CN107895411B - Lithium ion battery working condition extraction method based on power and power change equivalence - Google Patents
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- 229910001416 lithium ion Inorganic materials 0.000 title claims abstract description 11
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Abstract
The invention discloses a lithium ion battery working condition extraction method based on power and power change equivalence, which comprises the following steps: step 1: arranging to obtain original working condition data, and assuming that the length of the input data is T(s); step 2: calculating power interval probability distribution and power change value interval probability distribution of original working conditions; and step 3: dividing each (T/200) s of the total working condition into a small interval, which is called as a short-time working condition; and 4, step 4: assuming that the length of output data is T(s), randomly selecting (200T/T) data from 200 short-time working conditions in a random selection mode, and splicing the data together in front and back, wherein the assumed target working condition is called; and 5: respectively calculating power interval probability distribution and power change value interval probability distribution of each assumed target working condition, and calculating power interval probability distribution, power change value interval probability distribution and power interval probability distribution of the original working condition of each assumed target working condition; step 6: and simplifying and regulating the obtained target working condition data, and obtaining a final working condition result by using a dynamic averaging method.
Description
Technical Field
The invention belongs to the technical field of lithium ion batteries, and particularly relates to a lithium ion battery working condition extraction method based on power and power change equivalence.
Background
The development and extraction of vehicle test conditions aims at simulating the actual use condition of a vehicle on a laboratory bench, and the most common method is to extract a short-time typical condition segment capable of representing the vehicle running condition from long-time vehicle actual running data. In the practical application of the electric automobile, the power demand of the vehicle on the motor is dynamically changed, and the instantaneous high-current impact and the instantaneous switching between charging and discharging provide higher requirements on the dynamic performance of the power battery. The power battery testing working condition suitable for the actual use condition is made to have important significance for testing and evaluating the dynamic performance of the power battery, and the method is also the basis for testing the working condition service life of the battery in a laboratory.
The change in power has an important effect on the battery, and the impact capability of the battery caused by sudden power change is tested. Most of the existing battery working condition extraction methods only consider the equivalence of power, but ignore the equivalence of power change values.
Disclosure of Invention
In view of the problems and drawbacks described in the background art, the present invention is directed to a method for evaluating equivalence by adding a power variation value to an equivalence evaluation index in consideration of the equivalence of power before and after extraction of a working condition. Meanwhile, in the process, the final working condition is obtained by using a method of repeatedly and iteratively selecting the optimal value for the first time.
A lithium ion battery working condition extraction method based on power and power change equivalence comprises the following steps:
step 1: arranging to obtain original working condition data, and assuming that the length of the input data is T(s);
the original working condition data are mostly real vehicle operation data recorded by a BMS (battery management system), or the original working condition data can be obtained by other sources or methods;
the data length is the duration of the working condition data, and the recording mode is usually one data per 1 s.
The form of the working condition data is mostly data of the battery power along with time, and can also be data of the battery current (multiplying power) along with time. The subsequent steps of the method of the invention have consistent power or current processing modes, so the method explanation is carried out by taking power data as an example.
Step 2: calculating power interval probability distribution and power change value interval probability distribution of original working conditions;
the interval probability distribution is that data is divided into one interval every x, the number of data in each interval in the data is counted, and the number of the data in each interval is divided by the total number of the data to obtain the interval probability distribution;
the power interval probability distribution is used for leading the power to be from the minimum value PminTo a maximum value PmaxDividing each n into an interval, counting the number of data in each interval in the original working condition, and dividing the number of the data in each interval by the total number of the data in the original working condition to obtain the probability distribution of the power interval;
the power change value is a value of power change, namely a value obtained by subtracting a previous second from a next second in original working condition data, and the capacity of the battery for sudden power change is inspected. Subtracting the previous second from the next second of the original working condition data to obtain original working condition power change value data;
the power change value interval probability distribution is used for leading the power change value to be from the minimum value deltaPminTo a maximum value deltaPmaxDividing each m into an interval, counting the number of data in each interval in the power change value of the original working condition, and dividing the number of the data in each interval by the total number of the data of the power change value of the original working condition to obtain the probability distribution of the power change value interval;
and step 3: dividing each (T/200) s of the total working condition into a small interval, which is called as a short-time working condition, and dividing into 200 short-time working conditions.
And 4, step 4: the actual duration of the target condition as the output result may be set according to the specific requirements of different tasks. Assuming that the length of the output data is T(s), randomly selecting (200T/T) data from 200 short-time working conditions in a random selection mode, and splicing the data together front and back, which is called an assumed target working condition. And (3) counting the power interval probability distribution and the power change value interval probability distribution of the assumed target working condition, wherein the power interval probability distribution and the power change value interval probability distribution calculation method is as in the step 2.
And 5: and (3) a combination method of randomly selecting short-time working conditions for multiple times is adopted, and the power interval probability distribution and the power change value interval probability distribution of each assumed target working condition are respectively calculated. And calculating Euclidean distances between the power interval probability distribution and the power change value interval probability distribution of each assumed target working condition and the power interval probability distribution and the power change value interval probability distribution of the original working condition, and selecting a group of results with the minimum Euclidean distances as final working condition extraction results.
Step 6: and simplifying and normalizing the obtained target working condition data, and obtaining the working condition data with a square wave-like effect by using a dynamic averaging method, wherein the working condition data is a final working condition result. The degree of simplification and regulation can be adjusted according to actual conditions. The larger the regularity, the simpler and more aesthetic the working conditions, but the more distortions.
The invention has the beneficial effects that:
the battery temperature is an important factor influencing the aging and the degradation of the battery, and the change of the battery temperature is closely related to the heating power of the battery, so the effectiveness of the working condition extraction method is evaluated according to the equivalence of the heating power of the battery. And counting the probability distribution of the heating power of the target result working condition and the original total working condition. Multiple experiments prove that the heating power of the target result working condition is similar to that of the total working condition, and the method is effective when the heating equivalence of the original total working condition and the target result working condition is met.
The invention provides a method for extracting the working condition of a power battery based on the principle that the target working condition is equivalent to the power and the power change of real vehicle data, which considers the equivalence of the power and the influence of the power change on the impact of the battery in the testing process of the battery, adds the power change value as an important factor, and obtains good effect by using the idea of iterative optimization.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is an original input condition diagram according to an embodiment of the present invention.
FIG. 3 is a power interval probability distribution diagram of an original input condition according to an embodiment of the present invention.
FIG. 4 is a block probability distribution diagram of power variation values of an original input condition according to an embodiment of the present invention.
FIG. 5 is an output condition diagram of an embodiment of the present invention.
Detailed Description
The steps of the present invention will be further described in detail with reference to the attached drawings and an extraction example.
The invention is carried out according to the following steps:
step 1, original working condition data are obtained through sorting, and the length of input data is assumed to be T(s).
The original working condition data are mostly real vehicle operation data recorded by a BMS (battery management system); raw operating condition data may also be obtained from other sources or methods.
The data length is the duration of the working condition data, and the recording mode is usually one data per 1 s.
The form of the working condition data is mostly data of the battery power along with time, and can also be data of the battery current (multiplying power) along with time. The subsequent steps of the method of the invention have consistent power or current processing modes, so the method explanation is carried out by taking power data as an example.
Example input operating condition data is shown in FIG. 2.
Step 2, calculating power interval probability distribution and power change value interval probability distribution of the original working condition;
the interval probability distribution is that data is divided into one interval every x, the number of data in each interval in the data is counted, and the number of the data in each interval is divided by the total number of the data to obtain the interval probability distribution;
the power interval probability distribution is used for leading the power to be from the minimum value PminTo a maximum value PmaxDividing each n into an interval, counting the number of data in each interval in the original working condition, and dividing the number of the data in each interval by the total number of the data in the original working condition to obtain the probability distribution of the power interval;
the value of n is obtained by the following formula:
wherein P ismaxAnd PminRespectively the maximum power value and the minimum power value of the original working condition data, thus obtaining 10 power intervals in total, and expressing the probability of each power interval as a0,a1…a9。
The power change value is a value of power change, namely a value obtained by subtracting a previous second from a next second in original working condition data, and the capacity of the battery for sudden power change is inspected. Subtracting the previous second from the next second of the original working condition data to obtain original working condition power change value data;
the power change value interval probability distribution is used for leading the power change value to be from the minimum value deltaPminTo a maximum value deltaPmaxDividing each m into an interval, counting the number of data in each interval in the power change value of the original working condition, and dividing the number of the data in each interval by the total number of the data of the power change value of the original working condition to obtain the probability distribution of the power change value interval;
the value of m is obtained by the following formula:
wherein deltaPmaxAnd deltaPminRespectively the maximum value and the minimum value of the original working condition power change value data, thus obtaining 10 power change value intervals in total and expressing the probability of each power change value as x0,x1…x9。
The power distribution of the input original condition in the example is shown in fig. 3, and the distribution of the power variation value is shown in fig. 4.
And 3, dividing each (T/200) s of the total working conditions into a small interval, namely short-time working conditions, and obtaining 200 short-time working conditions in total, wherein if the (T/200) is not an integer, the maximum integer is selected.
In the example, the total duration of the original working condition is 18000s, which is divided into 200 short-time working conditions, and the duration of each working condition is 90 s.
And 4, setting the actual time of the target working condition as an output result according to the specific requirements of different tasks. Assuming that the length of the output data is T(s), randomly selecting (200T/T) data from 200 short-time working conditions in a random selection mode, and splicing the data together front and back, which is called an assumed target working condition. The power interval probability distribution and the power change value interval probability distribution of the assumed target working condition are counted, the power interval probability distribution and the power change value interval probability distribution calculation method is as in step 2, and the obtained power interval probability distribution is represented as b0,b1…b9The power variation interval probability distribution is represented as y0,y1…y9；
In the example, the selected target condition is 900s long, and the target condition is formed by connecting 10 short-time conditions.
And 5, a combination method of randomly selecting short-time working conditions for multiple times is adopted, and the power interval probability distribution and the power change value interval probability distribution of each assumed target working condition are calculated respectively. And calculating Euclidean distances between the power interval probability distribution and the power change value interval probability distribution of each assumed target working condition and the power interval probability distribution and the power change value interval probability distribution of the original working condition, and selecting a group of results with the minimum Euclidean distances as final working condition extraction results.
The Euclidean distance is calculated by the formula
Wherein each parameter is consistent with that in step 2 and step 4. The minimum euclidean distance results in the example were 0.022.
And 6, simplifying and normalizing the obtained target working condition data, and applying a dynamic averaging method, wherein the specific method is to average in a time period with small working condition data fluctuation to be used as a value of the time period to obtain working condition data with a square wave-like effect, namely the final working condition result. The definition of the simplified regularization degree, namely the fluctuation size, can be adjusted according to actual conditions. The larger the regularity, the simpler and more aesthetic the working conditions, but the more distortions.
A schematic of example condition extraction results is shown in FIG. 5.
The above embodiments describe the technical solutions of the present invention in detail. It will be clear that the invention is not limited to the described embodiments. Based on the embodiments of the present invention, those skilled in the art can make various changes, but any changes equivalent or similar to the present invention are within the protection scope of the present invention.
Claims (5)
1. A lithium ion battery working condition extraction method based on power and power change equivalence is characterized by comprising the following steps:
step 1: arranging to obtain original working condition data, and assuming that the length of the input data is T(s); the original working condition data is real vehicle operation data recorded by the BMS or original working condition data; the data length is the duration of working condition data, and the recording mode is one data per 1 s; the working condition data is in the form of data of battery power along with time or data of battery current along with time;
step 2: calculating power interval probability distribution and power change value interval probability distribution of original working conditions;
and step 3: dividing each T/200(s) of the original working condition into a small interval, called as short-time working condition, and obtaining 100 short-time working conditions by dividing;
and 4, step 4: the actual duration of the target working condition is set according to specific requirements, the length of output data is assumed to be T(s), 200T/T working conditions are randomly selected from 200 short-time working conditions in a random selection mode and spliced together in the front and back, and the assumed target working condition is called; counting the power interval probability distribution and the power change value interval probability distribution of the assumed target working condition, wherein the power interval probability distribution and the power change value interval probability distribution calculation method is as the step 2;
and 5: respectively calculating power interval probability distribution and power change value interval probability distribution of each assumed target working condition, calculating Euclidean distances among the power interval probability distribution and the power change value interval probability distribution of each assumed target working condition, the power interval probability distribution of the original working condition and the power change value interval probability distribution, and selecting a group of results with the minimum Euclidean distance as final working condition extraction results;
step 6: simplifying and normalizing the obtained target working condition data, and obtaining working condition data with a square wave-like effect by using a dynamic averaging method, namely a final working condition result;
in step 2, the interval probability distribution is obtained by dividing data into intervals every x, counting the number of data in each interval in the data, and dividing the number of data in each interval by the total number of data; the probability distribution of the power interval is that the power is from a minimum value PminTo a maximum value PmaxDividing each n into an interval, counting the number of data in each interval in the original working condition, and dividing the number of the data in each interval by the total number of the data in the original working condition to obtain power interval probability distribution; the power change value is a value obtained by subtracting the previous second from the next second in the original working condition data, so that the capacity of the battery for bearing power sudden change is inspected; subtracting the previous second from the next second of the original working condition data to obtain original working condition power change value data; the probability distribution of the power change value interval is that the power change value is changed from a minimum value deltaPminTo a maximum value deltaPmaxDividing each m into an interval, counting the number of data in each interval in the power change value of the original working condition, and dividing the number of the data in each interval by the total number of the data of the power change value of the original working condition, so as to obtain the probability distribution of the power change value interval.
2. The method for extracting the working condition of the lithium ion battery according to claim 1, wherein in the step 3, when T/200 is not an integer, the maximum integer is taken.
3. The method for extracting working conditions of lithium ion batteries according to claim 1, wherein the probability distribution of the obtained power interval in the step 4 is represented as b0,b1…b9The power variation interval probability distribution is represented as y0,y1…y9。
4. The method for extracting operating conditions of lithium ion batteries according to claim 1, wherein in step 5, the calculation formula of the Euclidean distance is
Wherein d is the Euclidean distance; in step 2, the probability distribution of the power interval of the original working condition is a0,a1…a9(ii) a In step 2, the probability distribution of the interval of the power change values of the original working conditions is x0,x1…x9(ii) a The probability distribution of the power interval of the target working condition in the step 4 is b0,b1…b9(ii) a The power change value interval probability distribution of the target working condition in the step 4 is y0,y1…y9。
5. The method for extracting the working conditions of the lithium ion battery according to claim 1, wherein the degree of the simplification and normalization in the step 6 is adjusted according to actual conditions; the larger the regularity, the simpler and more aesthetic the working conditions, but the more distortions.
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| CN109358291B (en) * | 2018-11-01 | 2019-11-05 | 中国海洋大学 | The simplification method of energy-storage system battery operating condition |
| CN110348164B (en) * | 2019-07-18 | 2022-12-16 | 国网山东省电力公司电力科学研究院 | Correction method for designed heat supply working condition diagram of thermoelectric unit |
| CN111044927B (en) * | 2019-12-25 | 2021-11-23 | 中国第一汽车股份有限公司 | Power battery service life evaluation method and system |
| CN111882697B (en) * | 2020-07-31 | 2021-11-16 | 中国汽车工程研究院股份有限公司 | Probability mutation rule-based voltage abnormal single body identification algorithm |
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