CN109738811A - Li-ion batteries piles external short circuit method for diagnosing faults based on twin-stage model prediction - Google Patents
Li-ion batteries piles external short circuit method for diagnosing faults based on twin-stage model prediction Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
Abstract
The present invention proposes a kind of Li-ion batteries piles external short circuit method for diagnosing faults based on twin-stage model prediction, is related to lithium-ion-power cell security technology area.Firstly, carrying out external short circuit experiment to Li-ion batteries piles, battery pack external short circuit twin-stage equivalent-circuit model is constructed, offline optimality identification is carried out to battery model parameter using tested experimental data；Then, battery in battery pack state is judged according to battery measurement data when operation, it was found that when partial cell voltage occurs abnormal, entirety is labeled as to abnormal adjacent cell is generated, it is denoted as abnormal battery pack, starts first order battery model, if first order battery model error is less than threshold limit value, second level battery model is then triggered, calculates and obtains model error；Finally, carrying out fault diagnosis to abnormal battery by measured data and the twin-stage model goodness of fit.This method step is simple, is easy to canbe used on line, and high reliablity, is suitable for electric automobile power battery on-line fault diagnosis and safety management.
Description
Technical field
The present invention relates to lithium-ion-power cell security technology area more particularly to a kind of lithiums based on twin-stage model prediction
Ion battery group external short circuit method for diagnosing faults.
Background technique
Development of Electric Vehicles is rapid in recent years, Development of EV be considered to be solve environmental pollution, reduce fuel consumption,
A kind of effective way of construction green, the urban transportation of environmental protection, however occur often in the application process of battery car on fire
The safety accident of explosion, the thermal runaway that root causes often caused by battery failures.External short circuit, be in battery failures very
Common and more serious one of failure, battery pack generates high current when external short circuit failure, is easy to cause battery high-temperature high fever,
The duration of external short circuit failure often only has tens seconds, therefore how effective, accurate and fast to the progress of external short trouble
The on-line fault diagnosis of speed, is a particularly significant technical problem.
Current existing battery management system majority is state estimation, the life prediction etc. for battery, and for battery
The method of safety problem and fault diagnosis is still immature, and especially the external short circuit fault diagnosis technology of high power battery group is more
It is deficient to lack.
Summary of the invention
It is a kind of pre- based on twin-stage model the technical problem to be solved by the present invention is in view of the above shortcomings of the prior art, provide
The Li-ion batteries piles external short circuit method for diagnosing faults of survey；This method step is simple, is easy to canbe used on line, and high reliablity,
Suitable for electric automobile power battery on-line fault diagnosis and safety management.
In order to solve the above technical problems, the technical solution used in the present invention is:
The present invention provides a kind of Li-ion batteries piles external short circuit method for diagnosing faults based on twin-stage model prediction, including
Following steps:
Step 1: carrying out the experiment of battery pack external short circuit, record experimental data, the measurement data I including electric currentc=[Ic1,
Ic2,…,IcN]T, end voltage measurement data Uc=[Uc1,Uc2,…,UcN]T, wherein N is data sampling quantity, and the value of N takes
Certainly current duration and sampling step length, the transposition of T representing matrix in external short circuit test；
Step 2: establishing the twin-stage battery model of external short circuit failure, and pass through obtained experimental data point in step 1
It is other that offline optimality parameter identification is carried out to twin-stage battery model；
First order battery model is a kind of improved equivalent-circuit model, improved method are as follows: by traditional equivalent circuit mould
Battery charge state SOC in type, is improved to depth of discharge ξ in short-circuit processE, and open-circuit voltage is considered as depth of discharge ξE's
Polynomial function；
The specific mathematical expression form of first order battery model are as follows:
Wherein, k indicates current sample time, τ=RpCp, Ut,Up, and UocRespectively indicate end voltage, the polarization electricity of battery pack
Pressure and open-circuit voltage；RpAnd R0Then respectively indicate polarization resistance and ohmic internal resistance, CpRepresent polarization capacity, iLIndicate battery pack
Electric current, ipIndicate RpOn the electric current that flows through, Δ t is sampling step length, ξEIndicate the depth of discharge in external short trouble.
Second level battery model is half-cell model, specific mathematical expression form are as follows:
WhereinRepresent constant voltage source；
Step 3: each monomer voltage of battery management system real-time monitoring battery pack is utilized, when percentage of batteries monomer voltage is low
In threshold limit value Vn, then 4 are entered step；
Step 4: adjacent abnormal battery cell is considered as an abnormal battery pack by triggering first order battery model, will be electric
Pond group electric current is as mode input, the predicted voltage of real-time computation model output；
Step 5: calculating the goodness of fit σ between first order cell model predictive voltage and measurement voltage, duration T1When
It carves, if goodness of fit σ < threshold limit value χ1, then a possibility that excluding external short circuit failure, and 8 are entered step, otherwise, preliminary boundary
It is set to external short circuit failure, triggers second level battery model, and enter step 6；
Step 6: using battery pack current as the prediction electricity of the input of second level battery model and computation model output in real time
Pressure calculates the goodness of fit σ between second level cell model predictive voltage and measurement voltage, duration T2Moment, if coincide
Spend σ > threshold limit value χ2, then confirm that the exception is caused by external short circuit failure, positioning is abnormal the position of battery cell simultaneously
Enter step 8；Otherwise, the diagnosis duration is increased into T3, and enter step 7；
Step 7: repeating to judge the goodness of fit using second level battery model, if goodness of fit σ < threshold limit value χ2Then exclude
A possibility that external short circuit failure, if goodness of fit σ > threshold limit value χ2, then it is confirmed as external short circuit failure；
Step 8: storing and export diagnostic result, return step 3 waits for next operation.
Twin-stage battery circuit model one in the step 2 is divided into two-stage, and wherein first order battery model is an electricity
Pond overall model, second level battery model are half-cell model；Second level battery model is half-cell model, the modeling of the second level
Method is that battery is considered as to two parts equivalent-circuit model, including model 1 and model 2, i.e., the sum of model 1 and model 2 are battery
Overall model, second level battery model refer in particular to wherein model 2；In model 1, by a variable voltage sourceWith internal resistance of cell R0、
Short-circuit resistance RSIt is linked as circuit；In model 2, there is a constant voltage sourceConnect with RC link and generates end voltage Ut, RC ring
Section is composed in parallel by a capacitor C and polarization resistance Rp；The open-circuit voltage of entire battery is variable voltage sourceWith constant voltage
SourceThe sum of:
Offline optimality parameter identification in the step 2 is that will test current measurement value IcAs mode input, end electricity
Pressure output U=[U1,U2,…,UN]TAs model export, using global optimization approach to the model parameter in step 2 carry out from
The identification of line optimality, and the identification process of model need to recognize respectively two-level model parameter, the parameter of two-level model is mutually indepedent.
Goodness of fit σ in the step 5 is defined as: model prediction result and actual test result within certain duration
Root-mean-square error inverse, it may be assumed that
Wherein ρ is the sampling number in duration T, Ut,mTo hold the model prediction of voltage as a result, UtFor end voltage
Line measurement data, θnRepresentative model parameter matrix.
Critical threshold values χ in the step 61For the critical threshold values of the goodness of fit of first order battery model, threshold limit value is taken
Model goodness of fit calculated result when value need to be tested slightly below；
Critical threshold values χ in the step 72For the critical threshold values of the goodness of fit of second level battery model, threshold limit value is taken
Model goodness of fit calculated result when value need to be tested slightly below.
The beneficial effects of adopting the technical scheme are that the lithium provided by the invention based on twin-stage model prediction
Ion battery group external short circuit method for diagnosing faults, this method optimize equivalent-circuit model using a kind of twin-stage, wherein the first order
Model is battery overall model, includes more parameter to be identified, Model suitability is good but precision is slightly lower, and second level model is
Half-cell model, includes less parameter to be identified and model accuracy is higher；Using battery pack external short circuit data to twin-stage
Model parameter is recognized, by the goodness of fit of battery pack measured data and model prediction, the online event of Lai Jinhang external short circuit
Barrier diagnosis.This method step is simple, is easy to canbe used on line, and high reliablity, is suitable for the online failure of electric automobile power battery
Diagnosis and safety management.
Detailed description of the invention
Fig. 1 is external short circuit fault diagnosis twin-stage equivalent-circuit model provided in an embodiment of the present invention, and wherein a is the first order
Battery model；B is the model 1 in the battery model of the second level；C is the model 2 in the battery model of the second level；
Fig. 2 is the external short circuit inline diagnosis estimation method process provided in an embodiment of the present invention based on twin-stage model prediction
Figure；
Fig. 3 is external short circuit twin-stage Model Distinguish error analysis result figure provided in an embodiment of the present invention, and wherein a is identification
Error analysis result figure, b are the error schematic diagram that Identification Errors analyze result；
Fig. 4 is battery pack external short circuit diagnostic graph provided in an embodiment of the present invention, and wherein a-1 is voltage diagnostic figure, and a-2 is
Scheme the partial enlarged view in a-1 at H, b-1 is twin-stage model error figure, and b-2 is the partial enlarged view schemed in b-1 at Z；
Fig. 5 is external short circuit experimental result picture provided in an embodiment of the present invention, and wherein a-1 is Voltage experiments result figure, a-2
For the partial enlarged view that AB in figure a-1 goes out, b is Current experiments result figure.
Specific embodiment
With reference to the accompanying drawings and examples, specific embodiments of the present invention will be described in further detail.Implement below
Example is not intended to limit the scope of the invention for illustrating the present invention.
In embodiment by taking 18650NMC cylindrical lithium-ion power battery as an example, voltage rating 3.6V, nominal capacity
For 2.4Ah, battery pack is formed using 6 pieces of battery cells of SOH value > 0.96；Experimental facilities uses: NEU_ESCTEST02 test
Platform cooperation sea to instrument GD-2045D temperature control box,
The method of the present embodiment is as described below.
The present invention provides a kind of Li-ion batteries piles external short circuit method for diagnosing faults based on twin-stage model prediction, such as schemes
Shown in 2, comprising the following steps:
Step 1: carrying out the experiment of battery pack external short circuit, record experimental data, the measurement data I including electric currentc=[Ic1,
Ic2,…,IcN]T, end voltage measurement data Uc=[Uc1,Uc2,…,UcN]T, wherein N is data sampling quantity, and the value of N takes
Certainly current duration and sampling step length, the transposition of T representing matrix in external short circuit test；
Step 2: establishing the twin-stage battery model of external short circuit failure, and pass through obtained experimental data point in step 1
It is other that offline optimality parameter identification is carried out to twin-stage battery model；Offline optimality parameter identification is that will test current measurement value Ic
As mode input, voltage output U=[U is held1,U2,…,UN]TIt is exported as model, using global optimization approach in step 2
Model parameter carry out offline optimality identification, and the identification process of model need to recognize respectively two-level model parameter, two-stage mould
The parameter of type is mutually indepedent.
First order battery model is a kind of improved equivalent-circuit model, as shown in Figure 1 shown in middle a figure, improved method
Are as follows: by the battery charge state SOC in traditional equivalent-circuit model, it is improved to depth of discharge ξ in short-circuit processE, and will open circuit
Voltage is considered as depth of discharge ξEPolynomial function；
The specific mathematical expression form of first order battery model are as follows:
Wherein, k indicates current sample time, τ=RpCp, Ut,Up, and UocRespectively indicate end voltage, the polarization electricity of battery pack
Pressure and open-circuit voltage；RpAnd R0Then respectively indicate polarization resistance and ohmic internal resistance, CpRepresent polarization capacity, iLIndicate battery pack
Electric current, ipIndicate RpOn the electric current that flows through, Δ t is sampling step length, ξEIndicate the depth of discharge in external short trouble.
Open-circuit voltage is indicated with multinomial, is shown below
N in formulapIt is polynomial number, αiRepresentative polynomial coefficient, ξEIndicate that the electric discharge in external short trouble is deep
Degree, circular are shown below:
Q in formulaRFor nominal capacity.
Second level battery model is half-cell model, and the modeling method of the second level is that battery is considered as to two parts equivalent circuit
Model, including model 1 and model 2, i.e. the sum of model 1 and model 2 are battery overall model, and second level battery model refers in particular to wherein
Model 2；In model 1, as shown in Figure 1 shown in middle b figure, by a variable voltage sourceWith internal resistance of cell R0, short-circuit resistance RSEven
For circuit；In model 2, as shown in Figure 1 shown in middle c figure, there is a constant voltage sourceConnect with RC link and generates end voltage
Ut, RC link are composed in parallel by a capacitor C and polarization resistance Rp；The open-circuit voltage of entire battery is variable voltage sourceWith perseverance
Constant voltage sourceThe sum of:
Second level battery model is half-cell model, specific mathematical expression form are as follows:
WhereinRepresent constant voltage source；
For first order battery model, parameter θ to be identified1=[α1,α2,…,α10,τ,Rp,R0] amount to 13 parameters, it is right
In second level battery model, band identified parameters θ2=[U0,τ,Rp] amount to 3 parameters.Using experimental data respectively to twin-stage battery
Model carries out offline optimality parameter identification, and discrimination method can use global optimization's method, in the present embodiment using something lost
Propagation algorithm carries out parameter identification, and selecting method does not constitute restriction to the present invention.Identification Errors are fig. 3, it is shown that in this way
Constructed model, second level cell model predictive precision can be very high.After the completion of identification, identification result such as table 1- table 2 is recorded
It is shown:
1 first order battery model parameter identification result of table
2 second level battery model parameter identification result of table
| Parameter | Identification result |
| U0(mV) | 589.7 |
| τ(s) | 6.9 |
| Rp(mΩ)) | 6.5 |
Step 3: each monomer voltage of battery management system real-time monitoring battery pack is utilized, if percentage of batteries monomer voltage
Lower than threshold limit value Vn, then 4 are entered step；
Monomer voltage threshold limit value Vn=2.0V is set in the present embodiment, and it is normal that the setting of threshold limit value is slightly less than battery
Electric discharge is by voltage；The battery management system is the battery management system of new-energy automobile, mainly has Current Voltage temperature to adopt
Collect function, battery status estimation, overvoltage protection and safety management system；
Step 4: adjacent abnormal battery cell is considered as an abnormal battery pack by triggering first order battery model, will be electric
Pond group electric current is as mode input, the predicted voltage of real-time computation model output；
Step 5: calculating the goodness of fit σ between first order cell model predictive voltage and measurement voltage, duration T1When
It carves, if goodness of fit σ < threshold limit value χ1, then a possibility that excluding external short circuit failure, and 8 are entered step, otherwise, preliminary boundary
It is set to external short circuit failure, triggers second level battery model, and enter step 6；
Goodness of fit σ's is defined as: the root mean square of model prediction result and actual test result misses within certain duration
The inverse of difference, it may be assumed that
Wherein ρ is the sampling number in duration T, Ut,mTo hold the model prediction of voltage as a result, UtFor end voltage
Line measurement data, θnRepresentative model parameter matrix.
Duration T is set in the present embodiment1=1.0s, T2=3.0s, T3=10.0s；Threshold limit value χ is set1=3.5,
Threshold limit value χ2=30.
Critical threshold values χ1Value for the critical threshold values of the goodness of fit of first order battery model, threshold limit value need to be tested slightly below
When model goodness of fit calculated result；
Critical threshold values χ2Value for the critical threshold values of the goodness of fit of second level battery model, threshold limit value need to be tested slightly below
When model goodness of fit calculated result.
The threshold limit value of the model goodness of fit is determined according to experimental result, when the value of threshold limit value need to be tested slightly below
Model goodness of fit calculated result, can guarantee that diagnosis process is not in fail to judge in this way；According to the experimental result of external short circuit,
Model goodness of fit calculated result is as shown in table 3:
The 3 model goodness of fit of table
| Experiment number | The first order model goodness of fit | The second level model goodness of fit |
| 1 | 3.9 | 33.8 |
| 2 | 4.1 | 32.4 |
| 3 | 4.4 | 31.7 |
| 4 | 3.7 | 35.5 |
| 5 | 4.6 | 37.1 |
Therefore, threshold limit value χ is set1=3.5, threshold limit value χ2=30.
Step 6: using battery pack current as the prediction electricity of the input of second level battery model and computation model output in real time
Pressure calculates the goodness of fit σ between second level cell model predictive voltage and measurement voltage, duration T2Moment, if coincide
Spend σ > threshold limit value χ2, then confirm that the exception is caused by external short circuit failure, positioning is abnormal the position of battery cell simultaneously
Enter step 8；Otherwise, the diagnosis duration is increased into T3, and enter step 7；
Step 7: repeating to judge the goodness of fit using second level battery model, if goodness of fit σ < threshold limit value χ2Then exclude
A possibility that external short circuit failure, if goodness of fit σ > threshold limit value χ2, then it is confirmed as external short circuit failure；
Step 8: storing and export diagnostic result, return step 3 waits for next operation.
On-line operation, using each monomer voltage of battery management system real-time monitoring battery pack, in the present embodiment, to 6
The battery pack of block battery composition carries out short circuit, and battery voltage quickly falls to 0.5V hereinafter, being lower than threshold limit value, therefore touches
First order battery model has been sent out, abnormal battery cell has been formed into abnormal battery pack by adjacent body, using battery pack current as mould
Type input, the predicted voltage of real-time computation model output, as in Fig. 4 a-1 figure it is shown in solid, while online obtaining battery pack end
Voltage measurements, as shown in the dotted line of a-1 figure in Fig. 4, such as the twin-stage model error figure that b-1 is 3-7 seconds in Fig. 4.
Goodness of fit σ between computation model predicted voltage and measurement voltage, and the goodness of fit is compared with threshold limit value,
The logic judgment process according to summary of the invention carries out fault diagnosis, and in the present embodiment, first order battery model coincide
σ ≈ 6.34 is spent, second level battery model is triggered, in the battery model operational process of the second level, model prediction result and actual measurement number
It is lower than 20mV according to error, is computed, 93.7 > threshold limit value χ of second level battery model goodness of fit σ ≈2, according to judgment criterion, really
Think that external short circuit failure, inline diagnosis process are completed, stores and export diagnostic result, as shown in Figure 5.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
Present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: it still may be used
To modify to technical solution documented by previous embodiment, or some or all of the technical features are equal
Replacement；And these are modified or replaceed, model defined by the claims in the present invention that it does not separate the essence of the corresponding technical solution
It encloses.
Claims (5)
1. a kind of Li-ion batteries piles external short circuit method for diagnosing faults based on twin-stage model prediction, it is characterised in that: including
Following steps:
Step 1: carrying out the experiment of battery pack external short circuit, record experimental data, the measurement data I including electric currentc=[Ic1,
Ic2,…,IcN]T, end voltage measurement data Uc=[Uc1,Uc2,…,UcN]T, wherein N is data sampling quantity, and the value of N takes
Certainly current duration and sampling step length, the transposition of T representing matrix in external short circuit test；
Step 2: establishing the twin-stage battery model of external short circuit failure, and right respectively by obtained experimental data in step 1
Twin-stage battery model carries out offline optimality parameter identification；
First order battery model is a kind of improved equivalent-circuit model, improved method are as follows: will be in traditional equivalent-circuit model
Battery charge state SOC, be improved to depth of discharge ξ in short-circuit processE, and open-circuit voltage is considered as depth of discharge ξEIt is multinomial
Formula function；
The specific mathematical expression form of first order battery model are as follows:
Wherein, k indicates current sample time, τ=RpCp, Ut,Up, and UocRespectively indicate the end voltage of battery pack, polarizing voltage,
And open-circuit voltage；RpAnd R0Then respectively indicate polarization resistance and ohmic internal resistance, CpRepresent polarization capacity, iLIndicate battery pack electricity
Stream, ipIndicate RpOn the electric current that flows through, Δ t is sampling step length, ξEIndicate the depth of discharge in external short trouble；
Second level battery model is half-cell model, specific mathematical expression form are as follows:
WhereinRepresent constant voltage source；
Step 3: utilizing each monomer voltage of battery management system real-time monitoring battery pack, face when percentage of batteries monomer voltage is lower than
Boundary threshold value Vn, then enter step 4；
Step 4: adjacent abnormal battery cell is considered as an abnormal battery pack, by battery pack by triggering first order battery model
Electric current is as mode input, the predicted voltage of real-time computation model output；
Step 5: calculating the goodness of fit σ between first order cell model predictive voltage and measurement voltage, duration T1Moment, such as
Fruit goodness of fit σ < threshold limit value χ1, then a possibility that excluding external short circuit failure, and 8 are entered step, otherwise, tentatively it is defined as
External short circuit failure triggers second level battery model, and enters step 6；
Step 6: using battery pack current as the predicted voltage of the input of second level battery model and computation model output in real time, meter
Calculate the goodness of fit σ between second level cell model predictive voltage and measurement voltage, duration T2Moment, if goodness of fit σ >
Threshold limit value χ2, then confirm that the exception is caused by external short circuit failure, positioning is abnormal position and the entrance of battery cell
Step 8；Otherwise, the diagnosis duration is increased into T3, and enter step 7；
Step 7: repeating to judge the goodness of fit using second level battery model, if goodness of fit σ < threshold limit value χ2It then excludes external short
A possibility that road failure, if goodness of fit σ > threshold limit value χ2, then it is confirmed as external short circuit failure；
Step 8: storing and export diagnostic result, return step 3 waits for next operation.
2. a kind of Li-ion batteries piles external short circuit fault diagnosis side based on twin-stage model prediction according to claim 1
Method, it is characterised in that: the twin-stage battery circuit model one in the step 2 is divided into two-stage, and wherein first order battery model is
One battery overall model, second level battery model are half-cell model；Second level battery model is half-cell model, the second level
Modeling method be that battery is considered as to two parts equivalent-circuit model, including model 1 and model 2, i.e. the sum of model 1 and model 2
For battery overall model, second level battery model refers in particular to wherein model 2；In model 1, by a variable voltage sourceWith battery
Internal resistance R0, short-circuit resistance RSIt is linked as circuit；In model 2, there is a constant voltage sourceConnect with RC link and generates end voltage
Ut, RC link are composed in parallel by a capacitor C and polarization resistance Rp；The open-circuit voltage of entire battery is variable voltage sourceWith perseverance
Constant voltage sourceThe sum of:
3. a kind of Li-ion batteries piles external short circuit fault diagnosis side based on twin-stage model prediction according to claim 1
Method, it is characterised in that: the offline optimality parameter identification in the step 2 is that will test current measurement value IcIt is defeated as model
Enter, holds voltage output U=[U1,U2,…,UN]TIt is exported as model, using global optimization approach to the model parameter in step 2
Offline optimality identification is carried out, and the identification process of model need to recognize respectively two-level model parameter, the parameter phase of two-level model
It is mutually independent.
4. a kind of Li-ion batteries piles external short circuit fault diagnosis side based on twin-stage model prediction according to claim 1
Method, it is characterised in that: goodness of fit σ in the step 5 is defined as: model prediction result and practical survey within certain duration
The inverse of the root-mean-square error of test result, it may be assumed that
Wherein ρ is the sampling number in duration T, Ut,mTo hold the model prediction of voltage as a result, UtFor the online survey for holding voltage
Measure data, θnRepresentative model parameter matrix.
5. a kind of Li-ion batteries piles external short circuit fault diagnosis side based on twin-stage model prediction according to claim 1
Method, it is characterised in that: the critical threshold values χ in the step 61For the critical threshold values of the goodness of fit of first order battery model, Threshold extent
The model goodness of fit calculated result when value of value need to be tested slightly below；
Critical threshold values χ in the step 72For the critical threshold values of the goodness of fit of second level battery model, the value of threshold limit value needs to omit
Model goodness of fit calculated result when lower than experiment.
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| CN112632850A (en) * | 2020-12-14 | 2021-04-09 | 华中科技大学 | Method and system for detecting abnormal battery in lithium battery pack |
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