invention, and applications, modifications and variations of the invention will be apparent to those skilled in the art.
Although embodiments of the present invention have been disclosed above, it is not limited to the details and embodiments shown and described, it is well suited to various fields of use for which the invention would be readily apparent to those skilled in the art, and accordingly, the invention is not limited to the specific details and illustrations shown and described herein, without departing from the general concepts defined in the claims and their equivalents.
Claims (7)
1. The online lithium precipitation detection method for the lithium ion battery based on the vehicle-cloud cooperation is characterized by comprising the following steps of:
s1, setting a pulse charging working condition for a vehicle-end battery, and collecting a voltage variation and a current variation to obtain a pulse internal resistance curve of the battery to be tested;
s2, uploading pulse resistance values and corresponding state of charge data of the test battery to the cloud end by the vehicle end;
s3, the cloud end performs normalization processing on the received pulse internal resistance data to obtain a first change curve, and further calculates the slow charge working condition data of the set pulse, and the cloud end performs normalization to obtain a second change curve;
and S4, the cloud calculates an average difference according to the relative pulse resistance values of the first change curve and the second change curve, further calculates a root mean square error corresponding to the curve, judges the average difference, and obtains whether lithium precipitation does not occur in the battery.
2. The method for detecting the online lithium precipitation of the lithium ion battery based on the vehicle-cloud cooperation as claimed in claim 1, wherein in the step S4, when the average difference is greater than 0, the lithium precipitation is judged not to occur in the lithium ion battery; when the average difference is smaller than 0, the lithium precipitation degree in the battery is obtained according to the comparison between the actual root mean square difference and a preset threshold value; and when the root mean square deviation is smaller than a preset threshold value, lithium precipitation occurs in the test battery, and when the root mean square deviation is larger than the preset threshold value, lithium precipitation occurs in the test battery.
3. The method for detecting online lithium precipitation of a lithium ion battery based on vehicle-cloud cooperation as claimed in claim 2, wherein the predetermined threshold is an arbitrary value of 0-0.3.
4. The method for online lithium ion battery analysis detection based on vehicle cloud cooperation as claimed in claim 3, wherein the battery is put on a hold period for a predetermined time when the battery reaches a preset state of charge, and charging is continued until the next preset state of charge or the voltage reaches an upper limit cut-off voltage after the preset hold period is reached.
5. The method for online lithium ion battery analysis detection based on vehicle-cloud cooperation as defined in claim 4, wherein the preset time is an arbitrary value between 1s and 25s in a rest stage of the preset time, and the variation between preset states of charge is an arbitrary value between 0.5% and 10%.
6. The method for online lithium analysis detection of a lithium ion battery based on vehicle cloud coordination according to claim 1, wherein in step S1, during each intermittent period between charging, a voltage variation dU1, a current variation dI1 and a corresponding battery state of charge SOC1 of a test battery are collected, and an ohm law (dU 1/dI 1) is utilized to obtain a relationship between the pulse internal resistance and the state of charge of the test battery during pulse charging.
7. The method for online lithium ion battery analysis detection based on vehicle-cloud cooperation as claimed in claim 6, wherein in step S3, for vehicle-end data received by a cloud end, according to pulse internal resistance values of the test battery acquired during each pulse charging period, normalization processing is performed by taking a resistance value R10 during a first pulse period as a reference, and a first change curve is drawn, wherein the first change curve is a curve of relative pulse internal resistance of the test battery changing along with a state of charge.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116338486A (en) * | 2023-05-29 | 2023-06-27 | 宁德时代新能源科技股份有限公司 | Lithium precipitation detection method and device for battery cell, electronic equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116338486A (en) * | 2023-05-29 | 2023-06-27 | 宁德时代新能源科技股份有限公司 | Lithium precipitation detection method and device for battery cell, electronic equipment and storage medium |
| CN116338486B (en) * | 2023-05-29 | 2023-11-03 | 宁德时代新能源科技股份有限公司 | Lithium precipitation detection method and device for battery cell, electronic equipment and storage medium |
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