CN109814043A - Cascaded structure Li-ion batteries piles consistency visual evaluating method - Google Patents
Cascaded structure Li-ion batteries piles consistency visual evaluating method Download PDFInfo
- Publication number
- CN109814043A CN109814043A CN201711147270.5A CN201711147270A CN109814043A CN 109814043 A CN109814043 A CN 109814043A CN 201711147270 A CN201711147270 A CN 201711147270A CN 109814043 A CN109814043 A CN 109814043A
- Authority
- CN
- China
- Prior art keywords
- battery
- value
- voltage
- charge
- consistency
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Landscapes
- Secondary Cells (AREA)
Abstract
The present invention relates to a kind of cascaded structure Li-ion batteries piles consistency visual evaluating methods, include the following steps: the voltage on-line measurement of each single battery in (1) charge and discharge process；(2) similarity matrix based on Euclidean distance calculates；(3) in centralization product matrix building；(4) characteristic value and feature vector of product matrix calculate in centralization；(5) building of r dimension fitting composition；(6) the consistency Visual evaluation of fitting composition is tieed up based on r.Compared with prior art, the present invention has sensitiveer, visualization, high reliability.
Description
Technical field
The present invention relates to battery technology fields, more particularly, to a kind of cascaded structure lithium ion based on multidimensional scaling
Consistency of battery pack visual evaluating method.
Background technique
Lithium ion battery has started widely to answer because having many advantages, such as high-energy density, long circulation life, low self-discharge rate
For fields such as new-energy automobile, smart grids.The inconsistency of Li-ion batteries piles is generally existing: (1) battery manufacture
Process includes multiple processes, and prior art level is difficult to ensure that the lithium ion battery of production is completely the same；(2) in use process
In, each single battery can undergo different Aging Courses, cause inconsistent to be gradually increased.In general, inconsistency can shadow
It rings battery performance to play, reduces whole service life, or even will increase security risk.Therefore, quick, reliable battery is developed
Group method for evaluating consistency is the key that battery pack balancing control, operation maintenance strategy successful implementation, runs to battery pack is improved
Performance and safety, prolong the service life, and play an important role.
Existing method for evaluating consistency of battery pack can be divided into static and two class evaluation indexes of dynamic, the former mainly includes
The parameters such as open-circuit voltage, active volume and internal resistance；The latter mainly includes charge-discharge characteristic curve, temperature curve, electrochemical impedance
The parameters such as spectrum, SOC.To the battery system of actual motion, according to electric current, voltage and the temperature information measured, the prior art is often
The only voltage between each single battery of simple evaluation, temperature difference extract the statistical indicators such as mean value, variance, though it can be to a certain degree
Consistency between each single battery of upper characterization, but it is difficult to rationally to embody monomer intrinsic parameters in battery pack that (such as open-circuit voltage can be used
Capacity, internal resistance etc.) otherness, and be difficult to visualize in higher dimensional space and show.It will be noted that measured from battery pack
Single battery electric current, voltage, temperature operation data are inside battery electrochemical reaction/transmitting coupling external manifestations, accumulate
Inside battery mechanistic information abundant is contained, support can be provided for the Conformance Assessment of battery pack.But how rationally to utilize survey
The voltage time sequence data obtained excavates the intrinsic parameters difference between single battery, realizes the rational evaluation of consistency of battery pack,
It is difficult, new method to be developed.
By retrieval, China Patent Publication No. is that CN101819259A discloses a kind of method for evaluating consistency of battery pack,
The following steps are included: charging to battery pack, battery pack charging curve is drawn according to battery pack end voltage；It calculates in battery pack
The DC internal resistance of each battery, the charging curve after obtaining removal ohm voltage drop；Polarizing voltage is calculated or measured, depolarization is obtained
Charging curve after voltage influence；Appointing takes the charging curve of two batteries individually to list；Using battery maximum available and just
Beginning state-of-charge SOC difference is modified to obtain the battery pack charging curve with good uniformity to above-mentioned two charging curves
For finally evaluating consistency of battery pack.Revised battery pack charging curve has good consistency, with simple using electricity
The external voltage in pond is compared as consistency judgment basis, and the difference of performance and state is fixed between battery, not with filling
The variation of the parameters such as electric current and locating state-of-charge and change, efficiently solve the Conformance Assessment based on external voltage difference
Method bring instability problem.But the voltage indexes between each single battery are only evaluated in the invention, though it can be to a certain extent
The consistency between each single battery is characterized, but is difficult to rationally embody monomer intrinsic parameters (such as active volume, internal resistance in battery pack
Deng) otherness, and be difficult to visualize in higher dimensional space and show.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind to be based on multidimensional scaling
The cascaded structure Li-ion batteries piles consistency visual evaluating method of method.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of cascaded structure Li-ion batteries piles consistency visual evaluating method, includes the following steps:
(1) in charge and discharge process each single battery voltage on-line measurement；
(2) similarity matrix based on Euclidean distance calculates；
(3) in centralization product matrix building；
(4) characteristic value and feature vector of product matrix calculate in centralization；
(5) building of r dimension fitting composition；
(6) the consistency Visual evaluation of fitting composition is tieed up based on r.
Preferably, the particular content of the step (1) includes:
To each charge or discharge stage, each single battery electricity of different moments is collected and recorded using battery management system
Pressure value, i-th of battery are V in the voltage value of k-th of instance sample pointik, the battery pack being composed in series by N number of single battery is every
The voltage curve that secondary charge or discharge phase measuring obtains indicates are as follows:
Wherein Vi=[Vi1,…,ViM]T, it is i-th single battery in the voltage curve in charge or discharge stage, wherein i=
1 ..., N, N are the single battery number in cascaded structure battery pack, and M is the measurement point number in the charge or discharge stage, T table
Show transposed operator.
Preferably, the particular content of the step (2) includes:
Based on each monomer battery voltage curve that the step (1) obtains, different monomers cell voltage profiles are calculated
Between Euclidean distance dij:
Wherein i, j=1 ..., N, VikIt is i-th of single battery in k-th of charge or discharge
The voltage value in stage, VjkFor j-th of single battery k-th of charge or discharge stage voltage value；
N (N-1)/2 time need to be calculated, can get the similarity matrix D based on Euclidean distance as follows:
Wherein dijBetween i-th of single battery and j-th of monomer battery voltage curve
Euclidean distance.
Preferably, the particular content of the step (3) includes:
Based on the similarity matrix based on Euclidean distance that the step (2) obtains, construct centralization inner product matrix B=
(bij)N×N, wherein the element b of interior product matrixijIt is calculated as follows:
Preferably, the particular content of the step (4) includes:
Product matrix in the centralization obtained based on the step (3), solves product matrix in this using singular value decomposition method
Characteristic value and unit character vector, note inner product matrix B N number of characteristic value be λ1≥λ2≥…≥λN, unit character vector is Γ
=[e1,…,eN]T。
Preferably, the particular content of the step (5) includes:
Based on the inner product matrix exgenvalue that the step (4) obtains, determines r maximum eigenvalue, be denoted as λ1≥λ2≥…
≥λr> 0, the r value is not more than 3, after determining r value, according to following r dimension fitting composition:
Preferably, there are two types of methods for the determination of the r value:
A. r=1,2 or 3 is determined in advance；
Or B. is determined by the ratio κ that the r characteristic values greater than zero before calculating account for all characteristic values, κ is by following public in formula
Formula is calculated:
Wherein κ0For previously given variation contribution proportion, the κ0Value is set as 0.99.
Preferably, the particular content of the step (6) includes:
Based on the r dimension fitting composition that the step (5) obtains, directly by carrying out discrete picture in r dimension space, visually
Change the similitude characterized between each single battery；Further directed to the fitting composition of r dimension space, abnormal battery collection identification is carried out,
Wherein the abnormal battery be in cascaded structure battery pack in other monomers battery intrinsic parameters existing probability meaning it is significant
The battery of difference realizes the quantitative assessment of consistency of battery pack.
Preferably, the step (6) comprising the following specific steps
(6.1) in r dimension space, the Euclidean distance matrix between each battery cell is calculatedWhereinI, j=1 ..., N；
(6.2) the Euclidean distance matrix obtained based on the step (6.1), calculates each single battery and other monomers
Average Euclidean distance similarity index between battery:
(6.3) the average distance similarity indices between all battery cells are calculated
(6.4) it to each single battery, calculatesValue, and single battery set after sorting in descending order is denoted as
{n1,…,nN}；
(6.5) remember normal battery set P={ n1,…,nN, abnormal batteries integrated S={ Ф } is empty set；Counter is set
T=1；
(6.6) n-th is removed from normal battery set PtA battery cell, i.e. P=P { nt}；
(6.7) be calculated as follows in normal battery set P the mean value of all battery average distance similarity indices and
Standard deviation:
(6.8) ifThen show n-thtA battery cell and other batteries there are significant difference, by itself plus
Enter abnormal batteries integrated, i.e. S=S ∪ { nt, t=t+1, if t≤N, return step (6.6) are set；
(6.9) if S is empty set, show that consistency of battery pack is good；Otherwise, there are SOC or intrinsic parameters are aobvious in battery pack
The abnormal battery collection S of difference is write, it will provide support for subsequent consistency operation maintenance.
Compared with prior art, the invention has the following advantages that
1, it proposes to use multidimensional scaling, realizes in cascaded structure battery pack the low of similarity matrix between each single battery
Dimension mapping, it can enhance the quick of battery pack SOC or intrinsic parameters difference on the basis of retaining higher-dimension topological structure similitude
Perception, therefore consistency of battery pack is evaluated sensitiveer；
2, by reasonably select low-dimensional mapping dimension r, it can be achieved that between each single battery inconsistency visualization, therefore
User friendly is strong；
3, it proposes the abnormal battery set identification method based on low-dimensional fitting composition, exists from angle of statistics identification significant
The abnormal battery collection of SOC or intrinsic parameters difference, therefore high reliablity.
Detailed description of the invention
Fig. 1 is flow chart of the method for the present invention.
Specific embodiment
The technical scheme in the embodiments of the invention will be clearly and completely described below, it is clear that described implementation
Example is a part of the embodiments of the present invention, rather than whole embodiments.Based on the embodiments of the present invention, ordinary skill
Personnel's every other embodiment obtained without making creative work all should belong to the model that the present invention protects
It encloses.
As shown in Figure 1, a kind of cascaded structure Li-ion batteries piles consistency Visual evaluation side based on multidimensional scaling
Method includes the following steps:
The voltage on-line measurement of each single battery in step 1 charge and discharge process: it to each charge or discharge stage, utilizes
Battery management system collects and records each unit cell voltage value of different moments, and i-th of battery is in k-th instance sample point
Voltage value is Vik, the battery pack that is composed in series by N number of single battery is bent in the voltage that each charge or discharge phase measuring obtains
Line may be expressed as:
Wherein Vi=[Vi1,…,ViM]T(i=1 ..., N) it is that i-th of single battery is bent in the voltage in charge or discharge stage
Line, N are the single battery number in cascaded structure battery pack, and M is the measurement point number in the charge or discharge stage, and T indicates to turn
Set operator.
Step 2 is calculated based on the similarity matrix of Euclidean distance: each monomer battery voltage obtained based on step 1
Curve calculates the Euclidean distance between different monomers cell voltage profiles:
N (N-1)/2 time need to be calculated, can get as follows based on the similarity matrix of Euclidean distance:
The building of product matrix in step 3 centralization: the similarity matrix based on Euclidean distance obtained based on step 2,
Construct centralization inner product matrix B=(bij)N×N, wherein the element b of interior product matrixijIt is calculated as follows:
The characteristic value and feature vector of product matrix calculate in step 4 centralization: in the centralization obtained based on step 3
Product matrix solves the characteristic value of product matrix and unit character vector in this using singular value decomposition method, remembers the N number of of inner product matrix B
Characteristic value and unit character vector are respectively λ1≥λ2≥…≥λNWith Γ=[e1,…,eN]T。
The building of step 5 .r dimension fitting composition: the inner product matrix exgenvalue obtained based on step 4 determines r maximum spy
Value indicative is denoted as λ1≥λ2≥…≥λr> 0 determines r by the ratio κ that the r characteristic values greater than zero before calculating account for all characteristic values
It is worth, κ is calculated by following formula in formula:
Wherein κ0Value may be configured as 0.99, after determining r value, fitting composition can be tieed up according to following r:
Step 6 ties up the consistency Visual evaluation of fitting composition based on r:
(6.1) in r dimension space, the Euclidean distance matrix between each battery cell is calculatedWhereinI, j=1 ..., N.
(6.2) the Euclidean distance matrix obtained based on step (6.1), is calculated between each single battery and other monomers battery
Average Euclidean distance similarity index:
(6.3) the average distance similarity indices between all battery cells are calculated
(6.4) it to each single battery, calculatesValue, and single battery set after sorting in descending order is denoted as
{n1,…,nN}；
(6.5) remember normal battery set P={ n1,…,nN, abnormal batteries integrated S={ Ф } (empty set)；Counter t is set
=1；
(6.6) n-th is removed from normal battery set PtA battery cell, i.e. P=P { nt}；
(6.7) be calculated as follows in normal battery set P the mean value of all battery average distance similarity indices and
Standard deviation:
(6.8) ifThen show n-thtA battery cell and other batteries there are significant difference, by itself plus
Enter abnormal batteries integrated, i.e. S=S ∪ { nt, t=t+1, if t≤N, return step (6.6) are set；
(6.9) if S is empty set, show that consistency of battery pack is good；Otherwise, there are SOC or intrinsic parameters are aobvious in battery pack
Write the abnormal battery collection S of difference.
The present invention develops a kind of one based on multidimensional scaling based on the monomer battery voltage curve measured
Cause property evaluation method, it is realized by constructing suitable lower dimensional space using the distance similarity matrix between monomer voltage curve
Visual evaluation is carried out between the operation characteristic difference single battery in charge and discharge process, to excavate the difference of battery extrinsic information
It is different, and propose based on low-dimensional fitting composition abnormal battery set identification method, between the consistency single battery realize it is perfect,
Reliable evaluation.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can readily occur in various equivalent modifications or replace
It changes, these modifications or substitutions should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with right
It is required that protection scope subject to.
Claims (9)
1. a kind of cascaded structure Li-ion batteries piles consistency visual evaluating method, which comprises the steps of:
(1) in charge and discharge process each single battery voltage on-line measurement；
(2) similarity matrix based on Euclidean distance calculates；
(3) in centralization product matrix building；
(4) characteristic value and feature vector of product matrix calculate in centralization；
(5) building of r dimension fitting composition；
(6) the consistency Visual evaluation of fitting composition is tieed up based on r.
2. according to the method described in claim 1, it is characterized by: the particular content of the step (1) includes:
To each charge or discharge stage, each monomer battery voltage of different moments is collected and recorded using battery management system
Value, i-th of battery are V in the voltage value of k-th of instance sample pointik, the battery pack being composed in series by N number of single battery is each
The voltage curve that charge or discharge phase measuring obtains indicates are as follows:
Wherein Vi=[Vi1,…,ViM]T, it is i-th single battery in the voltage curve in charge or discharge stage, wherein i=
1 ..., N, N are the single battery number in cascaded structure battery pack, and M is the measurement point number in the charge or discharge stage, T table
Show transposed operator.
3. according to the method described in claim 2, it is characterized by: the particular content of the step (2) includes:
Based on each monomer battery voltage curve that the step (1) obtains, calculate between different monomers cell voltage profiles
Euclidean distance dij:
Wherein i, j=1 ..., N, VikIt is i-th of single battery k-th of charge or discharge stage
Voltage value, VjkFor j-th of single battery k-th of charge or discharge stage voltage value；
N (N-1)/2 time need to be calculated, can get the similarity matrix D based on Euclidean distance as follows:
Wherein dijIt is European between i-th of single battery and j-th of monomer battery voltage curve
Distance.
4. according to the method described in claim 3, it is characterized by: the particular content of the step (3) includes:
Based on the similarity matrix based on Euclidean distance that the step (2) obtains, construct centralization inner product matrix B=
(bij)N×N, wherein the element b of interior product matrixijIt is calculated as follows:
5. according to the method described in claim 4, it is characterized by: the particular content of the step (4) includes:
Product matrix in the centralization obtained based on the step (3), the spy of product matrix in this is solved using singular value decomposition method
Value indicative and unit character vector, N number of characteristic value of note inner product matrix B are λ1≥λ2≥…≥λN, unit character vector be Γ=
[e1,…,eN]T。
6. according to the method described in claim 5, it is characterized by: the particular content of the step (5) includes:
Based on the inner product matrix exgenvalue that the step (4) obtains, determines r maximum eigenvalue, be denoted as λ1≥λ2≥…≥λr
> 0, the r value is not more than 3, after determining r value, according to following r dimension fitting composition:
7. according to the method described in claim 6, it is characterized by: the determination of the r value is there are two types of method:
A. r=1,2 or 3 is determined in advance；
Or B. determines that κ is by following formula meter in formula by the ratio κ that the r characteristic values greater than zero before calculating account for all characteristic values
It obtains:
Wherein κ0For previously given variation contribution proportion, the κ0Value is set as 0.99.
8. according to the method described in claim 6, it is characterized by: the particular content of the step (6) includes:
The r dimension fitting composition obtained based on the step (5) visualizes table directly by carrying out discrete picture in r dimension space
Levy the similitude between each single battery；Further directed to the fitting composition of r dimension space, abnormal battery collection identification is carried out, wherein
The abnormal battery is and significant difference in other monomers battery intrinsic parameters existing probability meaning in cascaded structure battery pack
Battery, realize the quantitative assessment of consistency of battery pack.
9. according to the method described in claim 8, it is characterized by: the step (6) comprising the following specific steps
(6.1) in r dimension space, the Euclidean distance matrix between each battery cell is calculatedWherein
(6.2) the Euclidean distance matrix obtained based on the step (6.1), calculates each single battery and other monomers battery
Between average Euclidean distance similarity index:
(6.3) the average distance similarity indices between all battery cells are calculated
(6.4) it to each single battery, calculatesValue, and single battery set after sorting in descending order is denoted as
{n1,…,nN}；
(6.5) remember normal battery set P={ n1,…,nN, abnormal batteries integrated S={ Ф } is empty set；Counter t=is set
1；
(6.6) n-th is removed from normal battery set PtA battery cell, i.e. P=P { nt}；
(6.7) mean value and standard of all battery average distance similarity indices in normal battery set P is calculated as follows
Difference:
(6.8) ifThen show n-thtThere are significant differences with other batteries for a battery cell, are added into different
Normal batteries integrated, i.e. S=S ∪ { nt, t=t+1, if t≤N, return step (6.6) are set；
(6.9) if S is empty set, show that consistency of battery pack is good；Otherwise, there are SOC or intrinsic parameters significance differences in battery pack
Different abnormal battery collection S, it will provide support for subsequent consistency operation maintenance.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711147270.5A CN109814043A (en) | 2017-11-17 | 2017-11-17 | Cascaded structure Li-ion batteries piles consistency visual evaluating method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711147270.5A CN109814043A (en) | 2017-11-17 | 2017-11-17 | Cascaded structure Li-ion batteries piles consistency visual evaluating method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109814043A true CN109814043A (en) | 2019-05-28 |
Family
ID=66599049
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711147270.5A Pending CN109814043A (en) | 2017-11-17 | 2017-11-17 | Cascaded structure Li-ion batteries piles consistency visual evaluating method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109814043A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111487553A (en) * | 2020-04-20 | 2020-08-04 | 国电南瑞科技股份有限公司 | Method and device for evaluating consistency of battery monomer |
| CN112924870A (en) * | 2021-04-12 | 2021-06-08 | 上海电享信息科技有限公司 | Method for evaluating inconsistency of battery |
| CN113866646A (en) * | 2021-11-15 | 2021-12-31 | 长沙理工大学 | Battery cluster inconsistency on-line monitoring method research based on polarization impedance voltage rise |
| CN113884894A (en) * | 2021-11-15 | 2022-01-04 | 长沙理工大学 | Battery cluster inconsistency online monitoring method research based on external characteristics |
| CN114355206A (en) * | 2022-01-05 | 2022-04-15 | 浙江零碳云能源科技有限公司 | Energy storage battery unsupervised fault diagnosis algorithm based on similarity measurement |
| CN115640702A (en) * | 2022-11-16 | 2023-01-24 | 武汉市蓝电电子股份有限公司 | Battery grouping method and system |
| CN118011240A (en) * | 2024-04-10 | 2024-05-10 | 深圳屹艮科技有限公司 | Method and device for evaluating consistency of batteries, storage medium and computer equipment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103817091A (en) * | 2014-02-28 | 2014-05-28 | 清华大学 | Battery sorting method and system |
| CN106953385A (en) * | 2017-04-26 | 2017-07-14 | 杭州科工电子科技有限公司 | A kind of Li-ion batteries piles balance control method |
| CN107093775A (en) * | 2017-05-04 | 2017-08-25 | 广东电网有限责任公司电力科学研究院 | A kind of method for evaluating consistency and device of cascaded structure battery pack |
-
2017
- 2017-11-17 CN CN201711147270.5A patent/CN109814043A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103817091A (en) * | 2014-02-28 | 2014-05-28 | 清华大学 | Battery sorting method and system |
| CN106953385A (en) * | 2017-04-26 | 2017-07-14 | 杭州科工电子科技有限公司 | A kind of Li-ion batteries piles balance control method |
| CN107093775A (en) * | 2017-05-04 | 2017-08-25 | 广东电网有限责任公司电力科学研究院 | A kind of method for evaluating consistency and device of cascaded structure battery pack |
Non-Patent Citations (1)
| Title |
|---|
| 张春燕等: "基于MDS的统计形状聚类", 《计算机技术与发展》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111487553A (en) * | 2020-04-20 | 2020-08-04 | 国电南瑞科技股份有限公司 | Method and device for evaluating consistency of battery monomer |
| CN112924870A (en) * | 2021-04-12 | 2021-06-08 | 上海电享信息科技有限公司 | Method for evaluating inconsistency of battery |
| CN113866646A (en) * | 2021-11-15 | 2021-12-31 | 长沙理工大学 | Battery cluster inconsistency on-line monitoring method research based on polarization impedance voltage rise |
| CN113884894A (en) * | 2021-11-15 | 2022-01-04 | 长沙理工大学 | Battery cluster inconsistency online monitoring method research based on external characteristics |
| CN113884894B (en) * | 2021-11-15 | 2023-07-21 | 长沙理工大学 | Battery cluster inconsistency on-line monitoring method based on external characteristics |
| CN113866646B (en) * | 2021-11-15 | 2024-05-17 | 长沙理工大学 | Battery cluster inconsistency online monitoring method based on polarization impedance voltage rise |
| CN114355206A (en) * | 2022-01-05 | 2022-04-15 | 浙江零碳云能源科技有限公司 | Energy storage battery unsupervised fault diagnosis algorithm based on similarity measurement |
| CN115640702A (en) * | 2022-11-16 | 2023-01-24 | 武汉市蓝电电子股份有限公司 | Battery grouping method and system |
| CN118011240A (en) * | 2024-04-10 | 2024-05-10 | 深圳屹艮科技有限公司 | Method and device for evaluating consistency of batteries, storage medium and computer equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109814043A (en) | Cascaded structure Li-ion batteries piles consistency visual evaluating method | |
| CN104502859B (en) | The detection of battery charge amount and cell health state, diagnostic method | |
| CN104793144B (en) | A kind of battery life quick determination method | |
| CN103792495B (en) | Method for evaluating battery performance based on Delphi method and grey relation theory | |
| CN102520361B (en) | State of health (SOH) value assessment method of battery pack | |
| CN103728563B (en) | A kind of measuring method of cell health state | |
| CN104977537B (en) | The determination method of battery SOC and the battery management system for using this method | |
| CN108037462A (en) | Storage battery health status quantization method and system | |
| CN116502112B (en) | New energy power supply test data management method and system | |
| CN204269787U (en) | The conforming detection system of a kind of lithium ion battery cryogenic property | |
| CN108107372A (en) | Accumulator health status quantization method and system based on the estimation of SOC subregions | |
| CN103909068B (en) | The method for separating of battery | |
| CN109143108A (en) | A kind of estimation method of the lithium ion battery SOH based on electrochemical impedance spectroscopy | |
| CN107093775A (en) | A kind of method for evaluating consistency and device of cascaded structure battery pack | |
| CN105866700B (en) | A kind of method that lithium ion battery quickly screens | |
| CN103412264B (en) | The conforming evaluation method of cell in battery pack | |
| CN107192954A (en) | A kind of performance of lithium ion battery inline diagnosis method | |
| CN104237802A (en) | Detection method for low-temperature performance uniformity of lithium ion batteries | |
| CN114240260B (en) | New energy group vehicle thermal runaway risk assessment method based on digital twinning | |
| CN108845264A (en) | A kind of cell health state estimation method based on small echo | |
| CN106033114A (en) | Health state evaluation method of energy storage battery | |
| CN115267555A (en) | Battery SOH (State of health) evaluation system of energy storage system based on battery multipoint temperature measurement | |
| CN107045105B (en) | A kind of Li-ion batteries piles utilisable energy calculation method | |
| CN112505573A (en) | Consistency evaluation index calculation method for retired power battery | |
| CN116699407A (en) | Power battery safety risk early warning method based on safety entropy |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190528 |