CN109596986B - Power battery pack internal resistance online estimation method and battery management system - Google Patents
Power battery pack internal resistance online estimation method and battery management system Download PDFInfo
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Abstract
The invention relates to the technical field of automobile power batteries, provides an online estimation method for internal resistance of a power battery pack and a battery management system, and solves the problem that the error of estimating the internal resistance of the power battery pack is large in the prior art. The online estimation method for the internal resistance of the power battery pack comprises the following steps: acquiring the internal resistance, temperature, voltage and charge state of each battery cell in the power battery pack, and judging whether the currently acquired internal resistances of all the battery cells meet the estimation condition; if the estimation conditions are met, judging whether the temperature, the voltage and the charge state of all the currently acquired battery cells meet the preset global distribution; if the preset global distribution is met, performing normality test and variance homogeneity test on the acquired internal resistance of each battery cell; and when the acquired internal resistance of each battery cell passes through the normality test and the variance homogeneity test, determining the internal resistance of the power battery pack according to the internal resistance of each battery cell. The embodiment of the invention is suitable for the process of estimating the internal resistance of the power battery pack on line.
Description
Technical Field
The invention relates to the technical field of automobile power batteries, in particular to an online estimation method for internal resistance of a power battery pack and a battery management system.
Background
The State of health (SOH) of a power battery refers to the ratio of the discharged capacity of the battery to the nominal capacity of the battery under a certain condition, reflects the overall performance of the battery and the current discharge capacity, and is mainly used for expressing the State of health of the power battery. The health condition of each battery cell in the power battery pack is known in real time, the service life of the battery cells can be prolonged, and the overall charging and discharging performance of the power battery pack is guaranteed.
The important criterion of the health state of the cell can be calibrated by the internal resistance of one of the battery performance parameters. Internal resistance exists in various types of cells, so that a part of electric energy is consumed by the internal resistance when the cells work, and the consumed electric energy is in direct proportion to the internal resistance of the cells. For the lithium ion battery, after a plurality of charging and discharging operations, the internal resistance of the lithium ion battery is gradually increased due to chemical changes, which causes the reduction of the available energy of the battery core. Under most conditions, the electric core with the same parameters has small internal resistance but strong discharge capacity. Therefore, estimating the internal resistance of the power battery pack becomes one of the key technologies for estimating the state of health of the power battery management system of the electric vehicle.
In the prior art of estimating the internal resistance of a power battery pack, there are a maximum method and an average method. The maximum method is to obtain the maximum estimated internal resistance as the estimated internal resistance of the battery pack by comparing the internal resistances of all the battery cells in the battery pack. But there are results that affect the battery pack due to some particular error point, causing a false estimate. As shown in table 1, the internal resistances of the first 7 cells are all 1, while the internal resistance of the 8 th cell is 5, if the internal resistance of the battery pack is estimated by the maximum method, the internal resistance of the battery pack will adopt the maximum internal resistance value as the internal resistance of the battery pack, that is, the internal resistance of the battery pack is 5, and the aging of the battery pack will be overestimated.
TABLE 1
| Cell number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| Internal resistance of | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 |
In addition, the average value method is a method of calculating the average internal resistance of all the battery cells in the battery pack as the internal resistance of the battery pack, and the method also has a large error under certain conditions. As shown in table 2, when the internal resistance of the 8 th cell is 1.1 and the internal resistance of the 7 th cell is 0.9, the internal resistance of the 7 th cell is averaged to be 1 when averaging, and the aging of the battery pack may be underestimated.
TABLE 2
| Cell number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| Internal resistance of | 1 | 1 | 1 | 1 | 1 | 1 | 0.9 | 1.1 |
Disclosure of Invention
In view of the above, the present invention is directed to an online estimation method for internal resistance of a power battery pack and a battery management system, so as to at least partially solve the above technical problems.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
an online estimation method for internal resistance of a power battery pack comprises the following steps: acquiring internal resistance and index data of each battery cell in the power battery pack, and judging whether the currently acquired internal resistance of all the battery cells meets an estimation condition, wherein the index data comprises temperature, voltage and charge state; if the internal resistances of all the currently acquired battery cells meet the estimation condition, judging whether the index data of all the currently acquired battery cells meet preset global distribution; if the index data of all the electric cores which are obtained currently meet the preset global distribution, performing normality test and variance homogeneity test on the obtained internal resistance of each electric core; and when the acquired internal resistance of each battery cell passes through the normality test and the variance homogeneity test, determining the internal resistance of the power battery pack according to the internal resistance of each battery cell.
Further, the estimation condition is that the internal resistance of each battery cell in the power battery pack is compared with the corresponding initial internal resistance value, and the number of the changed internal resistances of all the battery cells is determined to exceed the preset number.
Further, after the determining whether the currently obtained internal resistances of all the battery cells meet the estimation condition, the online estimation method for the internal resistance of the power battery pack further includes: and if the currently acquired internal resistances of all the battery cores do not meet the estimation condition, continuously acquiring the internal resistance and index data of each battery core in the power battery pack.
Further, the determining whether the currently acquired index data of all the electric cores meet the preset global distribution includes: establishing a three-dimensional list of the index data according to a preset numerical range, wherein the three-dimensional list is divided into the same areas with preset number, and the number of tables in each area is the same; determining the number of the distributed index data of all the currently acquired battery cores in each area in the three-dimensional list; determining a quality probability value corresponding to each region through a probability quality function according to the number of the distribution of each region; comparing the quality probability values corresponding to all the regions with a preset probability value; when the quality probability values corresponding to all the regions are greater than or equal to the preset probability value, determining that the currently acquired index data of all the battery cores meet the preset global distribution; and when at least one quality probability value smaller than the preset probability value exists in the quality probability values corresponding to all the regions, determining that the currently acquired index data of all the electric cores do not meet the preset global distribution.
Further, after the determining whether the currently acquired index data of all the battery cells meet the preset global distribution, the online estimation method for the internal resistance of the power battery pack further includes: and if the index data of all the electric cores acquired currently do not meet the preset global distribution, continuously acquiring the internal resistance and the index data of each electric core in the power battery pack.
Further, the performing a normality test and a variance homogeneity test on the obtained internal resistance of each battery cell includes: determining the currently acquired internal resistance of each of all the battery cells meeting the preset global distribution and the internal resistance of each battery cell acquired before the preset global distribution are met as a data set of the internal resistance of each battery cell; carrying out normality test on the data set of the internal resistance of each battery cell; and after the data set of the internal resistance of each battery cell passes the normality test, carrying out the variance homogeneity test on the data set of the internal resistance of each battery cell.
Further, after the performing the normality test and the homogeneity of variance test on the obtained internal resistance of each battery cell, the online estimation method for the internal resistance of the power battery pack further includes: carrying out normalization transformation on a data set of the internal resistance of the battery cell which does not pass the normality test; and carrying out variance correction on the data set of the internal resistance of the battery cell which does not pass the variance and homogeneity test.
Further, the determining the internal resistance of the power battery pack according to the internal resistance of each battery cell includes: according to
Obtaining an internal resistance estimated value of the ith battery cell
Wherein, yjIs the jth internal resistance value in the data set of the internal resistance of the ith battery cell, k is the total number of the internal resistance values in the data set of the internal resistance of the ith battery cell, fjThe weight is corresponding to the jth internal resistance value in the data set of the internal resistance of the ith battery cell, and the ratio of the number of the internal resistances in the data set of the internal resistance of the ith battery cell to the number of the internal resistances in the data set of the internal resistance of the ith battery cell is the same as the jth internal resistance value; according to
Obtaining the internal resistance X of the power battery pack, wherein n is the total number of the battery cores in the power battery pack,
an internal resistance evaluation value q of the ith electric core in the power battery packiAnd the weight corresponding to the internal resistance estimated value of the ith battery cell is used, and the ratio of the number of the internal resistance estimated values in the power battery pack, which is the same as the internal resistance estimated value of the ith battery cell, to n is used.
Further, after the internal resistance of the power battery pack is determined according to the internal resistance of each battery cell, the online estimation method for the internal resistance of the power battery pack further includes: and determining the internal resistance estimated value of each battery cell as a corresponding internal resistance initial value.
Compared with the prior art, the online estimation method for the internal resistance of the power battery pack has the following advantages:
(1) the online estimation method for the internal resistance of the power battery pack realizes online estimation of the internal resistance of the power battery pack, when the internal resistance and the index data of the battery cells in the battery pack are obtained, whether the index data meet the preset global distribution is judged when the internal resistances of all the battery cells meet the estimation conditions, the internal resistance of the battery pack is estimated when the index data meet the preset global distribution, and otherwise, the internal resistance and the index data of the battery cells are continuously obtained. The condition for estimating the internal resistance of the battery pack is limited to a certain extent, so that the estimation of the internal resistance of the battery cell once acquired is avoided, and the calculation resources and the power consumption are saved.
(2) The online estimation method for the internal resistance of the power battery pack realizes the normality inspection and the variance homogeneity inspection of the acquired internal resistance of each battery cell, and the internal resistance of the power battery pack is determined according to the internal resistance of each battery cell only when the acquired internal resistance of each battery cell passes the normality inspection and the variance homogeneity inspection, so that the internal resistance estimation precision of the power battery pack is improved, and the data reliability is increased.
Another object of the present invention is to provide a battery management system, which is used for executing the online estimation method of internal resistance of power battery pack as described above.
Compared with the prior art, the advantages of the battery management system and the online estimation method for the internal resistance of the power battery pack are the same, and are not repeated herein.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic flow chart of a method for online estimating internal resistance of a power battery pack according to an embodiment of the present invention;
FIG. 2 is a three-dimensional tabular illustration provided by an embodiment of the present invention;
FIG. 3 is a diagram of region partitioning for a three-dimensional list provided by an embodiment of the present invention;
fig. 4 is a diagram of distribution of currently acquired index data of all battery cells in each area in the three-dimensional list according to the embodiment of the present invention;
fig. 5 is a schematic flow chart of another online estimation method for internal resistance of a power battery pack according to an embodiment of the present invention.
Detailed Description
In addition, the embodiments of the present invention and the features of the embodiments may be combined with each other without conflict.
The present invention will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a schematic flow chart of a method for online estimating internal resistance of a power battery pack according to an embodiment of the present invention. As shown in fig. 1, the online estimation method for internal resistance of a power battery pack comprises the following steps:
103, if the index data of all the currently acquired battery cells meet the preset global distribution, performing normality test and variance homogeneity test on the acquired internal resistance of each battery cell;
and step 104, when the acquired internal resistance of each battery cell passes through the normality test and the variance homogeneity test, determining the internal resistance of the power battery pack according to the internal resistance of each battery cell.
The frequency of obtaining the internal resistance and the index data of each electric core in the power battery pack may be obtained in real time, or obtained at fixed intervals, and may be set according to specific requirements of a user, which is not limited in the embodiment of the present invention.
After the internal resistance of each electric core in the power battery pack is obtained every time, whether the currently obtained internal resistances of all the electric cores meet the estimation condition is judged. And the estimation condition is that the internal resistance of each battery cell in the power battery pack is compared with the corresponding initial internal resistance value, and the number of the changed internal resistances of all the battery cells is determined to exceed the preset number. For example, the number of the battery cells in the power battery pack is 24, after the internal resistance of each battery cell is obtained each time, the internal resistance is compared with the initial internal resistance value of each battery cell, and the number of the battery cells with changed internal resistance is counted, when the number of the battery cells with changed internal resistance is 14 and the preset number is 12, the number of the battery cells with changed internal resistance exceeds the preset number, that is, the currently obtained internal resistances of all the battery cells meet the estimation condition, and the step S102 is continuously executed. And when the number of the battery cells with the changed internal resistance is 10 and does not exceed the preset number (12), continuously acquiring the internal resistance and the index data of each battery cell in the power battery pack until the currently acquired internal resistances of all the battery cells meet the estimation condition, and then executing step S102. The preset number may be determined according to the total number of the battery cells included in the power battery pack, for example, the preset number may be 50% or 40% of the total number of the battery cells or other data, and may be set according to the requirement of the user for the power battery pack.
In step 102, if the currently acquired internal resistances of all the battery cells meet the estimation condition, it is determined whether the currently acquired index data of all the battery cells meet a preset global distribution. First, a three-dimensional list is established of index data, namely, temperature, voltage and state of charge according to preset value ranges, as shown in fig. 2, an X axis, a Y axis and a Z axis respectively represent the temperature, the voltage and the state of charge, and the corresponding relationship among the three is illustrated by the preset value ranges. The values shown in fig. 2 are only examples, and are not used to limit the embodiment of the present invention, and the value ranges of the temperatures of the battery cells in the power battery pack in the pure electric vehicle and the battery cells in the power battery pack in the hybrid vehicle may be different, which should be noted when setting the preset value ranges. Then, the three-dimensional list is divided into a preset number of same regions, and the number of tables included in each region is the same, as shown in fig. 3, if the number of tables included in each region is 1, the three-dimensional list can be divided into 125 regions. Or the divided regions may be set according to all the table books, for example, when there are 27 tables in total, 3 regions may be set, and the number of tables included in each region is 9. However, the regions are divided, and the number of tables in each region is ensured to be the same and the shapes of the tables are consistent. Then, values corresponding to the currently acquired temperature, voltage and state of charge of all the battery cells are respectively placed in corresponding tables, and the number of the currently acquired index data of all the battery cells distributed in each area in the three-dimensional list is determined, as shown in fig. 4, the index data of all the battery cells are respectively located in the preset value range of the corresponding index data. And after counting the number of the distribution of each region, determining the quality probability value corresponding to each region through a probability quality function.
The quality probability values of all the regions can be obtained according to the following probability quality functions:
fX(x)＝Pr(X＝x)＝P({s∈S:X(s)＝x})
wherein f isXFor the probability mass function, S is the number of all regions of the index data of all the battery cells in the three-dimensional list, S is the number of each region, and X is the representation region.
When the number of the common battery cells is 10, 3 regions are divided in the three-dimensional list, and the distribution condition of the index data of all the battery cells in the 3 regions is that the index data of 4 battery cells exist in the first region, the index data of 3 battery cells exist in the second region, and the index data of 3 battery cells exist in the third region. And obtaining the quality probability values of 4/10, 3/10 and 3/10 of each region according to the formula.
And obtaining the preset probability value R according to the R1/m-d 1/m, wherein m is the number of the regions, and d is a set coefficient. In the embodiment of the present invention, if m is 3 and d is set to 0.15, the preset probability value is 0.85 × 1/3.
Comparing the quality probability values corresponding to the three regions with the preset probability value, if the quality probability values are greater than or equal to the preset probability value, indicating that the quality distribution is good, determining that the index data of all the electric cores acquired currently meet the preset global distribution, and continuously performing normality test and variance homogeneity test on the acquired internal resistance of each electric core. If at least one quality probability value is smaller than the preset probability value, the distribution is not good, the currently acquired index data of all the electric cores are determined not to meet the preset global distribution, and then the step 101 is returned to continue to acquire the internal resistance and the index data of each electric core in the power battery pack.
And when the acquired internal resistance of each battery cell is subjected to the normality test and the variance homogeneity test, the normality test and the variance homogeneity test are carried out on the data set of the internal resistance of each battery cell. The data set of the internal resistance of each battery cell is the internal resistance of each battery cell in all the battery cells which meet the preset global distribution and are currently acquired, and the internal resistance of each battery cell which is acquired before the preset global distribution is met.
And performing normality test on the data set of the internal resistance of each battery cell. The normality test is a test for judging whether the population is subjected to normal distribution by using observation data, and is a special goodness-of-fit hypothesis test which is important in statistical judgment. The normal state test method generally used includes a normal probability paper method, a Charcot-Wilktest (Shapiro-Wilktest), a Kolmogorov test method, a skewness-kurtosis test method, and the like.
In the embodiment of the present invention, the normality test is described by taking skewness-kurtosis test as an example:
(1) calculating kurtosis and skewness
Respectively obtaining the skewness g of the data set of the internal resistance of the ith battery cell according to the following formulai1And kurtosis gi2：
fX(x)＝Pr(X＝x)＝P({s∈S:X(s)＝x})
Wherein, yjThe data representing the internal resistance of the ith cell is concentrated into the jth internal resistance value,
and k is the total number of the data set internal resistance values of the internal resistance of the ith battery cell.
(2) Kurtosis and skewness conversion
The skewness g is obtained by the following formulai1And kurtosis gi2Respectively corresponding conversion:
μi1(gi1)＝0
according to
Obtaining the skewness gi1Is Z isi1Wherein, in the step (A),
α2＝2/(W2-1)。
according to
The kurtosis g is obtainedi2Is Z isi2Wherein, in the step (A),
(3)K2computing
Degree of deviation gi1Is Z isi1And kurtosis gi2Is Z isi2Substituting into the following formula to obtain K2The value of (c):
substituting the data set of the internal resistance of the ith battery cell into the formula to obtain K corresponding to the data set of the internal resistance of the ith battery cell2And compared to their corresponding preset desired values, e.g., different values of k correspond to different preset desired values, as shown in table 3.
TABLE 3
| k value | Preset desired |
| 20 | 1.971 |
| 50 | 2.017 |
| 100 | 2.026 |
| 250 | 2.012 |
| 500 | 2.009 |
| 1000 | 2.000 |
Table 3 shows, as an example only, a preset expected value corresponding to a part of K values, when K corresponds to the data set of the internal resistance of the ith cell2When the difference value between the value of (a) and the corresponding preset expected value is within a preset range, the data set of the internal resistance of the ith battery cell passes the normality test, and then the data set of the internal resistance of the ith battery cell is subjected to the variance homogeneity test. On the contrary, if the difference is not within the preset range, the data set of the internal resistance of the ith battery cell does not pass the normality test, and the data set of the internal resistance of the ith battery cell needs to be subjected to normalization transformation.
The normalization transform used in the embodiments of the present invention is a standard normalization using a formula
Normalizing all internal resistance values in the data set of the internal resistances of the battery cells which do not pass the normality test, wherein yjFor failing to pass the normality testThe data of the internal resistance of the tested battery cell is concentrated into the jth internal resistance value,
σ is the variance of the data set, which is the mean of the internal resistance values in the data set.
And then, carrying out variance and homogeneity inspection on the data set of the internal resistance of the battery cell passing through the normality inspection and the data set of the internal resistance of the battery cell after normalization transformation. The homogeneity test of variance is a method for checking whether the overall variances of different samples are the same in mathematical statistics. The rationale is to make some assumption about the characteristics of the population and then to infer whether this assumption should be rejected or accepted by statistical reasoning from sampling studies. Common homogeneity tests for variance are: hartley test, Bartlett test, modified Bartlett test. The data set of the internal resistance of each battery cell may be checked with reference to the variance homogeneity check in the prior art, and the part does not belong to the key point described in the embodiment of the present invention, and therefore, details are not described in the embodiment of the present invention. And performing variance correction on the data set of the internal resistance of the battery cell which does not pass the variance and homogeneity test. For example, the formula in the prior art can be utilized
The variance is corrected. Since the variance correction can be implemented with reference to the prior art, and this part is not the focus of the description of the embodiment of the present invention, it is not described in detail in the embodiment of the present invention.
In step S103, when the obtained internal resistance of each battery cell passes through the normality test and the variance homogeneity test, determining the internal resistance of the power battery pack according to the internal resistance of each battery cell.
And carrying out weighted average on the internal resistance value in the data set of the internal resistance of each battery cell through the normality test and the variance homogeneity test, thereby obtaining the internal resistance estimated value corresponding to each battery cell. For example, according to
Obtaining an internal resistance estimated value of the ith battery cell
Wherein, yjIs the jth internal resistance value in the data set of the internal resistance of the ith battery cell, k is the total number of the internal resistance values in the data set of the internal resistance of the ith battery cell, fjThe weight corresponding to the jth internal resistance value in the data set of the internal resistance of the ith battery cell is used, and the ratio of the number of the internal resistances of the ith battery cell in the data set of the internal resistance of the ith battery cell to the number of the internal resistances of the ith battery cell in the data set of the internal resistance of the ith battery cell is equal to the jth internal resistance value. For example, the jth internal resistance value in the data set of the internal resistances of the ith battery cell is 0.5, the total number k of the internal resistance values in the data set is 100, where 30 (including the jth internal resistance value) internal resistance values are the same as the jth internal resistance value, the weight corresponding to the jth internal resistance value is 30/100-0.3, and so on.
After the internal resistance estimated value of each electric core in the power battery pack is obtained, the internal resistance estimated values of all the electric cores in the power battery pack are weighted and averaged to obtain the internal resistance of the power battery pack. For example. According to
Obtaining the internal resistance X of the power battery pack, wherein n is the total number of the battery cores in the power battery pack,
an internal resistance evaluation value q of the ith electric core in the power battery packiAnd the weight corresponding to the internal resistance estimated value of the ith battery cell is used, and the ratio of the number of the internal resistance estimated values in the power battery pack, which is the same as the internal resistance estimated value of the ith battery cell, to n is used. Taking the internal resistance estimated values corresponding to the battery cells in the power battery pack shown in table 4 as an example, the total number of the battery cells in the power battery pack is 24, the weight corresponding to the internal resistance estimated value of the 1 st battery cell is 1/24, that is, only the internal resistance estimated value of the 1 st battery cell is 0.5, the weight corresponding to the internal resistance estimated value of the 2 nd battery cell is 4/24, that is, the internal resistance estimated values of 4 (including the 2 nd battery cell) battery cells are the same as the internal resistance estimated value of the 2 nd battery cell, and the weights of the internal resistance estimated values of other battery cells are analogized in this way.
TABLE 4
In addition, in an embodiment of the present invention, after the internal resistance of the power battery pack is determined according to the internal resistance of each battery cell, the internal resistance estimated value of each battery cell is determined as the corresponding initial internal resistance value, so that after the internal resistance and the index data of each battery cell in the power battery pack are obtained next time, the updated initial internal resistance values are used to determine whether the currently obtained internal resistances of all the battery cells meet the estimation condition.
To facilitate understanding of the embodiment of the present invention, fig. 5 is a schematic flow chart of a method for online estimating internal resistance of a power battery pack according to the embodiment of the present invention, and as shown in fig. 5, the method includes the following steps:
505, performing normality test on the data set of the internal resistance of each battery cell, executing a step 507 through the normality test, and executing a step 506 without the normality test;
step 507, performing a variance and homogeneity test on the data set of the internal resistance of each battery cell, executing step 509 through the variance and homogeneity test, and executing step 508 if the data set of the internal resistance of each battery cell does not pass the variance and homogeneity test;
and 511, carrying out weighted average on the internal resistances of all the battery cores in the power battery pack to obtain the internal resistance of the power battery pack.
The embodiment of the invention improves the calculation precision of the internal resistance of the power battery pack, and judges whether the battery meets the replacement condition or not through comprehensive, more credible and more stable internal resistance. In addition, when the internal resistance of the battery cell in the battery pack is obtained, the judgment of whether the index data of the battery cell meets the preset global distribution is performed when the internal resistances of all the battery cells meet the estimation condition, if the distribution quality of the currently obtained index data of the battery cell is good, the estimation of the internal resistance of the battery pack is performed, and otherwise, the internal resistance of the battery cell is continuously obtained. The condition for estimating the internal resistance of the battery pack is limited to a certain extent, so that the estimation of the internal resistance of the battery cell once acquired is avoided, and the calculation resources and the power consumption are saved. And the acquired internal resistance of each battery cell is subjected to the normality test and the variance homogeneity test, and only when the acquired internal resistance of each battery cell passes the normality test and the variance homogeneity test, the internal resistance of the power battery pack is determined according to the internal resistance of each battery cell, so that the internal resistance estimation precision of the power battery pack is improved, and the data reliability is increased.
Correspondingly, the embodiment of the invention also provides a battery management system, and the battery management system is used for executing the online estimation method of the internal resistance of the power battery pack.
Those skilled in the art will understand that all or part of the steps in the method according to the above embodiments may be implemented by a program, which is stored in a storage medium and includes several instructions to enable a single chip, a chip, or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (9)
1. The online estimation method for the internal resistance of the power battery pack is characterized by comprising the following steps:
acquiring internal resistance and index data of each battery cell in the power battery pack, and judging whether the currently acquired internal resistance of all the battery cells meets an estimation condition, wherein the index data comprises temperature, voltage and charge state;
if the internal resistances of all the currently acquired battery cells meet the estimation condition, judging whether the index data of all the currently acquired battery cells meet preset global distribution;
if the index data of all the electric cores which are obtained currently meet the preset global distribution, performing normality test and variance homogeneity test on the obtained internal resistance of each electric core;
when the acquired internal resistance of each battery cell passes through the normality test and the variance homogeneity test, determining the internal resistance of the power battery pack according to the internal resistance of each battery cell,
wherein, the judging whether the index data of all the electric cores acquired currently meet the preset global distribution includes:
establishing a three-dimensional list of the index data according to a preset numerical range, wherein the three-dimensional list is divided into the same areas with preset number, and the number of tables in each area is the same;
determining the number of the distributed index data of all the currently acquired battery cores in each area in the three-dimensional list;
determining a quality probability value corresponding to each region through a probability quality function according to the number of the distribution of each region;
comparing the quality probability values corresponding to all the regions with a preset probability value;
when the quality probability values corresponding to all the regions are greater than or equal to the preset probability value, determining that the currently acquired index data of all the battery cores meet the preset global distribution;
and when at least one quality probability value smaller than the preset probability value exists in the quality probability values corresponding to all the regions, determining that the currently acquired index data of all the electric cores do not meet the preset global distribution.
2. The method for estimating the internal resistance of the power battery pack on line according to claim 1, wherein the estimation condition is that the internal resistance of each battery cell in the power battery pack is compared with a corresponding initial internal resistance value, and it is determined that the number of the internal resistances of all the battery cells which change exceeds a preset number.
3. The online estimation method for internal resistance of a power battery pack according to claim 1, wherein after the determining whether the currently obtained internal resistances of all the battery cells satisfy the estimation condition, the online estimation method for internal resistance of a power battery pack further comprises:
and if the currently acquired internal resistances of all the battery cores do not meet the estimation condition, continuously acquiring the internal resistance and index data of each battery core in the power battery pack.
4. The online estimation method for internal resistance of a power battery pack according to claim 1, wherein after the determining whether the currently acquired index data of all the battery cells satisfy the preset global distribution, the online estimation method for internal resistance of a power battery pack further comprises:
and if the index data of all the electric cores acquired currently do not meet the preset global distribution, continuously acquiring the internal resistance and the index data of each electric core in the power battery pack.
5. The online estimation method for internal resistance of power battery pack according to claim 2, wherein the performing of the normality test and the homogeneity of variance test on the acquired internal resistance of each battery cell comprises:
determining the currently acquired internal resistance of each of all the battery cells meeting the preset global distribution and the internal resistance of each battery cell acquired before the preset global distribution are met as a data set of the internal resistance of each battery cell;
carrying out normality test on the data set of the internal resistance of each battery cell;
and after the data set of the internal resistance of each battery cell passes the normality test, carrying out the variance homogeneity test on the data set of the internal resistance of each battery cell.
6. The online estimation method for internal resistance of power battery pack according to claim 5, wherein after the performing the normality test and the homogeneity of variance test on the acquired internal resistance of each battery cell, the online estimation method for internal resistance of power battery pack further comprises:
carrying out normalization transformation on a data set of the internal resistance of the battery cell which does not pass the normality test;
and carrying out variance correction on the data set of the internal resistance of the battery cell which does not pass the variance and homogeneity test.
7. The online estimation method for internal resistance of power battery pack according to claim 5, wherein the determining the internal resistance of the power battery pack according to the internal resistance of each battery cell comprises:
according to
Obtaining an internal resistance estimated value of the ith battery cell
Wherein, yjIs the jth internal resistance value in the data set of the internal resistance of the ith battery cell, k is the total number of the internal resistance values in the data set of the internal resistance of the ith battery cell, fjThe weight is corresponding to the jth internal resistance value in the data set of the internal resistance of the ith battery cell, and the ratio of the number of the internal resistances in the data set of the internal resistance of the ith battery cell to the number of the internal resistances in the data set of the internal resistance of the ith battery cell is the same as the jth internal resistance value;
according to
Obtaining the internal resistance X of the power battery pack, wherein n is the total number of the battery cores in the power battery pack,
an internal resistance evaluation value q of the ith electric core in the power battery packiAnd the weight corresponding to the internal resistance estimated value of the ith battery cell is used, and the ratio of the number of the internal resistance estimated values in the power battery pack, which is the same as the internal resistance estimated value of the ith battery cell, to n is used.
8. The online estimation method for internal resistance of power battery pack according to claim 7, wherein after determining the internal resistance of the power battery pack according to the internal resistance of each battery cell, the online estimation method for internal resistance of power battery pack further comprises:
and determining the internal resistance estimated value of each battery cell as a corresponding internal resistance initial value.
9. A battery management system, wherein the battery management system is configured to perform the online estimation method for internal resistance of power battery pack according to any one of claims 1 to 8.
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