CN114325445A - Lithium ion battery health state rapid evaluation method based on region frequency - Google Patents
Lithium ion battery health state rapid evaluation method based on region frequency Download PDFInfo
- Publication number
- CN114325445A CN114325445A CN202111387849.5A CN202111387849A CN114325445A CN 114325445 A CN114325445 A CN 114325445A CN 202111387849 A CN202111387849 A CN 202111387849A CN 114325445 A CN114325445 A CN 114325445A
- Authority
- CN
- China
- Prior art keywords
- voltage
- battery
- lithium ion
- reg
- frequency
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 title claims abstract description 50
- 229910001416 lithium ion Inorganic materials 0.000 title claims abstract description 50
- 230000036541 health Effects 0.000 title claims abstract description 42
- 238000011156 evaluation Methods 0.000 title claims abstract description 20
- 238000007599 discharging Methods 0.000 claims abstract description 40
- 238000012417 linear regression Methods 0.000 claims abstract description 12
- 230000001419 dependent effect Effects 0.000 claims abstract description 6
- 238000000034 method Methods 0.000 claims description 51
- 238000005070 sampling Methods 0.000 claims description 21
- 230000008569 process Effects 0.000 claims description 19
- 230000003862 health status Effects 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000010354 integration Effects 0.000 claims description 3
- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 description 8
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 7
- 229910052799 carbon Inorganic materials 0.000 description 7
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 6
- 230000032683 aging Effects 0.000 description 6
- 229910052744 lithium Inorganic materials 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 239000006185 dispersion Substances 0.000 description 4
- 238000005259 measurement Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 230000007423 decrease Effects 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 241000651994 Curio Species 0.000 description 1
- QSNQXZYQEIKDPU-UHFFFAOYSA-N [Li].[Fe] Chemical compound [Li].[Fe] QSNQXZYQEIKDPU-UHFFFAOYSA-N 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000001351 cycling effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000013210 evaluation model Methods 0.000 description 1
- 230000002427 irreversible effect Effects 0.000 description 1
- 238000006386 neutralization reaction Methods 0.000 description 1
- 238000004064 recycling Methods 0.000 description 1
- 238000012956 testing procedure Methods 0.000 description 1
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Landscapes
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
- Secondary Cells (AREA)
Abstract
The invention discloses a lithium ion battery health state rapid evaluation method based on region frequency, which comprises the following steps: step 1, charging and discharging a lithium ion battery under a certain multiplying power, collecting battery charging and discharging voltage data, acquiring a working voltage curve, and counting DL of the whole charging and discharging voltage curve; step 2, converting the charge-discharge voltage data into a PDF curve P (x), and searching the voltage V corresponding to the maximum peak of P (x)peak(ii) a Step 3, according to the voltage VpeakSelected area voltage DeltaVregAnd calculating the area voltage DeltaVregThe probability P of (A); step 4, multiplying the probability P and the probability DL to obtain a region frequency F; step 5, establishing a linear regression equation by taking the region frequency F as an independent variable and the battery SOH as a dependent variable; and 6, replacing the lithium ion sample battery to be detected, repeating the steps 1 to 4, substituting the frequency F 'of the region to be detected into a linear regression equation, calculating the SOH value of the battery corresponding to the frequency F' of the region to be detected, and realizing the SOH evaluation of the lithium ion sample battery to be detected.
Description
Technical Field
The invention relates to the technical field of battery health state evaluation, in particular to a lithium ion battery health state rapid evaluation method based on region frequency.
Background
China is now the world's largest carbon emitting country. By the end of 2020, the total carbon emission of China is 98.99 hundred million tons, which accounts for 30.7 percent of the total carbon emission of the whole world. Where transportation accounts for 15% of the chinese end-use carbon emissions, has grown at an average rate of about 5% per year over the last 9 years. To achieve the goal of "carbon peak by 2030 and carbon neutralization by 2060", the chinese government has energetically driven traffic electrification, which is considered to be an effective way to significantly reduce carbon emissions. By the end of 2021, 6 months, 603 million new energy vehicles exist in China, wherein 493 ten thousand pure electric vehicles exist. And the power source of the electric vehicle is usually a high-performance lithium ion battery. However, the life of lithium ion batteries is not infinite due to the gradual aging of the battery during charge and discharge cycles. Irreversible and inevitable internal reactions during cycling reduce the performance of the battery, such as a decrease in capacity and an increase in resistance. If the State of Health (SOH) of the battery cannot be detected in time, the safety and reliability of the entire battery system cannot be guaranteed.
The SOH of the battery is evaluated in many ways. The traditional method is based on capacity calibration and pulse step internal resistance measurement. Although the test is accurate, the method consumes long time, and the normal commercial operation of the lithium ion battery needs to be stopped, and the capacity calibration and the pulse step internal resistance measurement are specially carried out, so that the use benefit of the lithium ion battery is influenced. If battery parameters such as temperature, voltage, current and the like collected by a Battery Management System (BMS) during normal operation of a battery can be utilized, and health factors reflecting the SOH of the battery are extracted through changes thereof, the online evaluation of the state of health of the lithium battery can be realized. At present, there are some reports on online evaluation of lithium battery health status.
Chinese patent CN 111458649 a (a method for rapidly detecting health degree of battery module) discloses a method for rapidly evaluating actual capacity of a battery module, which includes obtaining platform voltage data of a battery module sample with known available capacity during charging and discharging, converting the platform voltage data into a Probability Density Function (PDF) curve, calculating peak area of a set voltage interval, and fitting to establish a regression equation of state of health SOH and peak area of the battery module. In the detection process, only platform voltage data in the charging and discharging process of the battery module to be detected are acquired, peak areas are calculated according to the same steps, and the peak areas are substituted into a regression equation, so that the SOH of the battery module to be detected can be obtained. However, the method is sensitive to the sampling frequency, and the sampling frequency above 1Hz is often needed to ensure that model evaluation is not distorted, but the high sampling frequency is difficult to ensure in actual working conditions.
Chinese patent CN 111948546 a (a lithium battery health state evaluation method and system) discloses a lithium battery health state evaluation method, which finds the maximum peak height amplitude degree Δ H of the Incremental Capacity (ICA) curve during the charging and discharging process of the batterymaxLinear relation exists between the maximum peak height and the maximum peak height amplitude degree delta H corresponding to the sample battery under the state of health valuemaxPlotting Δ Hmax-SOH fitting curve, collecting voltage curve in the charging and discharging process of the battery to be detected to calculate maximum peak height amplitude Delta H in ICA curvemaxValue according to Δ HmaxThe SOH value of the battery to be detected can be obtained by fitting a curve to the SOH. However, this approach also has certain limitations. When a voltage plateau occurs during a charge and discharge test, the resolution of the voltage measurement may not be sufficient to distinguish between voltage differences. This may result in dV ═ 0, especially phosphorusThe voltage curve of the iron lithium battery is flatter. Even if the measurement accuracy is acceptable, the noise can be reduced, but this results in a high cost of the sampling module of the BMS.
Chinese patent CN 108490366 a (a method for quickly evaluating the health status of an electric vehicle retired battery module) discloses a method for quickly evaluating the health status of an electric vehicle retired battery module, which compares the state of health SOH of a battery with the Lorenz dispersion of voltage during discharging, and analyzes the correlation between the SOH and the Lorenz dispersion of voltage during discharging, and because the working voltage during charging and discharging of the battery can be collected in real time according to a battery management system, no extra collection is needed, and no workload is increased; the collected working voltage is only in a corresponding state of charge (SOC) interval, and a certain SOC value is not limited, so that the operation is more convenient; the calculation of the Lorenz dispersion of the voltage is based on the result of voltage averaging in the SOC interval, and the result is more accurate. The SOH of the retired battery of the electric vehicle can be quickly evaluated only by calculating the Lorenz dispersion of the voltage in the discharging process of the retired battery module to be tested, so that the consistency can be quickly sorted, and the simple, convenient and low-cost target of recycling the retired battery can be achieved. However, the working voltage of the battery sample in the model and the working voltage of the battery to be tested need to be in the same SOC interval. Since the SOC value is a dynamically changing value, the SOC value estimated by the battery management system may deviate from the true value with changes in the environment and the operating conditions, which brings uncertainty to the rapid estimation of the state of health of the battery module.
Disclosure of Invention
The present invention is made to solve the above problems, and an object of the present invention is to provide a method for rapidly evaluating the state of health of a lithium ion battery based on a region frequency.
The invention provides a lithium ion battery health state rapid evaluation method based on region frequency, which is characterized by comprising the following steps: step 1, charging and discharging a lithium ion battery under a certain multiplying power, collecting battery charging and discharging voltage data, acquiring a working voltage curve, and counting the number of voltage sampling points of the whole charging and discharging voltage curve; step 2, charging and discharging voltage dataConverting into PDF curve P (x), and searching voltage V corresponding to maximum peak of PDF curve P (x)peak(ii) a Step 3, according to the voltage VpeakSelected area voltage DeltaVregAnd calculating the area voltage DeltaVregThe probability P of (A); step 4, multiplying the probability P by the frequency of the voltage sampling point to obtain an area frequency F; step 5, establishing a linear regression equation by taking the region frequency F as an independent variable and the battery SOH as a dependent variable; step 6, replacing the lithium ion sample battery to be detected, repeating the steps 1 to 4, collecting charge and discharge voltage data of the lithium ion sample battery to be detected under the same multiplying power, and obtaining the regional voltage delta V of the lithium ion sample battery to be detectedregAnd substituting the corresponding frequency F 'of the region to be detected as a health factor index into the linear regression equation, and calculating the SOH value of the battery corresponding to the frequency F' of the region to be detected so as to realize the SOH evaluation of the battery of the lithium ion sample battery to be detected.
In the method for rapidly evaluating the state of health of the lithium ion battery based on the region frequency, provided by the invention, the method can also have the following characteristics: in the step 1, the battery charging and discharging voltage data is acquired automatically by a battery management system.
In the method for rapidly evaluating the state of health of the lithium ion battery based on the region frequency, provided by the invention, the method can also have the following characteristics: in the step 2, a ksDensity function in a Matlab statistical tool box is used for converting the charging and discharging voltage data into a PDF curve P (x), and the specific process is as follows:
x [ ], wherein charge and discharge voltage data is introduced into [ ];
[f,xi]ksDensity (x), wherein [ f, x [ ]i]Is the result form displayed by MATLAB calculation, and f is the numerical value of probability density, xiIs the corresponding abscissa (voltage value).
In the method for rapidly evaluating the state of health of the lithium ion battery based on the region frequency, provided by the invention, the method can also have the following characteristics: wherein, in step 3, the area voltage Δ V is calculatedregThe specific process of probability P of (a) includes the following substeps: step 3-1, using voltage V in PDF curve P (x)peakIs a center, a left part and a right partRespectively selecting a certain value to obtain a region voltage delta Vreg(ii) a Step 3-2, the PDF curve P (x) is subjected to area voltage delta VregPerforming internal fixed integration to obtain area voltage delta VregA fixed integral value of (2); step 3-3, taking the area voltage delta VregThe ratio of the constant integral value of (a) to the constant integral value of the entire PDF curve P (x) is the area voltage DeltaVregThe probability P of (a).
Action and Effect of the invention
According to the lithium ion battery health state rapid evaluation method based on the region frequency, firstly, a lithium ion battery is charged and discharged under a certain multiplying power, battery charging and discharging voltage data are collected, a working voltage curve is obtained, and the number of voltage sampling points of the whole charging and discharging voltage curve is counted; then converting the charge-discharge voltage data into a PDF curve P (x), and searching the voltage V corresponding to the maximum peak of the PDF curve P (x)peak(ii) a Then according to the voltage VpeakSelected area voltage DeltaVregAnd calculating the area voltage DeltaVregThe probability P of (A); multiplying the probability P by the number of times of voltage sampling points to obtain an area frequency F; then, establishing a linear regression equation by taking the region frequency F as an independent variable and the battery SOH as a dependent variable; finally, replacing the lithium ion sample battery to be detected, repeating the steps, collecting the charge-discharge voltage data of the lithium ion sample battery to be detected under the same multiplying power, and obtaining the regional voltage delta V of the lithium ion sample battery to be detectedregAnd substituting the corresponding frequency F 'of the region to be detected as a health factor index into the linear regression equation, and calculating the SOH value of the battery corresponding to the frequency F' of the region to be detected so as to realize the SOH evaluation of the battery of the lithium ion sample battery to be detected.
In the above-mentioned process of this embodiment, the operating voltage in the battery charge-discharge process can be gathered by battery management system in real time, need not carry out the off-line experiment to the lithium cell, need not additionally gather, reduces work load. Compared with the traditional PDF method which directly uses the peak height of PDF as a health factor, the method is insensitive to the sampling frequency, can still obtain high precision under the condition of low sampling frequency (such as 1/60Hz, namely sampling once in 1 minute), is simple in calculation, does not need filtering, and is suitable for real-time online SOH estimation.
Drawings
FIG. 1 is a flow chart of a method for battery state of health assessment based on region frequency in an embodiment of the present invention;
fig. 2 is a charging and discharging curve of the lithium iron phosphate module under the 1/3C capacity calibration condition in the embodiment of the invention;
fig. 3 is a diagram illustrating a result of a method for estimating a state of health of a battery based on a region frequency according to an embodiment of the present invention.
Detailed Description
In order to make the technical means, the creation features, the achievement objectives and the efficacy of the present invention easy to understand, the following embodiments specifically describe the method for rapidly evaluating the health status of the lithium ion battery based on the region frequency in combination with the accompanying drawings.
In this embodiment, a method for rapidly evaluating the state of health of a lithium ion battery based on a region frequency is provided.
Fig. 1 is a flowchart of a battery state of health evaluation method based on region frequency in the present embodiment.
As shown in fig. 1, the method for rapidly evaluating the state of health of a lithium ion battery based on a region frequency according to the present embodiment includes the following steps:
and step S1, charging and discharging the lithium ion battery under a certain multiplying power, collecting the charging and discharging voltage data of the battery, acquiring a working voltage curve, and counting the voltage sampling point times of the whole charging and discharging voltage curve.
In this embodiment, 8 lithium iron phosphate battery modules (15P4S, 15 in parallel and 4 in series) retired on a curio S18B electric vehicle are adopted, the nominal capacity is 40Ah, and the lithium iron phosphate battery modules are formed by connecting 4 15P1S battery units in series. The rated voltage of the 15P1S battery unit is 3.2V, and the rated voltage of the whole module is 12.8V.
Table 1 shows the charge and discharge testing procedure of the iron phosphate lithium battery module in this embodiment.
TABLE 1
As shown in table 1, the lithium iron phosphate modules were rapidly aged at a 2C (80A) rate. In the circulation process, a standing time of 30min is set between charging and discharging. The cell is cycled until its capacity drops below 60% SOH. After 50 times of aging, the capacity calibration was performed at 1/3C (13.3A) to obtain the residual capacity and the charge-discharge voltage curve, and the SOH thereof was calculated,
the battery test system voltage sampling frequency used in this example was 1/60 Hz.
Fig. 2 is a charging and discharging curve of the lithium iron phosphate module in this embodiment under the 1/3C capacity calibration condition.
As shown in fig. 2, as the aging period increases, the module charging/discharging platform becomes shorter, and the SOH of the display module becomes smaller. The plateau potential of the charging curve increases with the increase of the aging period, and the plateau potential of the discharging curve decreases with the increase of the aging period, which indicates that the aging causes the increase of the internal resistance of the battery.
Table 2 shows the available capacity and SOH value of the lithium iron phosphate module in different cycle periods under the 1/3C calibration condition in this example.
As shown in table 2, the lithium iron phosphate battery module was aged in a 2C cycle protocol with 0-100% SOC, completing 400 charge and discharge cycles, and the SOH decayed from 96.7% to 55.73%.
Fig. 3 is a diagram illustrating the result of the battery state of health estimation method based on the region frequency in the present embodiment.
Fig. 3(a) is a battery charging graph in the present embodiment.
As shown in fig. 3(a), the data length DL of the charging curve is 176.
Step S2, charging and dischargingConverting the voltage data into a PDF curve P (x), and searching the voltage V corresponding to the maximum peak of the PDF curve P (x)peak。
Fig. 3(b) is a PDF plot in this embodiment.
As shown in fig. 3(b), the searched maximum PDF peak coordinate is (13.49, 2.326).
Step S3, according to the voltage VpeakSelected area voltage DeltaVregAnd calculating the area voltage DeltaVregThe probability P of (a). The specific implementation is divided into the following substeps:
FIG. 3(c) is a diagram showing the selected area voltage Δ V in this embodimentregSchematic representation of (a).
Step S3-1, using voltage V in PDF curve P (x)peakSelecting a certain value from the left and right sides to obtain a region voltage delta Vreg。
As shown in FIG. 3(c), the area voltage Δ V is takenregIs 200mV, expressed as VpeakWhen the voltage V is set to be about 13.49V and 100mV is applied to the left and right sides, the starting voltage V of the domain voltage is set to be V013.39V, end voltage Vt＝13.59V。
Step S3-2, applying the PDF curve P (x) to the area voltage Δ VregPerforming internal fixed integration to obtain area voltage delta VregThe fixed integral value of (2).
Step S3-3, taking area voltage delta VregThe ratio of the constant integral value of (a) to the constant integral value of the entire PDF curve P (x) is the area voltage DeltaVregThe probability P of (a).
As shown in FIG. 3(c), the termination voltage V is due to the entire PDF curveendTo an initial voltage VstartThe definite integral of (a) is 1. In practice, then the fixed integral of the PDF curve P (x) in the area voltage is exactly the probability P that the voltage falls within that area voltage. Fig. 3(c) shows that the probability P of the area voltage is 0.4036.
Step S4, multiplying the probability P by the frequency of voltage sampling points to obtain an area frequency F;
fig. 3(d) is a schematic diagram of calculating the area frequency F in this embodiment.
As shown in fig. 3(d), the region frequency F is 71.03.
And step S5, establishing a rapid evaluation model, taking the region frequency F of the charging and discharging process in 8 circulation periods of the sample battery as an independent variable, taking the SOH value of the 8 circulation periods of the sample battery as a dependent variable, and establishing a linear regression equation.
Fig. 3(e) is a fitted curve of the block frequency and SOH at a block voltage of 200mV during charging and discharging in this example. The left side is the charging process and the right side is the discharging process.
Fitting R of the charging procedure, as shown in FIG. 3(e)2Is 0.9254. Fitting of discharge process R2Is 0.9560. The fitting precision is better in the charging and discharging process.
Step S6, replacing the lithium ion sample battery to be tested, repeating the steps 1-4, and obtaining the area voltage delta V of the lithium ion sample battery to be tested under the condition that the charging and discharging voltage data of the lithium ion sample battery to be tested under the same multiplying power are acquiredregAnd substituting the corresponding frequency F 'of the region to be detected as a health factor index into the linear regression equation, and calculating the SOH value of the battery corresponding to the frequency F' of the region to be detected so as to realize the SOH evaluation of the battery of the lithium ion sample battery to be detected.
Effects and effects of the embodiments
According to the method for rapidly evaluating the health state of the lithium ion battery based on the region frequency, firstly, the lithium ion battery is charged and discharged under a certain multiplying power, the charging and discharging voltage data of the battery are collected, a working voltage curve is obtained, and the frequency of voltage sampling points of the whole charging and discharging voltage curve is counted; then converting the charge-discharge voltage data into a PDF curve P (x), and searching the voltage V corresponding to the maximum peak of the PDF curve P (x)peak(ii) a Then according to the voltage VpeakSelected area voltage DeltaVregAnd calculating the area voltage DeltaVregThe probability P of (A); multiplying the probability P by the number of times of voltage sampling points to obtain an area frequency F; then, establishing a linear regression equation by taking the region frequency F as an independent variable and the battery health state as a dependent variable; finally, the lithium ion sample battery to be detected is replaced, the steps are repeated, the charging and discharging voltage data of the lithium ion sample battery to be detected under the same multiplying power are collected, and the lithium ion to be detected is obtainedSub-sample cell in-region voltage Δ VregAnd substituting the corresponding to-be-detected region frequency F 'as a health factor index into the linear regression equation, and calculating the battery health state value corresponding to the to-be-detected region frequency F', so as to realize the battery health state evaluation of the to-be-detected lithium ion sample battery.
In the above-mentioned process of this embodiment, the operating voltage in the battery charge-discharge process can be gathered by battery management system in real time, need not carry out the off-line experiment to the lithium cell, need not additionally gather, does not increase work load. Compared with the traditional PDF method which directly uses the peak height of PDF as a health factor, the method is insensitive to the sampling frequency, can still obtain high precision under the condition of low sampling frequency (such as 1/60Hz, namely sampling once in 1 minute), is simple in calculation, does not need filtering, and is suitable for real-time online SOH estimation.
The above embodiments are preferred examples of the present invention, and are not intended to limit the scope of the present invention.
Claims (4)
1. A lithium ion battery health state rapid evaluation method based on region frequency is characterized by comprising the following steps:
step 1, charging and discharging a lithium ion battery under a certain multiplying power, collecting battery charging and discharging voltage data, acquiring a working voltage curve, and counting the number of voltage sampling points of the whole charging and discharging voltage curve;
step 2, converting the charge and discharge voltage data into a probability density function curve P (x), and searching a voltage V corresponding to the maximum peak of the probability density function curve P (x)peak；
Step 3, according to the voltage VpeakSelected area voltage DeltaVregAnd calculating the area voltage DeltaVregThe probability P of (A);
step 4, multiplying the probability P and the frequency of the voltage sampling points to obtain an area frequency F;
step 5, establishing a linear regression equation by taking the region frequency F as an independent variable and the battery health state as a dependent variable;
step 6, replacing the lithium ions to be detectedAnd (4) repeating the steps 1 to 4, collecting the charge-discharge voltage data of the lithium ion sample battery to be tested under the same multiplying power, and obtaining the voltage delta V of the lithium ion sample battery to be tested in the regionregAnd substituting the corresponding to-be-detected region frequency F 'as a health factor index into the linear regression equation, and calculating a battery health state value corresponding to the to-be-detected region frequency F', so as to realize the battery health state evaluation of the to-be-detected lithium ion sample battery.
2. The method for rapidly evaluating the health status of a lithium ion battery according to claim 1, wherein the method comprises the following steps:
in the step 1, the battery charging and discharging voltage data is acquired automatically by a battery management system.
3. The method for rapidly evaluating the health status of a lithium ion battery according to claim 1, wherein the method comprises the following steps:
in the step 2, a ksDensity function in a Matlab statistical tool box is used for converting the charging and discharging voltage data into a probability density function curve P (x), and the specific process is as follows:
x＝[]，
wherein the charge-discharge voltage data is introduced into [ ];
[f,xi]＝ksdensity(x)，
wherein [ f, x [ ]i]Is the result form displayed by MATLAB calculation, and f is the numerical value of probability density, xiIs the corresponding abscissa (voltage value).
4. The method for rapidly evaluating the health status of a lithium ion battery according to claim 1, wherein the method comprises the following steps:
wherein, in step 3, the area voltage Δ V is calculatedregThe specific process of probability P of (a) includes the following substeps:
step 3-1, using the voltage V in the probability density function curve P (x)peakSelecting a certain value from the left and right sides to obtain a region voltage delta Vreg；
Step 3-2, the voltage delta V of the probability density function curve P (x) in the regionregPerforming internal fixed integration to obtain area voltage delta VregA fixed integral value of (2);
step 3-3, taking the area voltage delta VregThe ratio of the constant integral value of (a) to the constant integral value of the entire probability density function curve P (x) is the area voltage DeltaVregThe probability P of (a).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111387849.5A CN114325445B (en) | 2021-11-22 | 2021-11-22 | Regional frequency-based lithium ion battery health state rapid assessment method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111387849.5A CN114325445B (en) | 2021-11-22 | 2021-11-22 | Regional frequency-based lithium ion battery health state rapid assessment method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114325445A true CN114325445A (en) | 2022-04-12 |
| CN114325445B CN114325445B (en) | 2023-12-22 |
Family
ID=81045768
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111387849.5A Active CN114325445B (en) | 2021-11-22 | 2021-11-22 | Regional frequency-based lithium ion battery health state rapid assessment method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114325445B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115144779A (en) * | 2022-06-10 | 2022-10-04 | 中国第一汽车股份有限公司 | Battery state determination method, device, vehicle and storage medium |
| CN117930064A (en) * | 2024-03-21 | 2024-04-26 | 四川新能源汽车创新中心有限公司 | Method, system, computing equipment and medium for nondestructive testing lithium precipitation |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103605078A (en) * | 2013-11-12 | 2014-02-26 | 清华大学 | Performance test method for power battery or battery packet of hybrid power vehicle |
| CN103675702A (en) * | 2013-12-04 | 2014-03-26 | 清华大学 | Method for evaluating state of health battery in real time |
| US20160245876A1 (en) * | 2013-10-01 | 2016-08-25 | Centre National De La Recherche Scientifique | Method and apparatus for evaluating the state of health of a lithium battery |
| CN108896913A (en) * | 2018-05-10 | 2018-11-27 | 燕山大学 | A kind of evaluation method of health state of lithium ion battery |
| FR3074736A1 (en) * | 2017-12-11 | 2019-06-14 | Commissariat A L'energie Atomique Et Aux Energies Alternatives | METHOD FOR ESTIMATING THE HEALTH STATUS OF A FUEL CELL FROM MEASUREMENTS IN REAL USAGE |
| WO2019162749A1 (en) * | 2017-12-07 | 2019-08-29 | Yazami Ip Pte. Ltd. | Method and system for online assessing state of health of a battery |
| CN111458649A (en) * | 2020-04-23 | 2020-07-28 | 国网陕西省电力公司汉中供电公司 | Rapid detection method for health degree of battery module |
| CN111856307A (en) * | 2020-07-09 | 2020-10-30 | 深圳市欣旺达综合能源服务有限公司 | Method and device for estimating SoH of battery, storage medium and intelligent equipment |
| CN111948546A (en) * | 2020-08-07 | 2020-11-17 | 国网陕西省电力公司汉中供电公司 | Lithium battery health degree evaluation method and system |
| CN112485693A (en) * | 2020-11-19 | 2021-03-12 | 上海电力大学 | Battery health state rapid evaluation method based on temperature probability density function |
-
2021
- 2021-11-22 CN CN202111387849.5A patent/CN114325445B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160245876A1 (en) * | 2013-10-01 | 2016-08-25 | Centre National De La Recherche Scientifique | Method and apparatus for evaluating the state of health of a lithium battery |
| CN103605078A (en) * | 2013-11-12 | 2014-02-26 | 清华大学 | Performance test method for power battery or battery packet of hybrid power vehicle |
| CN103675702A (en) * | 2013-12-04 | 2014-03-26 | 清华大学 | Method for evaluating state of health battery in real time |
| WO2019162749A1 (en) * | 2017-12-07 | 2019-08-29 | Yazami Ip Pte. Ltd. | Method and system for online assessing state of health of a battery |
| FR3074736A1 (en) * | 2017-12-11 | 2019-06-14 | Commissariat A L'energie Atomique Et Aux Energies Alternatives | METHOD FOR ESTIMATING THE HEALTH STATUS OF A FUEL CELL FROM MEASUREMENTS IN REAL USAGE |
| CN108896913A (en) * | 2018-05-10 | 2018-11-27 | 燕山大学 | A kind of evaluation method of health state of lithium ion battery |
| CN111458649A (en) * | 2020-04-23 | 2020-07-28 | 国网陕西省电力公司汉中供电公司 | Rapid detection method for health degree of battery module |
| CN111856307A (en) * | 2020-07-09 | 2020-10-30 | 深圳市欣旺达综合能源服务有限公司 | Method and device for estimating SoH of battery, storage medium and intelligent equipment |
| CN111948546A (en) * | 2020-08-07 | 2020-11-17 | 国网陕西省电力公司汉中供电公司 | Lithium battery health degree evaluation method and system |
| CN112485693A (en) * | 2020-11-19 | 2021-03-12 | 上海电力大学 | Battery health state rapid evaluation method based on temperature probability density function |
Non-Patent Citations (2)
| Title |
|---|
| 刘大同;周建宝;郭力萌;彭宇;: "锂离子电池健康评估和寿命预测综述", 仪器仪表学报, no. 01 * |
| 李程;李新周;刘俊华;韩军林;刘继涛;吕思濛;廖强强: "基于概率密度分析法快速筛选退役电池模组", 电工技术, no. 001 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115144779A (en) * | 2022-06-10 | 2022-10-04 | 中国第一汽车股份有限公司 | Battery state determination method, device, vehicle and storage medium |
| CN117930064A (en) * | 2024-03-21 | 2024-04-26 | 四川新能源汽车创新中心有限公司 | Method, system, computing equipment and medium for nondestructive testing lithium precipitation |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114325445B (en) | 2023-12-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103675702B (en) | A kind of method of real-time assessment cell health state | |
| CN108445406B (en) | Power battery state of health estimation method | |
| CN109870659A (en) | Using the health state of lithium ion battery evaluation method of sliding window optimizing strategy | |
| CN102468521B (en) | Method and apparatus for assessing battery state of health | |
| CN102565710B (en) | Method and apparatus for assessing battery state of health | |
| CN108490366B (en) | Rapid assessment method for state of health of electric automobile retired battery module | |
| CN114325445A (en) | Lithium ion battery health state rapid evaluation method based on region frequency | |
| CN111398833A (en) | Battery health state evaluation method and evaluation system | |
| CN111948546A (en) | Lithium battery health degree evaluation method and system | |
| CN105093131A (en) | Battery health characteristic parameter extracting method for echelon use of lithium iron phosphate battery | |
| CN106154164B (en) | Battery health state assessment method | |
| CN106772104B (en) | A kind of power battery SOC value evaluation method | |
| CN112485693B (en) | Battery health state rapid evaluation method based on temperature probability density function | |
| CN111458649A (en) | Rapid detection method for health degree of battery module | |
| CN114430080A (en) | Power battery cell abnormal self-discharge identification method based on operation data | |
| CN111064253A (en) | Battery health degree rapid evaluation method based on average discrete Frechet distance | |
| CN113109729B (en) | Vehicle power battery SOH evaluation method based on accelerated aging test and real vehicle working condition | |
| CN111366864B (en) | Battery SOH on-line estimation method based on fixed voltage rise interval | |
| CN106597288A (en) | Power supply SOC estimation method | |
| CN105572601A (en) | Judgment method for the reason of lithium battery performance degradation | |
| CN103529397A (en) | Method for estimating electric quantity of battery and system for managing electric quantity of battery | |
| CN111123136A (en) | Online measuring device and online measuring method for health state of lithium ion battery | |
| CN106405424B (en) | Method and device for metering residual electric quantity of lithium ion battery | |
| CN112363061A (en) | Thermal runaway risk assessment method based on big data | |
| CN106599333A (en) | Method for estimating SOH of power supply |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20231123 Address after: Zone B on the east side of Building 2, No. 416 Haiyou Avenue, Binhai High tech Zone, Binhai New Area, Tianjin, 300450 Applicant after: Tianjin Saidmei New Energy Technology Co.,Ltd. Address before: No. 1851, Hucheng Ring Road, Yangpu District, Shanghai 200090 Applicant before: Shanghai Electric Power University |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |