CN113011012B - Box-Cox change-based energy storage battery residual life prediction method - Google Patents
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Abstract
The invention discloses a method for predicting the residual life of an energy storage battery based on Box-Cox change, which comprises the following steps: s1, firstly, extracting historical capacity data of a battery from a battery historical capacity database; s2, linearizing the battery history capacity data extracted in the step S1; s3, solving the parameter lambda in the step S2 through a maximum likelihood estimation method; s4, solving parameters of a battery capacity attenuation track fitting equation through a least square method; s5, judging whether the current battery capacity is in front of the broom neck. According to the invention, through adding the broom effect of the battery, the battery attenuation effect is predicted by combining the multiple Box-cox changes with the maximum likelihood estimation method and the least square fitting, so that the prediction precision and convergence capacity of the residual service life of the battery are improved, a large number of battery offline aging experiments are avoided, and the practical applicability and convenience are improved.
Description
Technical Field
The invention relates to the technical field of a residual life prediction method of an energy storage battery, in particular to a residual life prediction method of an energy storage battery based on Box-Cox change.
Background
The remaining life of a battery refers to the number of cycles of cycle life that must be experienced under certain charge and discharge conditions for the maximum available capacity of the battery to decay to a certain specified failure threshold. The prediction of the remaining life of the battery is a process of predicting and calculating the remaining life of the battery based on historical data of the battery by using a certain mathematical means. At present, the method for predicting the remaining life of the battery is mainly divided into: 1. empirical prediction methods (including single-exponential models, double-exponential models, linear models, polynomial models, verhulst models, etc.); 2. filtering prediction methods (including Kalman filtering, extended Kalman filtering, unscented Kalman filtering, particle filtering, unscented example filtering, etc.). The experience prediction method has good online operation capability, but the predictability is poor, so that the actual use requirement of the battery is difficult to meet; while filtering prediction methods can improve the accuracy and convergence of empirical predictions methods, they increase the dependence of the algorithm on the model and complex data calculations.
However, in the existing technical means, an effective technical method for the defects of the two methods is not available, and the prediction accuracy and convergence capability of the residual life of the battery can be effectively improved, so that the invention provides the residual life prediction method of the energy storage battery based on Box-Cox change.
Disclosure of Invention
The invention aims to provide a residual life prediction method of an energy storage battery based on Box-Cox change, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a residual life prediction method of an energy storage battery based on Box-Cox change comprises the following steps:
s1, firstly, extracting historical capacity data of a battery from a battery historical capacity database;
s2, linearizing the battery history capacity data extracted in the step S1;
s3, solving the parameter lambda in the step S2 through a maximum likelihood estimation method;
s4, solving parameters of a battery capacity attenuation track fitting equation through a least square method;
s5, judging whether the current battery capacity is in front of a broom neck;
s6, if so, solving the residual life of the battery; if not, repeating steps S1-S5.
Further, in step S2, box-Cox linearization processing is performed on the historical capacity data of the battery using the following formula:
wherein:is the historical capacity observed value after Box-Cox conversionLambda is the Box-Cox transform coefficient and Cmax is the historical capacity observation before Box-Cox transform.
Further, in step S3, the parameter λ is solved using the following formula:
wherein: l (lambda) is a logarithmic natural function, n is the number of samples of the battery history capacity observation value, C is the battery history capacity observation vector, K is the cycle number vector corresponding to the battery history capacity, T is the transpose of the K vector, B is a parameter, B= (K) T k) - 1 k T C。
Further, after the Box-Cox transformation in steps S1-S3, when the battery history capacity data is linearized, the decay trajectory fitting equation of the battery capacity is:
where a is the slope of the fitted battery capacity decay trajectory fitting equation and b is the deviation of the fitted battery capacity decay trajectory fitting equation.
Further, in step S4, the parameters of the battery capacity fade trajectory fitting equation are solved using the following formula:
further, after obtaining the two parameters a and b of the fitting equation by the least square method, the cycle number n corresponding to the battery capacity failure threshold can be solved, namely, the fitting equation is as follows:
further, in step S5, the method for determining whether the current battery capacity is in front of the broom neck is as follows:
(1) Initializing charge and discharge of the battery through multiple times of small current, and establishing a mapping relation table of battery voltage V and charge and discharge capacity;
(2) The normal battery working charge and discharge under actual use is started, and the mapping relation table of the battery voltage and the charging capacity of each time is recorded;
(3) Setting an upper limit threshold lambda of a voltage difference value, and judging the voltage difference delta between the voltage V2 under the current circulation times and the voltage V1 under the last circulation times in real time V Size of the value:
if lambda > delta V Continue to look for another delta V ；
If lambda is less than or equal to delta V The battery capacity at this time, which is the "broom neck" of the battery, is recorded.
Further, in step S6, if it is determined that the current battery capacity is before the neck, the number of cycles n of the battery capacity failure threshold under the current charge and discharge environmental conditions of step S5 and the number of battery charge and discharge cycles n at that time are recorded n Remaining life n of battery s The method comprises the following steps:
n s ＝n-n n ；
if the current battery capacity is judged not to be in front of the broom neck, the BOX-c0X change is used once again for the performance attenuation curve behind the battery broom neck, the steps of S1-S5 are repeated, and then the cycle number n of the battery capacity failure threshold under the current charge and discharge environment condition and the cycle number n of the battery charge and discharge at the moment are recorded n Remaining life n of battery n The method comprises the following steps:
n s ＝n-n n 。
further, in the step (1), the specific method for establishing the mapping relation table of the battery voltage V and the charge-discharge capacity is as follows:
first,: the battery is initialized by charging and discharging with low current, and each single battery comprises:
(1) when in charging: uchar=ocv+ir internal battery resistance;
(2) when discharging, the following steps are carried out: uidis=ocv-IR internal cell resistance;
(1) the formula in + (2): ocv= (uchar+udis)/2;
wherein OCV is open-circuit voltage, uchar is charging voltage, and Udis is discharging voltage;
then: the initial battery capacity of the battery is obtained through the contact pin SOC-OCV table, the battery is measured in real time to serve as the electric current, and the real-time battery capacity is obtained by using an ampere-hour integration method, wherein the formula is as follows:
finally: and establishing a mapping relation table of corresponding battery capacity values under different voltages and corresponding optical systems.
Further, in step (2), the voltage value V2 corresponding to the capacity value of the map of battery voltage and charge capacity obtained each time is subtracted from the voltage value V1 corresponding to the capacity value of the map of battery voltage and charge capacity obtained last time, thereby obtaining a voltage difference value Δ V The method comprises the following steps: delta V ＝V 2 -V 1 。
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, through adding the broom effect of the battery, the battery attenuation effect is predicted by combining the multiple Box-cox changes with the maximum likelihood estimation method and the least square fitting, so that not only are the prediction precision and convergence capacity of the residual service life of the battery improved, but also a large number of off-line aging experiments of the battery are avoided, and the practical applicability and convenience are improved.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of the overall invention;
FIG. 2 is a schematic diagram of the discharge broom effect of the judgment broom neck of the present invention;
FIG. 3 is a schematic diagram of the charged broom effect of the present invention for determining the broom neck;
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-3, the present invention provides the following technical solutions: a residual life prediction method of an energy storage battery based on Box-Cox change comprises the following steps:
s1, firstly, extracting historical capacity data of a battery from a battery historical capacity database;
s2, linearizing the battery history capacity data extracted in the step S1;
s3, solving the parameter lambda in the step S2 through a maximum likelihood estimation method;
s4, solving parameters of a battery capacity attenuation track fitting equation through a least square method;
s5, judging whether the current battery capacity is in front of a broom neck;
s6, if so, solving the residual life of the battery; if not, repeating steps S1-S5.
Further, in step S2, box-Cox linearization processing is performed on the historical capacity data of the battery using the following formula:
wherein:for the historical capacity observation after Box-Cox transformation, λ is the Box-Cox transformation coefficient, and Cmax is the historical capacity observation before Box-Cox transformation.
Further, in step S3, the parameter λ is solved using the following formula:
wherein: l (lambda) is a logarithmic natural function, n is the number of samples of the battery history capacity observation value, C is the battery history capacity observation vector, K is the cycle number vector corresponding to the battery history capacity, T is the transpose of the K vector, B is a parameter, B= (K) T k) - 1 k T C。
Further, after the Box-Cox transformation in steps S1-S3, when the battery history capacity data is linearized, the decay trajectory fitting equation of the battery capacity is:
where a is the slope of the fitted battery capacity decay trajectory fitting equation and b is the deviation of the fitted battery capacity decay trajectory fitting equation.
Further, in step S4, the parameters of the battery capacity fade trajectory fitting equation are solved using the following formula:
further, after obtaining the two parameters a and b of the fitting equation by the least square method, the cycle number n corresponding to the battery capacity failure threshold can be solved, namely, the fitting equation is as follows:
further, in step S5, the method for determining whether the current battery capacity is in front of the broom neck is as follows:
(1) Initializing charge and discharge of the battery through multiple times of small current, and establishing a mapping relation table of battery voltage V and charge and discharge capacity;
(2) The normal battery working charge and discharge under actual use is started, and the mapping relation table of the battery voltage and the charging capacity of each time is recorded;
(3) Setting an upper limit threshold lambda of a voltage difference value, and judging the voltage difference delta between the voltage V2 under the current circulation times and the voltage V1 under the last circulation times in real time V Size of the value:
if lambda > delta V Continue to look for another delta V ；
If lambda is less than or equal to delta V The battery capacity at this time, which is the "broom neck" of the battery, is recorded.
Further, in step S6, if it is determined that the current battery capacity is before the neck, the number of cycles n of the battery capacity failure threshold under the current charge and discharge environmental conditions of step S5 and the number of battery charge and discharge cycles n at that time are recorded n Remaining life n of battery s The method comprises the following steps:
n s ＝n-n n ；
if the current battery capacity is judged not to be in front of the broom neck, the BOX-COX change is used once again for the performance decay curve behind the battery broom neck, the steps of S1-S5 are repeated, and then the cycle number n of the battery capacity failure threshold under the current charge and discharge environment condition and the cycle number n of the battery charge and discharge are recorded n Remaining life n of battery n The method comprises the following steps:
n s ＝n-n n 。
further, in the step (1), the specific method for establishing the mapping relation table of the battery voltage V and the charge-discharge capacity is as follows:
first,: the battery is initialized by charging and discharging with low current, and each single battery comprises:
(1) when in charging: uchar=ocv+ir internal battery resistance;
(2) when discharging, the following steps are carried out: uidis=ocv-IR internal cell resistance;
(1) the formula in + (2): ocv= (uchar+udis)/2;
wherein OCV is open-circuit voltage, uchar is charging voltage, and Udis is discharging voltage;
then: the initial battery capacity of the battery is obtained through the contact pin SOC-OCV table, the battery is measured in real time to serve as the electric current, and the real-time battery capacity is obtained by using an ampere-hour integration method, wherein the formula is as follows:
finally: and establishing a mapping relation table of corresponding battery capacity values under different voltages and corresponding optical systems.
Further, in step (2), the voltage value V2 corresponding to the capacity value of the map of battery voltage and charge capacity obtained each time is subtracted from the voltage value V1 corresponding to the capacity value of the map of battery voltage and charge capacity obtained last time, thereby obtaining a voltage difference value Δ V The method comprises the following steps: delta V ＝V 2 -V 1 。
The specific implementation mode is as follows: when the method is used, firstly, historical capacity data of a battery are extracted, and nonlinear capacity attenuation tracks are linearized through Box-Cox transformation, wherein the formula is as follows:
wherein:for the historical capacity observation value after Box-Cox conversion, lambda is a Box-Cox conversion coefficient, and Cmax is the historical capacity observation value before Box-Cox conversion;
then solving the parameter lambda by a maximum likelihood estimation method, wherein the formula is as follows:
wherein: l (lambda) is a logarithmic natural function, n is the number of samples of the battery history capacity observation, C is the battery history capacity observation vector, K is the battery historyThe number of cycles vector corresponding to the capacity, T is the transpose of the K vector, B is the parameter, b= (K) T k) - 1 k T C；
After Box-Cox conversion, the historical capacity data of the battery is linearized, and the decay track fitting equation of the capacity of the battery is as follows:a is the slope of a fitted battery capacity decay trajectory fitting equation, and b is the deviation of the fitted battery capacity decay trajectory fitting equation;
then solving parameters of a battery capacity attenuation track fitting equation by a least square method, wherein the formula is as follows:
after obtaining two parameters a and b of a fitting equation by a least square method, the cycle number n corresponding to the battery capacity failure threshold can be solved, namely the fitting equation is as follows:n is a value of n; because of the strong nonlinearity of the power battery at the end of charge and discharge, namely a battery broom effect is generated, the battery performance attenuation curve of the battery is the same in the industry at the intersection point of the multiple charge and discharge data, and the battery performance attenuation curve is irregular nonlinearity after exceeding the battery performance attenuation curve of the battery is the same in the front of the broom; at this time, we judge whether the current battery capacity is in front of the neck, if yes, then record the cycle number n of the battery capacity failure threshold under the current charge and discharge environmental condition of step S5 and the battery charge and discharge cycle number n at this time n Remaining life n of battery s The method comprises the following steps:
n s ＝n-n n ；
if not, repeating steps S1-S5 for a performance decay curve behind the neck of the swab, using a BOX-COX change, and recording the sameThe cycle number n of the battery capacity failure threshold under the current charge and discharge environment condition and the battery charge and discharge cycle number n at the time n Remaining life n of battery n The method comprises the following steps:
n s ＝n-n n ；
the method for judging whether the current battery capacity is before or after the broom neck is as follows:
(1) The battery voltage V and the charge-discharge capacity mapping relation table is established through the initialization charge-discharge of the battery with multiple times of small currents, and the specific method is as follows:
firstly, the battery is initialized by charging and discharging with small current, and each single battery comprises:
(1) when in charging: uchar=ocv+ir internal resistance of battery
(2) When discharging, the following steps are carried out: udis=OCV-IR battery internal resistance
Because the battery internal resistance R is related to the charge and discharge currents, different currents can cause different battery internal resistances, but the method uses small currents to initialize the battery, so that the problem can be avoided;
(1) and (2) obtaining: ocv= (uchar+udis)/2;
wherein OCV is open-circuit voltage, uchar is charging voltage, and Udis is discharging voltage;
in order to obtain the open-circuit voltage OCV of the battery in the prior art, the battery is required to be kept still for a period of time (more than 8 hours), so that the chemical property of the battery is stable, and the accurate OCV is obtained, but a large amount of time is needed, and the battery is rarely allowed to be kept still for a long time in practical application, so that the OCV of the battery can be conveniently and rapidly obtained by the method;
then, obtaining initial battery capacity of the battery through a pin SOC-OCV table, measuring the acting electric current of the battery in real time, and obtaining the real-time battery capacity by using an ampere-hour integration method;
finally: establishing a mapping relation table of corresponding battery capacity value corresponding optical systems under different voltages;
(2) Starting to charge and discharge normal battery operation under actual use, and recording a mapping relation table of battery voltage and charge capacity each time; and subtracting the voltage value V2 corresponding to the capacity value of the mapping relation table of the battery voltage and the charging capacity obtained each time from the voltage value V1 corresponding to the capacity value of the mapping relation table of the battery voltage and the charging capacity obtained last time to obtain a voltage difference delta V The method comprises the following steps: delta V ＝V 2 -V 1
(3) Setting an upper limit threshold lambda of a voltage difference value, and judging the voltage difference delta between the voltage V2 under the current circulation times and the voltage V1 under the last circulation times in real time V Size of the value:
if lambda > delta V Continue to look for another delta V
If lambda is less than or equal to delta V Recording the capacity of the battery at the moment, wherein the capacity is the 'broom neck' of the battery;
according to the method, the broom effect of the battery is added, and the battery attenuation effect is predicted by combining a multiple Box-cox change with a maximum likelihood estimation method and a least square fitting method, so that not only are the prediction precision and convergence capacity of the residual service life of the battery improved, but also a large number of off-line aging experiments of the battery are avoided, and the practical applicability and convenience are improved.
The working principle of the invention is as follows:
referring to the attached drawings 1-3 of the specification, the invention predicts the attenuation effect of the battery by adding the broom effect of the battery and combining a plurality of Box-cox changes with a maximum likelihood estimation method and a least square fitting, thereby not only improving the prediction precision and convergence capacity of the residual service life of the battery, but also avoiding a large number of offline aging experiments of the battery and improving the practical applicability and convenience.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A residual life prediction method of an energy storage battery based on Box-Cox change is characterized by comprising the following steps: the method comprises the following steps:
s1, firstly, extracting historical capacity data of a battery from a battery historical capacity database;
s2, linearizing the battery history capacity data extracted in the step S1;
s3, solving the parameter lambda in the step S2 through a maximum likelihood estimation method;
s4, solving parameters of a battery capacity attenuation track fitting equation through a least square method; after the Box-Cox conversion in the steps S1-S3, when the historical capacity data of the battery is linearized, the decay track fitting equation of the capacity of the battery is as follows:
wherein:for the historical capacity observation after Box-Cox conversion, a is the battery capacity after fittingSlope of decay trajectory fitting equation, b is deviation of battery capacity decay trajectory fitting equation after fitting;
the parameters of the battery capacity fade trajectory fitting equation are solved using the following formula:
after obtaining two parameters a and b of a fitting equation by a least square method, the cycle number n corresponding to the battery capacity failure threshold can be solved, namely the fitting equation is as follows:
s5, judging whether the current battery capacity is before neck sweeping; the method for judging whether the current battery capacity is before neck sweeping comprises the following steps:
(1) Initializing charge and discharge of the battery through multiple times of small current, and establishing a mapping relation table of battery voltage V and charge and discharge capacity;
(2) The normal battery working charge and discharge under actual use is started, and the mapping relation table of the battery voltage and the charging capacity of each time is recorded;
(3) Setting an upper limit threshold lambda of a voltage difference value, and judging the voltage difference delta between the voltage V2 under the current circulation times and the voltage V1 under the last circulation times in real time V Size of the value:
if lambda > delta V Continue to look for another delta V ；
If lambda is less than or equal to delta V Recording the capacity of the battery at the moment, wherein the capacity is the neck sweeping of the battery;
s6, if so, solving the residual life of the battery; if not, repeating steps S1-S5.
2. The method for predicting the remaining life of the energy storage battery based on Box-Cox variation according to claim 1, wherein the method comprises the following steps: in step S2, box-Cox linearization processing is performed on the historical capacity data of the battery using the following formula:
wherein:and (3) historical capacity observation values after Box-Cox transformation, wherein lambda is a Box-Cox transformation coefficient.
3. The method for predicting the remaining life of the energy storage battery based on Box-Cox variation according to claim 1, wherein the method comprises the following steps: in step S3, the parameter λ is solved using the following formula:
wherein: l (lambda) is a logarithmic natural function, n is the number of samples of the battery history capacity observation value, C is the battery history capacity observation vector, K is the cycle number vector corresponding to the battery history capacity, B= (K) T k) -1 k T C。
4. The method for predicting the remaining life of the energy storage battery based on Box-Cox variation according to claim 1, wherein the method comprises the following steps: in step S6, if it is determined that the current battery capacity is before the neck-sweep, the cycle number n of the battery capacity failure threshold under the current charge-discharge environmental condition of step S5 and the battery charge-discharge cycle number n at that time are recorded n Remaining life n of battery s The method comprises the following steps:
n s ＝n-n n ；
if the current battery capacity is judged not to be before neck sweeping, the performance decay curve behind the neck sweeping of the battery is used once again for B0X-COX change, the process repeats the steps of S1-S5, and then the battery capacity loss under the current charge and discharge environment condition is recordedCycle number n of effective threshold and battery charge-discharge cycle number n at that time n Remaining life n of battery n The method comprises the following steps:
n s ＝n-n n 。
5. the method for predicting the remaining life of the energy storage battery based on Box-Cox variation according to claim 1, wherein the method comprises the following steps: in the step (1), the specific method for establishing the mapping relation table of the battery voltage V and the charge-discharge capacity is as follows:
first,: the battery is initialized by charging and discharging with low current, and each single battery comprises:
(1) when in charging: uchar=ocv+ir internal battery resistance;
(2) when discharging, the following steps are carried out: uidis=ocv-IR internal cell resistance;
(1) the formula in + (2): OCV= (Uchar+Udis)/2
Wherein OCV is open-circuit voltage, uchar is charging voltage, and Udis is discharging voltage;
then: the initial battery capacity of the battery is obtained through the contact pin SOC-OCV table, the battery is measured in real time to serve as the electric current, and the real-time battery capacity is obtained by using an ampere-hour integration method, wherein the formula is as follows:
finally: and establishing a mapping relation table of corresponding battery capacity values under different voltages and corresponding optical systems.
6. The method for predicting the remaining life of the energy storage battery based on Box-Cox variation according to claim 1, wherein the method comprises the following steps: in step (2), the voltage value V2 corresponding to the capacity value of the mapping table of the battery voltage and the charge capacity obtained each time is subtracted from the voltage value V1 corresponding to the capacity value of the mapping table of the battery voltage and the charge capacity obtained last time, thereby obtaining a voltage difference value delta V The method comprises the following steps: delta V ＝V 2 -V 1 。
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