CN113884912A - Interpolation fitting method and device for dQ/dV curve of lithium battery based on simulated annealing algorithm - Google Patents
Interpolation fitting method and device for dQ/dV curve of lithium battery based on simulated annealing algorithm Download PDFInfo
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- 238000012216 screening Methods 0.000 claims abstract description 14
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- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 1
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Abstract
The application relates to a method and a device for interpolation fitting of a dQ/dV curve of a lithium battery based on a simulated annealing algorithm, belonging to the technical field of lithium batteries and comprising the following steps: acquiring the charging electric quantity of a battery monomer to be detected in each time period; acquiring sampling data from the charging starting moment to the moment when the charging is carried out to the cut-off voltage, wherein the sampling data comprises a charging amount, voltage every preset time and SOC sampling data in a charging period; screening the sampling data to obtain filtered sampling data; performing piecewise fitting on the filtered sampling data to obtain a primary fitting curve; optimizing the preliminary fitting curve by using a simulated annealing algorithm to obtain a dQ/dV curve; more accurate dQ/dV curve can be obtained, and the health state judgment of the battery can be fully guaranteed on the basis of the dQ/dV curve with good characteristics and high precision.
Description
[ technical field ] A method for producing a semiconductor device
The application relates to a method and a device for interpolation fitting of a dQ/dV curve of a lithium battery based on a simulated annealing algorithm, and belongs to the technical field of lithium batteries.
[ background of the invention ]
The dQ/dV curve (capacity increment curve) is a curve diagram of a one-to-one correspondence relationship obtained by selecting a voltage grid point of a unit length and simultaneously carrying out differential value on the increment of the capacity in the charging and discharging processes of the battery. Because the dQ/dV curve (capacity increment curve) of the lithium ion battery is an effective tool for analyzing the state of the battery inside the battery, the state of the internal parameter of the battery can be obtained by the dQ/dV curve without disassembling the battery at all.
In the traditional drawing process of the dQ/dV curve, points are mostly taken at constant intervals, or Q is taken at constant capacity intervals, or V is taken at equal voltage intervals.
However, even under laboratory conditions, even if the acquisition accuracy reaches 0.1%, a phenomenon of data point missing or data missing occurs, resulting in an error situation in which the method cannot be used due to the data point missing.
[ summary of the invention ]
The application provides a method and a device for interpolation fitting of a dQ/dV curve of a lithium battery based on a simulated annealing algorithm, and provides a method for determining an optimal discrimination point by combining a piecewise fitting and an optimization algorithm, so that the dQ/dV curve is drawn, and the accuracy of the curve is greatly improved. The application provides the following technical scheme:
in one aspect, a method for interpolation fitting of a dQ/dV curve of a lithium battery based on a simulated annealing algorithm is provided, and the method comprises the following steps:
acquiring the charging electric quantity of a battery monomer to be detected in each time period;
acquiring sampling data from the charging starting moment to the moment when the charging is carried out to the cut-off voltage, wherein the sampling data comprises a charging amount, and voltage and SOC sampling data at intervals of a preset time length in a charging period;
screening the sampling data to obtain filtered sampling data so as to filter data points of a voltage corresponding to a plurality of capacity values;
performing piecewise fitting on the filtered sampling data to obtain a primary fitting curve;
and optimizing the preliminary fitting curve by using a simulated annealing algorithm to obtain a dQ/dV curve.
Optionally, the performing piecewise fitting on the filtered sampling data to obtain a preliminary fitting curve includes:
when N data sampling points exist currently, the next point is accepted from the first point as a current new data set, and curve fitting is carried out to obtain a fitting function V (f) (q), wherein V represents voltage, q represents charging capacity, and f represents the mapping relation between the voltage and the charging capacity; n is an integer greater than 1;
calculating an error between a fitting function value obtained through the fitting function and the original data;
comparing the error with a preset segmentation threshold;
under the condition that the comparison result meets the condition, executing the next accepting point again to serve as the current new data set, and performing curve fitting to obtain a fitting function V ═ f (q);
and under the condition that the comparison result does not meet the condition, triggering and executing the step of optimizing the preliminary fitting curve by using the simulated annealing algorithm to obtain a dQ/dV curve.
Optionally, the comparing the error with a preset segmentation threshold includes:
determining that the comparison result satisfies a condition when the error is less than the segmentation threshold;
and determining that the comparison result does not meet the condition under the condition that the error is greater than or equal to the segmentation threshold.
Optionally, the optimizing the preliminary fitted curve by using a simulated annealing algorithm to obtain a dQ/dV curve includes:
determining the optimal number of segments and fitting parameters of the preliminary fitting curve by using a simulated annealing algorithm to obtain an optimized fitting curve;
and (4) carrying out derivation on the optimized fitting curve to obtain the dQ/dV curve.
Optionally, the determining the optimal number of segments and fitting parameters of the preliminary fitted curve using a simulated annealing algorithm comprises:
setting curve parameters of the preliminary fitting curve as an initial solution, a current solution and an optimal solution;
in the temperature reduction link, randomly generating a new solution, then substituting the new solution, and calculating a new solution error value;
when the error of the new solution is smaller than the current error, accepting the new solution and setting the new solution as the current solution;
when the error of the new solution is smaller than the error generated by the optimal solution, saving the new solution as the optimal solution;
and when the error of the new solution is larger than the current error, the probability is accepted and stored as the current solution, otherwise, the new solution is discarded.
Optionally, when the new solution error is greater than the current error, the probability represented by the following equation is accepted:
p＝-exp(ΔE)/T
Δ E ═ new solution generation error — old solution generation error;
wherein p is probability, T is number of segments, and the number of corresponding segments gradually decreases as the number of cycles increases.
Optionally, the filtering the sampling data to obtain filtered sampling data, so as to filter data points of a voltage corresponding to a plurality of capacity values according to the following formula:
wherein, V represents voltage, Q represents battery capacity, n is nth data, and the value of n is an integer greater than 1.
Optionally, the step of optimizing the preliminarily fitted curve by using a simulated annealing algorithm to obtain a dQ/dV curve further includes:
and smoothing the dQ/dV curve to obtain a final dQ/dV curve.
Optionally, the smoothing the dQ/dV curve to obtain a final dQ/dV curve includes:
and smoothing the dQ/dV curve by adopting a Witten-Bell smoothing algorithm to obtain the final dQ/dV curve.
In another aspect, an interpolation fitting lithium battery dQ/dV curve device based on a simulated annealing algorithm is provided, the device includes:
the electric quantity data acquisition module is used for acquiring the charging electric quantity of the battery monomer to be detected in each time period;
the sampling data acquisition module is used for acquiring sampling data from the charging start moment to the moment when the charging is reached to the cut-off voltage, and the sampling data comprises a charging amount, voltage and SOC sampling data at intervals of a preset time length in a charging period;
the sampling data screening module is used for screening the sampling data to obtain filtered sampling data so as to filter data points of a voltage corresponding to a plurality of capacity values;
the data initial fitting module is used for performing segmented fitting on the filtered sampling data to obtain a primary fitting curve;
and the fitting curve optimization module is used for optimizing the preliminary fitting curve by using a simulated annealing algorithm to obtain a dQ/dV curve.
The beneficial effects of this application include at least: the charging electric quantity of a battery monomer to be tested in each time period is obtained; acquiring sampling data from the charging starting moment to the moment when the charging is carried out to the cut-off voltage, wherein the sampling data comprises a charging amount, voltage every preset time and SOC sampling data in a charging period; screening the sampling data to obtain filtered sampling data so as to filter data points of a voltage corresponding to a plurality of capacity values; performing piecewise fitting on the filtered sampling data to obtain a primary fitting curve; optimizing the preliminary fitting curve by using a simulated annealing algorithm to obtain a dQ/dV curve; the problems that data points are easy to lose in the existing dQ/dV curve drawing mode and the drawing mode cannot be used can be solved; more accurate dQ/dV curve can be obtained, and the health state judgment of the battery can be fully guaranteed on the basis of the dQ/dV curve with good characteristics and high precision.
The foregoing description is only an overview of the technical solutions of the present application, and in order to make the technical solutions of the present application more clear and clear, and to implement the technical solutions according to the content of the description, the following detailed description is made with reference to the preferred embodiments of the present application and the accompanying drawings.
[ description of the drawings ]
FIG. 1 is a flow chart of a method for interpolating a fitted dQ/dV curve for a lithium battery based on a simulated annealing algorithm according to an embodiment of the present application;
FIG. 2 is a schematic diagram of the relationship between capacity and voltage provided by one embodiment of the present application;
FIG. 3 is a table of segment thresholds, segment numbers, and accumulated error squares as provided by an embodiment of the present application;
FIG. 4 is a first-order derived capacity delta curve provided by an embodiment of the present application;
FIG. 5 is a smoothed capacity delta curve provided by one embodiment of the present application;
FIG. 6 is a flow chart of a method for interpolating a fitted lithium battery dQ/dV curve based on a simulated annealing algorithm according to another embodiment of the present application;
FIG. 7 is a block diagram of an interpolation fitting lithium battery dQ/dV curve device based on a simulated annealing algorithm according to another embodiment of the present application.
[ detailed description ] embodiments
The following detailed description of embodiments of the present application will be made with reference to the accompanying drawings and examples. The following examples are intended to illustrate the present application but are not intended to limit the scope of the present application.
First, several terms referred to in the present application will be described.
And (3) simulating an annealing algorithm: the simulated annealing algorithm is based on the solid annealing principle, is an algorithm based on probability, heats the solid to be sufficiently high, then slowly cools the solid, when heating, the particles in the solid become disordered along with the temperature rise, the internal energy is increased, when slowly cooling, the particles gradually get ordered, each temperature reaches an equilibrium state, and finally, the internal energy is reduced to the minimum when reaching a ground state at normal temperature. The simulated annealing algorithm starts from a certain high initial temperature, and randomly searches a global optimal solution of the objective function in a solution space by combining with the probability jump characteristic along with the continuous decrease of the temperature parameter, namely, the global optimal solution can jump out probabilistically in a local optimal solution and finally tends to be global optimal. The simulated annealing algorithm is a general optimization algorithm, theoretically, the algorithm has probability global optimization performance, and is widely applied to engineering at present, such as the fields of VLSI, production scheduling, control engineering, machine learning, neural networks, signal processing and the like.
Witten-Bell smoothing algorithm: the idea of the algorithm is that if an instance in the test process does not appear in the corpus, it is a new thing, i.e. it appears for the first time, and the probability of the non-appearing instance can be replaced by the probability of seeing the new instance in the corpus.
Fig. 1 is a flowchart of a method for interpolating and fitting a dQ/dV curve of a lithium battery based on a simulated annealing algorithm according to an embodiment of the present application, where the method at least includes the following steps:
Specifically, the charging time point and the charging current value of the battery monomer to be tested are determined, and the charging electric quantity in each time period is calculated according to an ampere-hour integration method.
The preset time period may be 1 second or longer or shorter, and the value of the preset time period is not limited in this embodiment.
And 103, screening the sampling data to obtain filtered sampling data so as to filter data points of a voltage corresponding to a plurality of capacity values.
Specifically, the collected data is simply processed to exclude data points where one voltage corresponds to multiple capacity values. And then, further data screening can be performed, namely when a plurality of voltages correspond to one capacity value, the average value of the capacities is adopted under the same voltage. Meanwhile, for the case of noisy original data, simple smoothing operation is required to obtain data points which preliminarily meet the requirements.
Specifically, the sampling data is filtered to obtain filtered sampling data, and the filtering is performed according to the following formula for filtering data points of a voltage corresponding to a plurality of capacity values:
wherein, V represents voltage, Q represents battery capacity, n is nth data, and the value of n is an integer greater than 1.
The meaning of the above formula is that the battery capacity is steadily increased during the charging process of the battery. The voltage of the battery is also steadily increased, the dQ/dV curve is made by simply researching the physicochemical process between the inside and the outside indicated by the battery at different increasing rates and the meaning represented by the process in the process of increasing the voltage V and the capacity Q, and the curve corresponding to the filtered sampling data is shown in fig. 2.
And 104, performing piecewise fitting on the filtered sampling data to obtain a primary fitting curve.
And performing piecewise fitting on the filtered sampling data to obtain a preliminary fitting curve, wherein the preliminary fitting curve comprises the following steps: when N data sampling points exist currently, the next point is accepted from the first point as a current new data set, and curve fitting is carried out to obtain a fitting function V (f) (q), wherein V represents voltage, q represents charging capacity, and f represents the mapping relation between the voltage and the charging capacity; n is an integer greater than 1; calculating an error between a fitting function value obtained through the fitting function and the original data; comparing the error with a preset segmentation threshold; if the comparison result meets the condition, accepting the next point as the current new data set again, and performing curve fitting to obtain a fitting function V ═ f (q); and under the condition that the comparison result does not meet the condition, triggering and executing the step of optimizing the preliminary fitting curve by using the simulated annealing algorithm to obtain a dQ/dV curve.
Wherein comparing the error to a preset segment threshold comprises: when the error is smaller than the segmentation threshold, the data set at the moment is proved to be better, and the next point can be accepted to form a new data set, namely, the comparison result is determined to meet the condition; and determining that the comparison result does not meet the condition under the condition that the error is greater than or equal to the segmentation threshold.
And 105, optimizing the preliminary fitting curve by using a simulated annealing algorithm to obtain a dQ/dV curve.
The threshold value is set manually and can be adjusted, the newly obtained data set is calculated for multiple times by adopting a simulated annealing algorithm to obtain an optimal solution, and then the obtained curve is subjected to derivation to obtain a dQ/dV curve.
Specifically, the preliminary fitting curve is optimized by using a simulated annealing algorithm to obtain a dQ/dV curve, which comprises the following steps: determining the optimal number of segments and fitting parameters of the preliminary fitting curve by using a simulated annealing algorithm to obtain an optimized fitting curve; and (5) carrying out derivation on the optimized fitting curve to obtain a dQ/dV curve.
In the fitting stage, the optimal number of segments and fitting parameters of the preliminarily fitted curve are determined by using a simulated annealing algorithm, and the method comprises the following steps: setting curve parameters of the preliminary fitting curve as an initial solution, a current solution and an optimal solution; in the temperature reduction link, randomly generating a new solution, then substituting the new solution, and calculating a new solution error value; when the error of the new solution is smaller than the current error, accepting the new solution and setting the new solution as the current solution; when the error of the new solution is smaller than the error generated by the optimal solution, saving the new solution as the optimal solution; and when the error of the new solution is larger than the current error, the probability is accepted and stored as the current solution, otherwise, the new solution is discarded.
When the new solution error is greater than the current error, the probability represented by the following equation is accepted:
p＝-exp(ΔE)/T
Δ E ═ new solution generation error — old solution generation error;
wherein p is probability, T is number of segments, and the number of corresponding segments gradually decreases as the number of cycles increases.
For example, the set segmentation process is an initial state that the number of segments of the simulated annealing algorithm after heating is 300, and according to the idea of the simulated annealing algorithm, the temperature slowly decreases from the moment of high temperature, and the local advantage can be skipped by combining the probability density formula, so that the global optimal concept is achieved. I.e., the number of stages is set from the highest 270 to the number of stages of 1 (i.e., the stage is the ending temperature). The iteration starts according to the following rule:
the preset value is as follows: an initial temperature (270); an end temperature (1); an initial solution; the annealing speed; the following process continues when the number of anneals (not reaching the end temperature):
generating a new solution from the initial solution
Error of new solution generation-error of old solution generation
Entering an annealing process
If Δ E <0
Accept, update
Otherwise
Accept with probability p ═ exp (Δ E)/T
End up
According to the process, in the fitting stage, the curve parameters obtained by fitting are set as an initial solution, a current solution and an optimal solution. In the temperature reduction link, a for loop is adopted to set a 200-time loop, a new solution is randomly generated and then substituted, the error value of the new solution is calculated, when the error of the new solution is smaller than the current error, the new solution is accepted, the new solution is set as the current solution, when the error of the new solution is smaller than the error of the optimal solution, the new solution is stored as the optimal solution, when the error of the new solution is larger than the current error, the probability is accepted, the current solution is stored, and otherwise, the new solution is discarded. With particular reference to the correspondence between different segmentation thresholds, number of segments and square curve accumulated error shown in fig. 3.
Optionally, the method respectively undergoes data preliminary screening, data segmentation, curve fitting, parameter adjustment by adopting a simulated annealing algorithm to obtain a function curve, and derivation is performed on the curve to obtain a target function. The resulting dQ/dV curve is generally a square curve, as shown with reference to fig. 4. Based on this, after this step, namely after optimizing the preliminarily fitted curve by using the simulated annealing algorithm to obtain the dQ/dV curve, the method further includes: and smoothing the dQ/dV curve to obtain a final dQ/dV curve. Refer to the smoothed curve shown in FIG. 5
Schematically, smoothing the dQ/dV curve to obtain a final dQ/dV curve, includes: and smoothing the dQ/dV curve by adopting a Witten-Bell smoothing algorithm to obtain a final dQ/dV curve.
In order to more clearly understand the interpolation fitting lithium battery dQ/dV curve method based on the simulated annealing algorithm provided by the application, the method is exemplified below. Referring to fig. 6, the method includes the following steps: carrying out primary cleaning on original data, carrying out curve segmentation fitting on the cleaned data, and determining whether the error after fitting is smaller than a set threshold A; if so, continuing to search the next point for curve fitting, and determining whether the last point is traversed; if the last point is traversed or whether the error after fitting is larger than or equal to a set threshold A, performing multiple fitting by using a simulated annealing algorithm; if the last point is not traversed, the step of searching the next point for curve fitting is executed again. After the simulated annealing algorithm is used for fitting the curve for multiple times, derivation and smoothing are carried out on the curve, and a final dQ/dV curve is obtained.
In summary, the interpolation fitting lithium battery dQ/dV curve method based on the simulated annealing algorithm provided by this embodiment obtains the charging capacity of the battery cell to be tested in each time period; acquiring sampling data from the charging starting moment to the moment when the charging is carried out to the cut-off voltage, wherein the sampling data comprises a charging amount, voltage every preset time and SOC sampling data in a charging period; screening the sampling data to obtain filtered sampling data so as to filter data points of a voltage corresponding to a plurality of capacity values; performing piecewise fitting on the filtered sampling data to obtain a primary fitting curve; optimizing the preliminary fitting curve by using a simulated annealing algorithm to obtain a dQ/dV curve; the problems that data points are easy to lose in the existing dQ/dV curve drawing mode and the drawing mode cannot be used can be solved; more accurate dQ/dV curve can be obtained, and the health state judgment of the battery can be fully guaranteed on the basis of the dQ/dV curve with good characteristics and high precision.
FIG. 7 is a block diagram of a device for interpolating a fitted lithium battery dQ/dV curve based on a simulated annealing algorithm according to an embodiment of the present application. The device at least comprises the following modules: the system comprises an electric quantity data acquisition module 710, a sampling data acquisition module 720, a sampling data screening module 730, a data initial fitting module 740 and a fitting curve optimization module 750.
The electric quantity data acquisition module 710 is used for acquiring the charging electric quantity of the battery monomer to be tested in each time period;
a sampling data obtaining module 720, configured to obtain sampling data from a charging start time to a time when the charging reaches a cut-off voltage, where the sampling data includes a charging amount, and voltage and SOC sampling data at preset time intervals during the charging period;
the sampling data screening module 730 is configured to screen the sampling data to obtain filtered sampling data, so as to filter data points of a voltage corresponding to multiple capacity values;
the data initial fitting module 740 is configured to perform piecewise fitting on the filtered sampling data to obtain a preliminary fitting curve;
and a fitting curve optimization module 750, configured to optimize the preliminary fitting curve by using a simulated annealing algorithm to obtain a dQ/dV curve.
For relevant details reference is made to the above-described method embodiments.
It should be noted that: the interpolation fitting lithium battery dQ/dV curve device based on the simulated annealing algorithm provided in the above embodiment is illustrated by only dividing the functional modules when performing the interpolation fitting lithium battery dQ/dV curve based on the simulated annealing algorithm, and in practical applications, the function allocation may be completed by different functional modules according to needs, that is, the internal structure of the interpolation fitting lithium battery dQ/dV curve device based on the simulated annealing algorithm is divided into different functional modules to complete all or part of the functions described above. In addition, the interpolation fitting lithium battery dQ/dV curve device based on the simulated annealing algorithm and the interpolation fitting lithium battery dQ/dV curve method based on the simulated annealing algorithm provided in the embodiments belong to the same concept, and specific implementation processes thereof are described in the method embodiments in detail and are not described herein again.
Optionally, the present application further provides a computer readable storage medium, in which a program is stored, and the program is loaded and executed by a processor to implement the interpolation fitting lithium battery dQ/dV curve method based on the simulated annealing algorithm according to the above method embodiments.
Optionally, the present application further provides a computer product, which includes a computer readable storage medium, in which a program is stored, and the program is loaded and executed by a processor to implement the interpolation fitting lithium battery dQ/dV curve method based on the simulated annealing algorithm according to the above method embodiment.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for interpolation fitting of a dQ/dV curve of a lithium battery based on a simulated annealing algorithm is characterized by comprising the following steps:
acquiring the charging electric quantity of a battery monomer to be detected in each time period;
acquiring sampling data from the charging starting moment to the moment when the charging is carried out to the cut-off voltage, wherein the sampling data comprises a charging amount, and voltage and SOC sampling data at intervals of a preset time length in a charging period;
screening the sampling data to obtain filtered sampling data so as to filter data points of a voltage corresponding to a plurality of capacity values;
performing piecewise fitting on the filtered sampling data to obtain a primary fitting curve;
and optimizing the preliminary fitting curve by using a simulated annealing algorithm to obtain a dQ/dV curve.
2. The method of claim 1, wherein the piecewise fitting the filtered sample data to obtain a preliminary fitting curve comprises:
when N data sampling points exist currently, the next point is accepted from the first point as a current new data set, and curve fitting is carried out to obtain a fitting function V (f) (q), wherein V represents voltage, q represents charging capacity, and f represents the mapping relation between the voltage and the charging capacity; n is an integer greater than 1;
calculating an error between a fitting function value obtained through the fitting function and the original data;
comparing the error with a preset segmentation threshold;
under the condition that the comparison result meets the condition, executing the next accepting point again to serve as the current new data set, and performing curve fitting to obtain a fitting function V ═ f (q);
and under the condition that the comparison result does not meet the condition, triggering and executing the step of optimizing the preliminary fitting curve by using the simulated annealing algorithm to obtain a dQ/dV curve.
3. The method of claim 2, wherein comparing the error to a preset fragmentation threshold comprises:
determining that the comparison result satisfies a condition when the error is less than the segmentation threshold;
and determining that the comparison result does not meet the condition under the condition that the error is greater than or equal to the segmentation threshold.
4. The method of claim 1, wherein said optimizing said preliminary fit curve using a simulated annealing algorithm to obtain a dQ/dV curve comprises:
determining the optimal number of segments and fitting parameters of the preliminary fitting curve by using a simulated annealing algorithm to obtain an optimized fitting curve;
and (4) carrying out derivation on the optimized fitting curve to obtain the dQ/dV curve.
5. The method of claim 4, wherein the determining the optimal number of segments and fitting parameters for the preliminary fit curve using a simulated annealing algorithm comprises:
setting curve parameters of the preliminary fitting curve as an initial solution, a current solution and an optimal solution;
in the temperature reduction link, randomly generating a new solution, then substituting the new solution, and calculating a new solution error value;
when the error of the new solution is smaller than the current error, accepting the new solution and setting the new solution as the current solution;
when the error of the new solution is smaller than the error generated by the optimal solution, saving the new solution as the optimal solution;
and when the error of the new solution is larger than the current error, the probability is accepted and stored as the current solution, otherwise, the new solution is discarded.
6. The method of claim 5, wherein when the new solution error is greater than the current error, the probability represented by the following equation is accepted:
p＝-exp(ΔE)/T
Δ E ═ new solution generation error — old solution generation error;
wherein p is probability, T is number of segments, and the number of corresponding segments gradually decreases as the number of cycles increases.
7. The method of any one of claims 1 to 6, wherein the filtering of the sampled data to obtain filtered sampled data to filter data points for a plurality of capacity values for a voltage is performed according to the following equation:
wherein, V represents voltage, Q represents battery capacity, n is nth data, and the value of n is an integer greater than 1.
8. The method of any of claims 1 to 6, wherein the dQ/dV curve is a square curve, and wherein the optimizing the preliminary fit curve using the simulated annealing algorithm further comprises:
and smoothing the dQ/dV curve to obtain a final dQ/dV curve.
9. The method of claim 8, wherein smoothing the dQ/dV curve to obtain a final dQ/dV curve comprises:
and smoothing the dQ/dV curve by adopting a Witten-Bell smoothing algorithm to obtain the final dQ/dV curve.
10. An interpolation fitting lithium battery dQ/dV curve device based on a simulated annealing algorithm is characterized by comprising:
the electric quantity data acquisition module is used for acquiring the charging electric quantity of the battery monomer to be detected in each time period;
the sampling data acquisition module is used for acquiring sampling data from the charging start moment to the moment when the charging is reached to the cut-off voltage, and the sampling data comprises a charging amount, voltage and SOC sampling data at intervals of a preset time length in a charging period;
the sampling data screening module is used for screening the sampling data to obtain filtered sampling data so as to filter data points of a voltage corresponding to a plurality of capacity values;
the data initial fitting module is used for performing segmented fitting on the filtered sampling data to obtain a primary fitting curve;
and the fitting curve optimization module is used for optimizing the preliminary fitting curve by using a simulated annealing algorithm to obtain a dQ/dV curve.
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