CN105116337B  A kind of full electric charge storage life evaluation method of lithium ion battery  Google Patents
A kind of full electric charge storage life evaluation method of lithium ion battery Download PDFInfo
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 CN105116337B CN105116337B CN201510373029.9A CN201510373029A CN105116337B CN 105116337 B CN105116337 B CN 105116337B CN 201510373029 A CN201510373029 A CN 201510373029A CN 105116337 B CN105116337 B CN 105116337B
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 238000003860 storage Methods 0.000 title claims abstract description 140
 HBBGRARXTFLTSGUHFFFAOYSAN Lithium Ion Chemical compound data:image/svg+xml;base64,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 data:image/svg+xml;base64,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 [Li+] HBBGRARXTFLTSGUHFFFAOYSAN 0.000 title claims abstract description 51
 229910001416 lithium ion Inorganic materials 0.000 title claims abstract description 51
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 WHXSMMKQMYFTQSUHFFFAOYSAN lithium Chemical compound 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Abstract
The invention discloses a kind of full electric charge storage life evaluation method of lithium ion battery, step includes：The battery sample for evaluating the full state of charge of lithium ion battery is stored to the sample time t specified under multiple storage temperature value T to obtain capacitance loss rate Q and generate experimental data；Capacity attenuation Ageing Model is set up, model parameter ρ and model parameter a value is determined；Judge the selection reasonability of storage temperature scope based on whether each storage temperature value T drag parameter a meets Arrhenius formula；Statistics draws the average and standard deviation of model parameter A, B；The average of model parameter ρ value and model parameter A, B is substituted into model and is fitted goodness judgement；After the goodness of fit is met, acquisition lifespan distribution map is predicted to the lifespan of lithium ion battery to be evaluated at normal temperatures.The present invention have the advantages that abundant life assessment principle, high data reliability and it is highprecision, simple and easy to apply, be easily achieved, evaluation time is short, have a wide range of application.
Description
Technical field
The present invention relates to the storage life assessment technology of lithium ion battery, and in particular to the lithium that a kind of utilization high temperature accelerates from
The full electric charge storage life evaluation method of sub battery.
Background technology
With the development and the progress of science and technology of society, lithium ion battery has obtained widely should as energy resource system of new generation
With.Lithium ion battery has high security, the advantages of pollutionfree as electrical source of power compared to traditional oils electrical source of power so that
The great power supply on vehicle application prospect of lithium battery, has successfully produced the pure electric automobile based on lithium battery and extensive both at home and abroad at present
Sale.
However, the lifespan of lithium ion battery is to limit one of its wide variety of maximum restraining factors.Generally, the longevity of battery
Life terminal is defined as the 80% of its initial capacity.Lithium ion battery is proposed as the electrical source of power requirement of electric automobile by external,
Its service life target is that using 15 years, its capacity remained at more than 80% after circulating battery more than 45000 times, and proposition exists
The lifespan of battery is tested and analyzed out in 1~2 year by test, current driving force battery is still difficult to reach this life level.
In the lifespan of the method rapid evaluating cell accelerated by high temperature, have to the research for promoting longlife batteries and extremely close
The effect of key.For battery under different stateofcharges memory requirement, at present both at home and abroad conventional battery storage life accelerate it is old
Change evaluation method and be generally empirical method.The rule of thumb is set up on the basis of experience, is proposed on the basis of certain condition is met,
10 DEG C of the storage temperature rise of battery, about one times of the capacity attenuation speed increase of battery, this method can simply estimate lithium
The full electric charge storage life of ion battery, but the confidence level of data is not high, there is the shortcomings of accuracy is not high, evaluation time is long.
Therefore, how accurately to estimate the full electric charge storage life of lithium ion battery, have become lithium ion battery by wide popularization and application
When be badly in need of one of key technical problem for solving.
The content of the invention
The technical problem to be solved in the present invention is：For the above mentioned problem of prior art, there is provided a kind of life assessment principle
Fully, high data reliability and it is highprecision, simple and easy to apply, be easily achieved, evaluation time is short, have a wide range of application, can to lithium from
The improvement of the storage life of sub battery provides the full electric charge storage life evaluation method of lithium ion battery of reliable technical support.
In order to solve the abovementioned technical problem, the technical solution adopted by the present invention is：
A kind of full electric charge storage life evaluation method of lithium ion battery, step includes：
1) storage temperature scope is determined according to the storage minimum temperature and electrolyte decomposition temperature of lithium ion battery to be evaluated,
Multiple storage temperature value T are chosen according to default temperature interval in the range of storage temperature, the full electric charge of lithium ion battery will be evaluated
The battery sample of state stores every under the sample time t specified, each storage temperature value T under multiple storage temperature value T
A kind of sample time t obtains each sample point as a sample point, at least one battery sample of each sample point correspondence
The capacitance loss rate Q generation experimental datas of lower battery sample；
2) capacity attenuation Ageing Model shown in formula (1) is set up, experimental error is considered on the basis of the experimental data, and
Model parameter ρ value is determined using capacity attenuation Ageing Model shown in formula (1)；It is determined that on the basis of model parameter ρ value, profit
Capacity attenuation Ageing Model shown in formula (1) is fitted with the nonlinear curve function of origin softwares, each is obtained and deposits
The value of simulation curve and model parameter a under temperature storage angle value T；
In formula (1), Q represents the capacitance loss rate of battery sample, and T represents storage temperature value, and t represents sample time, a, ρ,
A, B are model parameter to be solved；
3) whether meet Arrhenius formula based on the model parameter a under each storage temperature value T to judge storage
The selection reasonability of temperature range, determines in experimental data capacitance loss rate Q, storage temperature value T, sample time if rationally
T error range simultaneously redirects execution step 4)；If unreasonable, one storage temperature value T of highest is cast out from experimental data
Number of sampling evidence, redirect execution step 3)；
4) capacitance loss rate Q in experimental data, storage temperature value T, sample time t and model parameter ρ value are substituted into
Formula (2) illustrated equation obtains the value of model parameter A, B, and is based on appearance in the experimental data using Monte Carlo methods
Amount loss rate Q, storage temperature value T, sample time t error range draw the value of n group model parameters A, B, to n group model parameters
A, B value carry out the average and standard deviation that statistics draws model parameter A, B；
X=(R^{T}R)^{1}R^{T}Q (2)
In formula (2), R^{T}Representing matrix R transposed matrix, shown in matrix Q expression formula such as formula (3), matrix R expression formula
As shown in formula (4), shown in matrix x expression formula such as formula (5)；
In formula (3), Q_{11}Represent first storage temperature value T_{11}Lower first sample time t_{11}Corresponding capacitance loss rate,
Q_{mn}Represent mth of storage temperature value T_{mn}Lower nth of sample time t_{mn}Corresponding capacitance loss rate；
In formula (4), T_{11}Represent first storage temperature value, T_{mn}Represent mth of storage temperature value, t_{11}First is represented to deposit
Temperature storage angle value T_{11}Under first sample time t_{11}, t_{mn}Represent mth of storage temperature value T_{mn}Under nth of sample time；
In formula (5), the matrix in x expressions (2), ρ, A, B are model parameter to be solved；
5) average of model parameter ρ value and model parameter A, B is substituted into capacity attenuation Ageing Model shown in formula (1),
And carried out curve fitting using origin curve matchings, judge that fitting obtains the coefficient of determination R of curve^{2}Whether default threshold is more than
Value, then judges that the goodness of fit meets requirement if greater than default threshold value, redirects execution step 6)；Otherwise the goodness of fit is judged not
Meet and require, adjustment capacitance loss rate Q, storage temperature value T, sample time t error range redirect execution step 4)；
6) interval of model parameter A, B is determined according to the average and standard deviation of model parameter A, B, taken described
In value is interval, the value of model parameter A, B is solved to the confidence area of the average under default confidence level according to known standard deviation
Between mode carry out random value, obtain the value of multigroup model parameter A, B；By Monte Carlo method by model parameter ρ
Value and the value of multigroup model parameter A, B substitute into capacity attenuation Ageing Model shown in formula (1) to lithium ion to be evaluated
The lifespan of battery at normal temperatures is predicted, and obtains the lifespan distribution map of lithium ion battery to be evaluated.
Preferably, the step 1) after also include revision sample time t the step of, specific steps include：Judgment experiment number
Whether the capacitance loss rate Q of battery sample exceedes default lossrate threshold under each storage temperature value T in, if some
The capacitance loss rate Q of battery sample exceedes lossrate threshold under storage temperature value T, then when by the sampling under storage temperature value T
Between t revise and obtain new sample point, and the battery sample of the full state of charge of storage is placed based on new sample point, obtains new
Sample point under battery sample capacitance loss rate Q；
Preferably, the step 1) in capacitance loss rate Q calculation expression such as formula (6) shown in；
Q=1Q_{1}/Q_{0} (6)
In formula (6), Q is the capacitance loss rate of battery sample under some sample point, Q_{0}Represent full electric charge battery sample
Capacity, Q_{1}Represent capacity of the battery sample in sample point.
Preferably, the step 1) in each sample point correspondence three battery samples, and generation experimental data after also wrap
Include data and reject and mend the step of surveying, detailed step includes：For three capacity of three battery samples of each sample point
Loss late Q, obtains maximum therein, minimum value and median first, then enters median with maximum, minimum value respectively
Row compare, if the error of median and maximum, median and and minimum value error all in predetermined threshold value, this is sampled
The capacitance loss rate Q values of point are maximum, minimum value, the average of median three；If only maximum in maximum, minimum value
Exceed predetermined threshold value with the error of median, then reject maximum, by the capacitance loss rate Q values of the sample point be median with
With the average of minimum value；If only the error of minimum value and median exceedes predetermined threshold value in maximum, minimum value, reject minimum
Value, by the capacitance loss rate Q values of the sample point be median with and maximum average；If both maximum, minimum value and
The error of median exceedes predetermined threshold value, then reapposes battery sample for the sample point and mend survey capacitance loss rate Q.
Preferably, the step 3) detailed step it is as follows：
3.1) value for the variable a being directed in experimental data under each storage temperature value T, is intended using origin linearity curves
Lna values are shared to 1/T mapping matched curves；
3.2) coefficient of determination R of curve is obtained according to fitting^{2}Judge that fitting obtains whether curve is straight line, if be fitted
It is straight line to curve, then judges that each storage temperature value T drag parameter a meets Arrhenius formula, storage temperature model
The selection enclosed rationally, determines capacitance loss rate Q in experimental data, storage temperature value T, sample time t error range and redirected
Perform step 4)；If to obtain curve be not straight line for fitting, judge each storage temperature value T drag parameter a be unsatisfactory for Ah
Lun Niwusi formula, the selection of storage temperature scope is unreasonable, and taking for one storage temperature value T of highest is cast out from experimental data
Sampling point data, redirect execution step 3).
Preferably, the step 4) in n group model parameters A, B value in n values be more than 1000.
Preferably, the step 4) in the average that statistics draws model parameter A, B is carried out to the values of n group model parameters A, B
Shi Caiyong function expression is specific as shown in formula (7)；
In formula (7),Represent to calculate obtained average, X_{i}Ith of value in n group model parameters A or B is represented, n represents mould
Shape parameter A or B quantity.
Preferably, the step 4) in the standard that statistics draws model parameter A, B is carried out to the values of n group model parameters A, B
The function expression used when poor is specific as shown in formula (8)；
In formula (8), S represents to calculate obtained standard deviation, X_{i}Ith of value in n group model parameters A or B is represented, n is represented
Model parameter A or B quantity,Represent n group model parameters A or B average.
Preferably, the step 6) in the value of model parameter A, B solved according to known standard deviation put default
When confidence interval of mean mode carries out random value under reliability, the confidential interval of random value is specific as shown in formula (9)；
In formula (9),N group model parameters A or B average are represented, S represents n group model parameters A or B standard deviation, n tables
Representation model parameter A or B quantity, μ are quantile, and 1 α are confidential interval.
The full electric charge storage life evaluation method tool of lithium ion battery of the present invention has the advantage that：
1st, life assessment principle is abundant.The present invention is by step 1) the battery sample of lithium ion battery completely state of charge will be evaluated
Product store the sample time t generation experimental datas specified under multiple storage temperature value T, and are declined in capacity shown in formula of setting up (1)
Subtract after Ageing Model, whether meet Arrhenius formula based on each storage temperature value T drag parameter a to judge storage
The selection reasonability of temperature range, therefore the present invention is the aging based on statistical analysis and Arrhenius formula to battery
Cheng Jinhang Acceleration studies are analyzed, and are made full use of statistical principle to simulate capacity attenuation in ageing process, are derived electricity
The lifespan in pond, with the sufficient advantage of life assessment principle.
2nd, high data reliability and high accuracy.Traditional life assessment method obtains battery generally according to the method for experience
Accelerated factor k (stores one day equivalent to k days under storage at room temperature) in the case where accelerating temperature T, and its error and accuracy are poor.And this
Inventive step 6) interval of model parameter A, B is determined according to the average and standard deviation of model parameter A, B, taken described
In value is interval, the value of model parameter A, B is solved to the confidence area of the average under default confidence level according to known standard deviation
Between mode carry out random value, obtain the value of multigroup model parameter A, B；By Monte Carlo method by model parameter ρ
Value and the value of multigroup model parameter A, B substitute into capacity attenuation Ageing Model shown in formula (1) to lithium ion to be evaluated
The lifespan of battery at normal temperatures is predicted, and obtains the lifespan distribution map of lithium ion battery to be evaluated, therefore based on statistics
Confidence level, the rate of decay of the battery drawn on the basis of the statistical analysis such as distributed area, therefore credible with high data
Degree and highprecision advantage.
3rd, it is simple and easy to apply, be easily achieved：The full electric charge storage life evaluation method of lithium ion battery of the present invention is simple and easy to apply,
Simple to operate on the basis of control evaluation environment precision, the program of its simulation process, which is obtained, to be also easy to realize.
4th, evaluation time is short.Conventional batteries life assessment method typically accelerates to combine the side that normal temperature is verified using single high temperature
Method, due to battery, the lifespan is generally several years at normal temperatures, therefore the correctness of checking model and accelerated factor is timeconsuming long.The present invention
The method that the full electric charge storage life evaluation method of lithium ion battery is tested using multitemperature utilizes room temperature and other higher temperatures
The correctness of point contrast verification model and accelerated factor simultaneously, the correct of model and accelerated factor can be verified in the short period of time
Property.
5th, the full electric charge storage life evaluation method of lithium ion battery of the present invention to lithium ion battery under full charge condition
Storage life is evaluated, and the lifespan of lithium battery can be evaluated fast and reliablely, can be provided for the producer and user lithium from
The evaluation method of storage life of the sub battery under full charge condition, ensures the production and application of product, can be to lithiumion electric
The improvement of the storage life in pond provides reliable technical support.
Brief description of the drawings
Fig. 1 is the basic procedure schematic diagram of the embodiment of the present invention one (embodiment two).
Fig. 2 is the step 2 of the embodiment of the present invention one) it is fitted obtained curve (aging tendency matched curve).
Fig. 3 is the step 5 of the embodiment of the present invention one) it is fitted obtained curve and real data compares figure.
Fig. 4 is the step 6 of the embodiment of the present invention one) obtained lifespan distribution map.
Fig. 5 is the step 3 of the embodiment of the present invention one) in figure is judged to the obtained activation energy of 1/T mappings with lna values.
Fig. 6 is the step 2 of the embodiment of the present invention two) it is fitted obtained curve (aging tendency matched curve).
Fig. 7 is the step 3 of the embodiment of the present invention two) in figure is judged to the obtained activation energy of 1/T mappings with lna values.
Fig. 8 is the step 5 of the embodiment of the present invention two) it is fitted obtained curve and real data compares figure.
Fig. 9 is the step 6 of the embodiment of the present invention two) obtained lifespan distribution map.
Embodiment
Hereafter will be using certain external battery of producer 18650 and certain domestic battery of producer 18650 as embodiment, to this
The full electric charge storage life evaluation method of invention lithium ion battery is further detailed.
Embodiment one：
As shown in figure 1, the present embodiment lithium ion battery includes the step of expiring electric charge storage life evaluation method：
1) determine that experiment condition carries out experiment generation experimental data：According to the storage minimum temperature of lithium ion battery to be evaluated
Storage temperature scope is determined with electrolyte decomposition temperature, multiple deposit is chosen according to default temperature interval in the range of storage temperature
Temperature storage angle value T, the battery sample for evaluating the full state of charge of lithium ion battery is stored under multiple storage temperature value T taking of specifying
Each sample time t under sample time t, each storage temperature value T is as a sample point, and each sample point correspondence is extremely
A few battery sample, obtains the capacitance loss rate Q generation experimental datas of battery sample under each sample point.
Requirement condition currently for the lithium ion lifespan is usually to press room temperature mathematic(al) expectation.Certain outer producer of the present embodiment China
The storage minimum temperature of 18650 batteries is designed as 298K, and general lithiumion battery electrolytes decomposition temperature is set in 353K or so
Determine maximum temperature for 343K and take interval temperature 10K, the storage temperature value T of selection be respectively 298K, 313K, 323K, 333K,
343K, the sample time t specified are designed as 30 days, 60 days, 90 days, 150 days, 210 days, 270 days, 360 days, each sample point
Three battery samples of correspondence, therefore the testing scheme of each sample point of design is as shown in table 1.
Table 1：The testing scheme of each sample point of embodiment one.
In the present embodiment, step 1) after the step of also include revision sample time t, specific steps include：Judgment experiment number
Whether the capacitance loss rate Q of battery sample exceedes default lossrate threshold under each storage temperature value T in, if some
The capacitance loss rate Q of battery sample exceedes lossrate threshold under storage temperature value T, then when by the sampling under storage temperature value T
Between t revise and obtain new sample point, and the battery sample of the full state of charge of storage is placed based on new sample point, obtains new
Sample point under battery sample capacitance loss rate Q；It should be noted that the step of foregoing revision sample time t is not
Necessary step, such as when storage temperature scope value is reasonable, then can need not revise sample time t completely.
In the present embodiment, the capacitance loss rate Q that 30 days battery samples are stored under 343K is about 18%, already close to battery
End of life (20%), it is therefore desirable to revised to the sample time t under 343K, the sample time t under revised 343K
Respectively 1/2 week, 1 week, 2 weeks, 3 weeks ... (i.e. 3 days, 7 days, 14 days ...), therefore the present embodiment enters to the sample time t under 343K
After row revision, the final test scheme of each sample point is substantially as shown in table 2.
Table 2：The final test scheme of each sample point of embodiment one.
2) capacity attenuation Ageing Model shown in formula (1) is set up, experimental error is considered on the basis of the experimental data, and
Model parameter ρ value is determined using capacity attenuation Ageing Model shown in formula (1)；It is determined that on the basis of model parameter ρ value, profit
Capacity attenuation Ageing Model shown in formula (1) is fitted with the nonlinear curve function of origin softwares, each is obtained and deposits
The value of simulation curve and model parameter a under temperature storage angle value T；
In formula (1), Q represents the capacitance loss rate of battery sample, and T represents storage temperature value, and t represents sample time, a, ρ,
A, B are model parameter to be solved；
In the present embodiment, by considering experimental error using the acquisition model parameter of capacity attenuation Ageing Model shown in formula (1) ρ
Value about between 0.47~0.55, for simplify it is followup calculate, modulus shape parameter ρ value is 0.5 (ρ=0.5).Taking ρ=0.5
Afterwards, the simulation curve being fitted under obtained different temperatures using the nonlinear curve function of origin softwares is as shown in Figure 2.
Referring to Fig. 2, curve is respectively that battery is damaged in 298K, 313K, 323K, 333K, 343K storage different time capacity from the bottom to top
Data point is respectively the actual capacitance loss rate Q of sample point on mistake rate Q curve matching value, curve.Right part a1~a5 difference tables
Show the value and its error of the model parameter a at a temperature of 298K, 313K, 323K, 333K, 343K, R^{2}Represent the coefficient of determination.
3) whether meet Arrhenius formula based on the model parameter a under each storage temperature value T to judge storage
The selection reasonability of temperature range, determines in experimental data capacitance loss rate Q, storage temperature value T, sample time if rationally
T error range simultaneously redirects execution step 4)；If unreasonable, one storage temperature value T of highest is cast out from experimental data
Number of sampling evidence, redirect execution step 3).
4) capacitance loss rate Q in experimental data, storage temperature value T, sample time t and model parameter ρ value are substituted into
Formula (2) illustrated equation obtains the value of model parameter A, B, and is based on appearance in the experimental data using Monte Carlo methods
Amount loss rate Q, storage temperature value T, sample time t error range draw the value of n group model parameters A, B, to n group model parameters
A, B value carry out the average and standard deviation that statistics draws model parameter A, B；
X=(R^{T}R)^{1}R^{T}Q (2)
In formula (2), R^{T}Representing matrix R transposed matrix, shown in matrix Q expression formula such as formula (3), matrix R expression formula
As shown in formula (4), shown in matrix x expression formula such as formula (5)；
In formula (3), Q_{11}Represent first storage temperature value T_{11}Lower first sample time t_{11}Corresponding capacitance loss rate,
Q_{mn}Represent mth of storage temperature value T_{mn}Lower nth of sample time t_{mn}Corresponding capacitance loss rate；
In formula (4), T_{11}Represent first storage temperature value, T_{mn}Represent mth of storage temperature value, t_{11}First is represented to deposit
Temperature storage angle value T_{11}Under first sample time t_{11}, t_{mn}Represent mth of storage temperature value T_{mn}Under nth of sample time；
In formula (5), the matrix in x expressions (2), ρ, A, B are model parameter to be solved.
In the present embodiment, the average and standard deviation A=11.5 (0.76) of model parameter A, B, B=3605 (243.7), wherein
Numerical value before bracket is the standard deviation that numerical value in average, bracket is parameter.
5) average of model parameter ρ value and model parameter A, B is substituted into capacity attenuation Ageing Model shown in formula (1),
And carried out curve fitting using origin curve matchings, obtained curve and real data comparison diagram is as shown in figure 3, judge fitting
Obtain the coefficient of determination R of curve^{2}Whether default threshold value is more than, then judging that the goodness of fit is met if greater than default threshold value wants
Ask, redirect execution step 6)；Otherwise judge that the goodness of fit is unsatisfactory for requiring, adjustment capacitance loss rate Q, storage temperature value T, sampling
Time t error range, redirects execution step 4).
Default threshold value is 0.9 in the present embodiment, if R^{2}>0.9, then it is assumed that matching degree can receive, if R^{2}<0.9 is recognized
Unacceptable for fitting degree, then need to jump to step 4), so as to expand or reduce capacitance loss rate Q, storage temperature value T,
A, B value are calculated on the basis of sample time t error range again.In the present embodiment, fitting obtains the coefficient of determination of curve
R^{2}≈ 0.97, is required therefore, it is determined that the goodness of fit is met, and redirects execution step 7).
6) interval of model parameter A, B is determined according to the average and standard deviation of model parameter A, B, taken described
In value is interval, the value of model parameter A, B is solved to the confidence area of the average under default confidence level according to known standard deviation
Between mode carry out random value, obtain the value of multigroup model parameter A, B；By Monte Carlo method by model parameter ρ
Value and the value of multigroup model parameter A, B substitute into capacity attenuation Ageing Model shown in formula (1) to lithium ion to be evaluated
Lifespan (the present embodiment with capacitance loss 20% be end of life) of the battery under normal temperature (298K) is predicted, and obtains to be evaluated
The lifespan distribution map of lithium ion battery is as shown in Figure 4.
As can be seen that the lifespan distributed area of battery is 2.54.8 from Fig. 4 statistics, and at 1000 times
In the simulation of Monte Carlo methods, the lifespan of battery calculated for 100 times is there are about below 3 years, there is the longevity calculated for 900 times
Life is more than 3 years.This just illustrates confidence level of the battery life more than 3 years 90%.With reference to the curve of lifespan distribution map, according to
To the evaluation of battery life in real life, it is believed that the average life span of battery is about 3.5 years, i.e., the lithium ion battery is in full electricity
The evaluation result of storage life under the conditions of lotus is 3.5 years.
In the present embodiment, step 1) in specifically refer to obtain the capacity of battery sample under each sample point according to formula (6)
Loss late Q；
Q=1Q_{1}/Q_{0} (6)
In formula (6), Q is the capacitance loss rate of battery sample under some sample point, Q_{0}Represent full electric charge battery sample
Capacity, Q_{1}Represent capacity of the battery sample in sample point.
In the present embodiment, step 1) in each sample point correspondence three battery samples, step 2) after also picked including data
The step of removing and mend survey, detailed step includes：For three capacitance loss rate Q of three battery samples of each sample point,
Maximum therein, minimum value and median are obtained first, are then compared median with maximum, minimum value respectively,
If the error of median and maximum, median and and minimum value error all in predetermined threshold value (specific value in the present embodiment
Then it is maximum, minimum value, the average of median three by the capacitance loss rate Q values of the sample point in 5%)；If maximum
Only the error of maximum and median exceedes predetermined threshold value in value, minimum value, then rejects maximum, and the capacity of the sample point is damaged
Mistake rate Q values be median with and minimum value average；If the error of only minimum value and median exceedes in maximum, minimum value
Predetermined threshold value, then reject minimum value, by the capacitance loss rate Q values of the sample point be median with and maximum average；If
The error of both maximum, minimum value and median exceedes predetermined threshold value, then reapposes battery sample for the sample point
And mend survey capacitance loss rate Q.Because the uniformity of the preparation of battery is likely to occur problem, and consistency problem under the high temperature conditions
It may be extended, it is therefore desirable to carry out the rejecting to experimental result appropriateness and mend to survey, can effectively eliminate because prepared by battery
Uniformity inequality produce accidental error, it is possible to increase the accuracy of experimental data.
In the present embodiment, the step 4) detailed step it is as follows：
4.1) value for the variable a being directed in experimental data under each storage temperature value T, is intended using origin linearity curves
Lna values are shared to 1/T mapping matched curves (as shown in Figure 3)；
4.2) coefficient of determination R of curve is obtained according to fitting^{2}Judge that fitting obtains whether curve is straight line, if be fitted
It is straight line to curve, then judges that each storage temperature value T drag parameter a meets Arrhenius formula, storage temperature model
The selection enclosed rationally, determines capacitance loss rate Q in experimental data, storage temperature value T, sample time t error range and redirected
Perform step 5)；If to obtain curve be not straight line for fitting, judge each storage temperature value T drag parameter a be unsatisfactory for Ah
Lun Niwusi formula, the selection of storage temperature scope is unreasonable, and taking for one storage temperature value T of highest is cast out from experimental data
Sampling point data, redirect execution step 3).Referring to Fig. 5, data point is respectively to be calculated using a values obtained under different temperatures in Fig. 5
1/T is mapped with lna values using origin linear curve fits in the lna values and 1/T values gone out, the present embodiment, matched curve
Coefficient of determination R^{2}=0.99, this illustrative graph is straight line to 1/T in whole temperature range lna values, therefore can be determined that a values
Arrhenius formula is met with temperature, therefore this sample meets Arrhenius in this experimental temperature scope (298K343K)
Formula, i.e., activation energy is consistent in whole temperature range.
In the present embodiment, the step 5) in n group model parameters A, B value in n values be more than 1000.N group model parameters
N values in A, B value are the number of times that Monte Carlo methods are simulated, the Monte Carlo methods simulation when n values are more than 1000
Data cause structure more accurate.
In the present embodiment, the step 5) in statistics carried out to the values of n group model parameters A, B draw model parameter A, B
The function expression used during average is specific as shown in formula (7)；
In formula (7),Represent to calculate obtained average, X_{i}Ith of value in n group model parameters A or B is represented, n represents mould
Shape parameter A or B quantity.
Preferably, the step 5) in the standard that statistics draws model parameter A, B is carried out to the values of n group model parameters A, B
The function expression used when poor is specific as shown in formula (8)；
In formula (8), S represents to calculate obtained standard deviation, X_{i}Ith of value in n group model parameters A or B is represented, n is represented
Model parameter A or B quantity,Represent n group model parameters A or B average.
In the present embodiment, the step 7) in the value of model parameter A, B is solved default according to known standard deviation
Confidence level under confidence interval of mean mode when carrying out random value, the confidential interval of random value is specific as shown in formula (9)；
In formula (9),N group model parameters A or B average are represented, S represents n group model parameters A or B standard deviation, n tables
Representation model parameter A or B quantity, μ are quantile, and 1 α are confidential interval.In the present embodiment, corresponding point of the α in confidential interval
Digit μ values are specifically as shown in table 3.
Table 3：, the corresponding quantile μ value tables of α in confidential interval.
α  0.90  0.95  0.975  0.99  0.995  0.999 
μ  1.282  1.646  1.960  2.326  2.576  3.090 
Embodiment two：
The present embodiment is identical with the basic step of embodiment one, and its main difference is：
In the present embodiment step 1) in, in view of testing the starting stage finds this example battery capacity attenuation mistake under 343K
It hurry up, the highest that this example is taken accelerates temperature 333K, it is 5K to take interval temperature range, take four interval temperature ranges, therefore store
Temperature is set as 298K, 318K, 323K, 328K, 333K, and will be set to sample time 15 days, 30 days, 45 days, 60 days, 90
My god.Simultaneously as the capacity difference of the cell of the battery of example 2 is larger, therefore increase sampling number is to 5, finally gives each
The final test scheme of individual sample point is as shown in table 4, in addition also to capacitance loss rate Q, storage in experimental data in the present embodiment
Temperature value T, sample time t error range are expanded, and equally also excessive to battery error point carries out appropriate deletion
Surveyed with mending.
Table 4：The final test scheme of each sample point of embodiment two.
In the present embodiment step 2) in, by consider experimental error using model obtain the scopes of ρ values about 0.42~
Between 0.57, to simplify followup calculating, ρ=0.5 is equally taken.Meanwhile, after ρ=0.5 is taken, obtain simulation at different temperatures
Curve and a values.Step 2) in it is as shown in Figure 6 using the obtained curve of nonlinear curve function fitting of origin softwares.Ginseng
See Fig. 6, curve is respectively storage different time capacity attenuation of the battery in 298K, 318K, 323K, 328K, 333K from the bottom to top
Data point is respectively actual capacity attenuation average value on curve matching value, curve, right part a1~a5 represent respectively 298K,
318K, 323K, 328K, 333K a values and its error, R^{2}Represent the coefficient of determination.
In the present embodiment step 3) in, it is determined that behind ρ=0.5, using origin linear curve fits with lna values to 1/T
The curve that mapping matched curve is obtained is as shown in Figure 7.Data results show the coefficient of determination R of the curve of fitting^{2}=
0.9963, this illustrative graph is that straight line, i.e. a values meet Arrhenius with temperature to 1/T in whole temperature range lna values
Formula, therefore this sample meets Arrhenius formula in this experimental temperature scope (298K333K), i.e., in whole temperature range
Interior activation energy is consistent.Data point is respectively the lna and 1/T calculated using a values obtained under different temperatures pass in wherein Fig. 7
System.
In the present embodiment step 4) in, the average and standard deviation A=4.85 (0.53), B=1483 of model parameter A, B
(138.7), the numerical value before its bracket is the standard deviation that the numerical value in average, bracket is parameter.
In the present embodiment step 5) in, the average of model parameter ρ value 0.5 and model parameter A, B is substituted into formula (1) institute
Show capacity attenuation Ageing Model, and the pass of carried out curve fitting using origin curve matchings obtained curve and actual data point
System is as shown in Figure 8.In Fig. 8, the actual data and its error range obtained in data point position, the curve that curve obtains for fitting is public
Formula is the fitting formula of whole temperature range, and by being known that the coefficient of determination (R after the fitting of Origin softwares^{2}) ≈ 0.96,
This illustrative graph fitting degree is fine.
In the present embodiment step 6) in, lithium ion battery to be evaluated is obtained based on 1000 Monte Carlo methods simulations
Lifespan distribution map as shown in figure 9, from Fig. 9 statistics as can be seen that battery lifespan distributed area be 2.2~2.7
Year, and in the simulation of 1000 Monte Carlo methods, the lifespan of battery calculated for 100 times is there are about below 2.3 years, is had
The lifespan calculated for 900 times is more than 2.3 years.This just illustrates confidence level of the battery life more than 2.3 years 90%.Battery
Average life span is about 2.5 years.With reference to lifespan distribution curve, according to the evaluation in real life to battery life, it is believed that battery
Average life span be about 2.5 years, i.e., the evaluation result of storage life of such a lithium ion battery under full charge condition is 2.5
Year.
Described above is only the preferred embodiment of the present invention, and protection scope of the present invention is not limited merely to abovementioned implementation
Example, all technical schemes belonged under thinking of the present invention belong to protection scope of the present invention.It should be pointed out that for the art
Those of ordinary skill for, some improvements and modifications without departing from the principles of the present invention, these improvements and modifications
It should be regarded as protection scope of the present invention.
Claims (9)
1. the full electric charge storage life evaluation method of a kind of lithium ion battery, it is characterised in that step includes：
1) storage temperature scope is determined according to the storage minimum temperature and electrolyte decomposition temperature of lithium ion battery to be evaluated, deposited
Multiple storage temperature value T are chosen according to default temperature interval in the range of storing temperature, the full state of charge of lithium ion battery will be evaluated
Battery sample each under the sample time t specified, each storage temperature value T is stored under multiple storage temperature value T
Sample time t obtains electric under each sample point as a sample point, at least one battery sample of each sample point correspondence
The capacitance loss rate Q generation experimental datas of pond sample；
2) capacity attenuation Ageing Model shown in formula (1) is set up, experimental error is considered on the basis of the experimental data, and utilize
Capacity attenuation Ageing Model shown in formula (1) determines model parameter ρ value；It is determined that on the basis of model parameter ρ value, utilizing
The nonlinear curve function of origin softwares is fitted to capacity attenuation Ageing Model shown in formula (1), obtains each storage
The value of simulation curve and model parameter a under temperature value T；
In formula (1), Q represents the capacitance loss rate of battery sample, and T represents storage temperature value, and t represents sample time, and a, ρ, A, B are equal
For model parameter to be solved；
3) storage temperature is judged based on whether the model parameter a under each storage temperature value T meets Arrhenius formula
The selection reasonability of scope, determines capacitance loss rate Q, storage temperature value T, sample time t in experimental data if rationally
Error range simultaneously redirects execution step 4)；If unreasonable, taking for one storage temperature value T of highest is cast out from experimental data
Sampling point data, redirect execution step 3)；
4) capacitance loss rate Q in experimental data, storage temperature value T, sample time t and model parameter ρ value are substituted into formula (2)
Illustrated equation obtains the value of model parameter A, B, and is based on capacitance loss in the experimental data using Monte Carlo methods
Rate Q, storage temperature value T, sample time t error range draw the value of n group model parameters A, B, to n group model parameters A, B
Value carries out the average and standard deviation that statistics draws model parameter A, B；
X=(R^{T}R)^{1}R^{T}Q (2)
In formula (2), R^{T}Representing matrix R transposed matrix, shown in matrix Q expression formula such as formula (3), matrix R expression formula such as formula
(4) shown in, shown in matrix x expression formula such as formula (5)；
In formula (3), Q_{11}Represent first storage temperature value T_{11}Lower first sample time t_{11}Corresponding capacitance loss rate, Q_{mn}Table
Show mth of storage temperature value T_{mn}Lower nth of sample time t_{mn}Corresponding capacitance loss rate；
In formula (4), T_{11}Represent first storage temperature value, T_{mn}Represent mth of storage temperature value, t_{11}Represent first storage temperature
Angle value T_{11}Under first sample time t_{11}, t_{mn}Represent mth of storage temperature value T_{mn}Under nth of sample time；
In formula (5), the matrix in x expressions (2), ρ, A, B are model parameter to be solved；
5) average of model parameter ρ value and model parameter A, B is substituted into capacity attenuation Ageing Model shown in formula (1), and profit
Carried out curve fitting with origin curve matchings, judge that fitting obtains the coefficient of determination R of curve^{2}Whether default threshold value is more than,
Then judge that the goodness of fit meets requirement if greater than default threshold value, redirect execution step 6)；Otherwise judge that the goodness of fit is discontented with
Foot requires that adjustment capacitance loss rate Q, storage temperature value T, sample time t error range redirect execution step 4)；
6) interval of model parameter A, B is determined according to the average and standard deviation of model parameter A, B, in the value area
In, the value of model parameter A, B is solved into the confidence interval of mean side under default confidence level according to known standard deviation
Formula carries out random value, obtains the value of multigroup model parameter A, B；By Monte Carlo method by model parameter ρ value
And the value of multigroup model parameter A, B substitutes into capacity attenuation Ageing Model shown in formula (1) to lithium ion battery to be evaluated
Lifespan at normal temperatures is predicted, and obtains the lifespan distribution map of lithium ion battery to be evaluated.
2. the full electric charge storage life evaluation method of lithium ion battery according to claim 1, it is characterised in that the step
1) the step of also including revision sample time t after, specific steps include：It is electric under each storage temperature value T in judgment experiment data
Whether the capacitance loss rate Q of pond sample exceedes default lossrate threshold, if battery sample under some storage temperature value T
Capacitance loss rate Q exceedes lossrate threshold, then by the sample time t under storage temperature value T revise obtaining new sampling
Point, and the battery sample for storing full state of charge is placed based on new sample point, obtain the appearance of battery sample under new sample point
Amount loss rate Q.
3. the full electric charge storage life evaluation method of lithium ion battery according to claim 2, it is characterised in that the step
1) in shown in capacitance loss rate Q calculation expression such as formula (6)；
Q=1Q_{1}/Q_{0} (6)
In formula (6), Q is the capacitance loss rate of battery sample under some sample point, Q_{0}The capacity of full electric charge battery sample is represented,
Q_{1}Represent capacity of the battery sample in sample point.
4. the full electric charge storage life evaluation method of lithium ion battery according to claim 3, it is characterised in that the step
1) sample point of each in three battery samples of correspondence, and also include data rejecting after generation experimental data and mend the step of surveying,
Detailed step includes：For three capacitance loss rate Q of three battery samples of each sample point, obtain first it is therein most
Median, is then compared, if median and maximum by big value, minimum value and median with maximum, minimum value respectively
Error, median and and minimum value error all in predetermined threshold value, then by the capacitance loss rate Q values of the sample point for most
Big value, minimum value, the average of median three；If the only error of maximum and median is more than default in maximum, minimum value
Threshold value, then reject maximum, by the capacitance loss rate Q values of the sample point be median with and minimum value average；If maximum
Only the error of minimum value and median exceedes predetermined threshold value in value, minimum value, then rejects minimum value, and the capacity of the sample point is damaged
Mistake rate Q values be median with and maximum average；If the error of both maximum, minimum value and median exceedes default
Threshold value, then reappose battery sample for the sample point and mend survey capacitance loss rate Q.
5. the full electric charge storage life evaluation method of lithium ion battery according to claim 1 or 2 or 3 or 4, its feature exists
In the step 3) detailed step it is as follows：
3.1) value for the variable a being directed in experimental data under each storage temperature value T, is used using origin linear curve fits
Lna values are to 1/T mapping matched curves；
3.2) coefficient of determination R of curve is obtained according to fitting^{2}Judge that fitting obtains whether curve is straight line, if fitting obtains song
Line is straight line, then judges that each storage temperature value T drag parameter a meets Arrhenius formula, storage temperature scope
Selection is reasonable, determines capacitance loss rate Q in experimental data, storage temperature value T, sample time t error range and redirects execution
Step 4)；If it is not straight line that fitting, which obtains curve, judge that each storage temperature value T drag parameter a is unsatisfactory for Allan Buddhist nun
This black formula, the selection of storage temperature scope is unreasonable, and one storage temperature value T of highest sample point is cast out from experimental data
Data, redirect execution step 3).
6. the full electric charge storage life evaluation method of lithium ion battery according to claim 5, it is characterised in that：The step
4) the n values in the value of n group models parameter A, B are more than 1000.
7. the full electric charge storage life evaluation method of lithium ion battery according to claim 6, it is characterised in that：The step
4) function expression for the value of n group model parameters A, B use when statistics draws the average of model parameter A, B in is specific such as
Shown in formula (7)；
In formula (7),Represent to calculate obtained average, X_{i}Ith of value in n group model parameters A or B is represented, n represents that model is joined
Number A or B quantity.
8. the full electric charge storage life evaluation method of lithium ion battery according to claim 7, it is characterised in that：The step
4) function expression for the value of n group model parameters A, B use when statistics draws the standard deviation of model parameter A, B in is specific
As shown in formula (8)；
In formula (8), S represents to calculate obtained standard deviation, X_{i}Ith of value in n group model parameters A or B is represented, n represents that model is joined
Number A or B quantity,Represent n group model parameters A or B average.
9. the full electric charge storage life evaluation method of lithium ion battery according to claim 8, it is characterised in that the step
6) value of model parameter A, B is solved into the confidence interval of mean mode under default confidence level according to known standard deviation in
When carrying out random value, the confidential interval of random value is specific as shown in formula (9)；
In formula (9),N group model parameters A or B average are represented, S represents n group model parameters A or B standard deviation, and n represents mould
Shape parameter A or B quantity, μ are quantile, and 1 α are confidential interval.
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