CN115144778A - Method for estimating internal resistance of battery by big data - Google Patents
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- CN115144778A CN115144778A CN202211068130.XA CN202211068130A CN115144778A CN 115144778 A CN115144778 A CN 115144778A CN 202211068130 A CN202211068130 A CN 202211068130A CN 115144778 A CN115144778 A CN 115144778A
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Abstract
The invention discloses a method for estimating internal resistance of a battery by big data, which relates to the technical field of battery packs and is used for acquiring operation data of the battery within a period of time; dividing the running data into a plurality of sections according to the SOC of the battery; respectively calculating the current standard deviation and the voltage standard deviation of each interval according to the operation data in each interval; and respectively calculating the internal resistance of the battery in each interval according to the current standard deviation and the voltage standard deviation of each interval. The method and the device use a large amount of running data of the battery within a period of time to comprehensively calculate the internal resistance of the battery, avoid the problem of wrong calculation of the internal resistance of the battery caused by asynchronous sampling of single data or sampling error and the like, and use the characteristic that the internal resistance of the battery cannot be suddenly changed to calculate the internal resistance of the battery by using adjacent batch data, namely data in an interval, so that the result is more reliable. The invention calculates the internal resistance of the battery in each interval and provides good data support for subsequent abnormal battery judgment.
Description
Technical Field
The invention relates to the technical field of battery packs, in particular to a method for estimating internal resistance of a battery by big data.
Background
The battery is used as a core part of the new energy automobile, and the stable operation of the battery is very important for ensuring the driving safety of the electric automobile. The battery has aging effect, after long-time use, the capacity and the internal resistance are changed compared with the factory, due to the inconsistency of the battery, the aging conditions of different batteries are different, and the aging conditions are represented by the inconsistency of the internal resistance and the capacity of the batteries. Due to the short plate effect of the battery pack, the battery with poor electrical performance influences the overall performance of the battery pack, and in extreme cases, the abnormal battery even threatens the safety of the battery pack. In order to find out the aging of the battery and the possible abnormality in time, the abnormal battery can be identified by calculating the internal resistance of the battery.
On an electric vehicle, sampling of battery voltage and current is done by a Battery Management System (BMS). Due to the influence of factors such as sampling frequency, processing mode, signal transmission time and the like, the sampling of the battery voltage and the current is not completely synchronous. If the internal resistance of the battery is calculated by adopting the incompletely synchronous signal, the problem of wrong calculation of the internal resistance exists.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a method for estimating the internal resistance of a battery by big data, which can accurately calculate the internal resistance of the battery.
In order to achieve the purpose, the invention adopts the following technical scheme that:
a method for estimating internal resistance of a battery by big data comprises the following steps:
s1, acquiring operation data of a battery within a period of time;
s2, dividing the running data into n intervals according to the SOC of the battery;
s3, respectively calculating the current standard deviation sigma I (k) and the voltage standard deviation sigma V (k) of each interval according to the operation data in each interval; wherein σ I (k) represents a current standard deviation of the kth interval; σ V (k) represents a voltage standard deviation of the kth section, k represents a section number, and k =1,2,3.. N;
and S4, respectively calculating the internal resistance R (k) of the battery in each interval according to the current standard deviation sigma I (k) and the voltage standard deviation sigma V (k) of each interval:
R(k)=σV(k)/σI(k)；
wherein, R (k) represents the internal resistance of the battery in the kth interval.
Preferably, in step S2, the section division method is specifically as follows:
if it is, SOC, n = 100/SOC, the SOC ranges from small to large being [0, SOC ], (SOC, 2, SOC,3, Δ SOC,100].
Preferably, the current standard deviation σ I (k) and the voltage standard deviation σ V (k) of each section are calculated as follows:
wherein I (I, k) represents a current value in the I-th operation data in the k-th interval, V (I, k) represents a voltage value in the I-th operation data in the k-th interval, I =1,2,. Nk, nk represents the total number of operation data in the k-th interval; avg _ I (k) represents the current average value in the kth interval, and avg _ V (k) represents the voltage average value in the kth interval.
Preferably, the SOC of the battery pack is used as the SOC of the battery.
Preferably, operation data of each battery in the battery pack within a period of time are acquired, and the internal resistance of each battery in the battery pack in each interval is calculated in the manner of the steps S1 to S4; the abnormal battery is judged by comparing the internal resistance of each battery in each interval, which is specifically as follows:
if the internal resistance difference value of a certain battery in the battery pack in two adjacent intervals exceeds a set threshold value, the battery is an abnormal battery;
if the difference between the internal resistance of a certain battery and the internal resistances of other batteries in the battery pack exceeds a set threshold value in the same interval, the battery is indicated as an abnormal battery.
The invention has the advantages that:
(1) The invention uses a large amount of running data of the battery in a period of time to comprehensively calculate the internal resistance of the battery, thereby avoiding the problem of wrong calculation of the internal resistance of the battery caused by asynchronous sampling or sampling error of single data.
(2) The invention utilizes the characteristic that the internal resistance of the battery can not change suddenly, and uses the adjacent batch data, namely the internal resistance of the battery calculated by the data in one interval, so that the result is more reliable.
(3) Because the voltage of the battery changes along with the change of the current, the ratio of the change amplitude of the voltage to the change amplitude of the current is the internal resistance R of the battery according to the formula Δ V = R Δ I. And because the internal resistance of the battery cannot be suddenly changed, the internal resistances of the batteries close to the SOC are approximately the same. The voltage and the current are respectively plotted on a coordinate axis along with the change of time, the voltage and the current both present a data band with a certain width, and the ratio of the widths of the voltage data band and the current data band is the internal resistance R of the battery. Thus, the problem of calculating the internal resistance of the battery by using a single piece of data is converted into the problem of calculating the internal resistance of the battery by using the width ratio of a voltage data band and a current data band which are formed by a plurality of pieces of data. Furthermore, in order to avoid the influence of individual outliers on the calculation result, when the width of the data band is calculated, only the width of the data band formed by most data is counted according to a statistical theory, so that the standard deviation of the data is used for calculating the internal resistance of the battery, and the settlement result is more accurate.
(4) The invention calculates the internal resistance of the battery in each interval to provide good data support for subsequent judgment of the abnormal battery, for example, the abnormal battery is judged by comparing the difference of the resistance of each battery in the battery pack in the same interval, or the abnormal battery is judged by comparing the internal resistance change conditions of the battery in two adjacent intervals, namely the change conditions of the internal resistance of the battery along with time.
Drawings
FIG. 1 is a flow chart of a method for estimating internal resistance of a battery by big data according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples 1,
As shown in fig. 1, a method for estimating internal resistance of a battery by big data comprises the following steps:
s1, acquiring running data of the battery within a period of time, and requiring the time interval between the first data and the last data to be less than a range T;
s2, dividing the operation data into a plurality of sections according to the SOC of the battery, wherein the sections are represented by integers k, and k =1,2, 3;
the SOC (State of charge) of the battery, which is a State of charge and reflects the remaining capacity of the battery, is numerically defined as a ratio of the remaining capacity to the battery capacity, and ranges from 0 to 100, and indicates that the battery is completely discharged when the SOC =0, and indicates that the battery is completely charged when the SOC = 100.
In the present embodiment, in order to simplify the SOC measurement process of each battery, the SOC of the battery pack is directly used as the SOC of the single battery.
In step S2, the interval division method is specifically as follows:
setting an SOC, and dividing the SOC into n intervals from small to large, namely k =1,2,3.. N;
n =100, Δ, the n intervals from small to large are [0, SOC ], (2, 3, SOC ], (n, 100).
S3, calculating a current standard deviation sigma I (k) and a voltage standard deviation sigma V (k) of each interval according to the operation data in each interval;
wherein σ I (k) represents a current standard deviation of a kth interval, I (I, k) represents a current value in the ith operation data in the kth interval, I =1,2,. Nk, nk represents a total number of operation data in the kth interval; avg _ I (k) represents the current average value of the kth interval;
σ V (k) represents a voltage standard deviation of a kth interval, V (i, k) represents a voltage value in the ith operation data in the kth interval, i =1,2,. Nk, nk represents a total number of operation data in the kth interval; avg _ V (k) represents the voltage average value of the kth interval;
and S4, calculating the internal resistance R (k) of the battery in each interval according to the current standard deviation sigma I (k) and the voltage standard deviation sigma V (k) of each interval:
R(k)=σV(k)/σI(k)；
wherein, R (k) represents the internal resistance of the battery in the kth interval.
Examples 2,
The operation data of each battery in the battery pack in a period of time is acquired, the internal resistances of each battery in each interval in the battery pack are calculated by using the calculation method of the embodiment 1, and the internal resistances of each battery in each interval are compared to judge the abnormal battery, which is specifically as follows:
if the internal resistance difference value of a certain battery in the battery pack in two adjacent intervals exceeds a set threshold value, the internal resistance of the battery is suddenly changed, namely the battery is an abnormal battery. And (4) judging the abnormal battery by comparing the internal resistance change conditions of the battery in two adjacent sections, namely the change conditions of the internal resistance of the battery along with time.
If the internal resistance of a certain battery in the battery pack is greatly different from the internal resistance of other batteries in the same interval, the battery is an abnormal battery.
In this embodiment, whether a large difference exists between the resistance of each battery and the internal resistance of other batteries is determined by calculating an average value of the resistances of the batteries in the battery pack in the same interval and determining a difference value between the resistance of each battery and the average value, and if the difference value between the resistance of a certain battery and the average value exceeds a set threshold, it indicates that the internal resistance of the battery and the internal resistance of other batteries are large, that is, the battery is an abnormal battery;
the difference between the resistance of a certain battery and the internal resistances of other batteries can be calculated respectively, the number of the differences exceeding the set threshold is counted, and if the number of the differences exceeding the set threshold exceeds the set number, the internal resistance of the battery is represented to have a large difference with the internal resistances of other batteries, that is, the battery is represented to be an abnormal battery.
The invention is not to be considered as limited to the specific embodiments shown and described, but is to be understood to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the appended claims.
Claims (5)
1. A method for estimating internal resistance of a battery by big data is characterized by comprising the following steps:
s1, acquiring operation data of a battery within a period of time;
s2, dividing the running data into n intervals according to the SOC of the battery;
s3, respectively calculating the current standard deviation sigma I (k) and the voltage standard deviation sigma V (k) of each interval according to the operation data in each interval; wherein σ I (k) represents a current standard deviation of the kth interval; σ V (k) represents a voltage standard deviation of the kth section, k represents a section number, and k =1,2,3.. N;
and S4, respectively calculating the internal resistance R (k) of the battery in each interval according to the current standard deviation sigma I (k) and the voltage standard deviation sigma V (k) of each interval:
R(k)=σV(k)/σI(k)；
wherein, R (k) represents the internal resistance of the battery in the kth interval.
2. The method for estimating internal resistance of battery by big data according to claim 1, wherein in step S2, the interval division manner is specifically as follows:
setting an SOC, n = 100/SOC, the SOC ranges of the n intervals are [ 0], SOC ], (. DELTA.SOC, 2. DELTA.SOC ], (2. SOC, 3.Δ SOC.) (n. Δ SOC, 100) from small to large.
3. The method for estimating internal resistance of battery according to claim 1, wherein the standard deviation of current σ I (k) and the standard deviation of voltage σ V (k) of each interval are calculated as follows:
wherein I (I, k) represents a current value in the I-th operation data in the k-th section, V (I, k) represents a voltage value in the I-th operation data in the k-th section, I =1,2,. Nk, nk represents the total number of operation data in the k-th section; avg _ I (k) represents a current average value in the kth interval, and avg _ V (k) represents a voltage average value in the kth interval.
4. The method for estimating the internal resistance of the battery by the big data as claimed in claim 1, wherein the SOC of the battery pack is used as the SOC of the battery.
5. The method for estimating internal resistance of the battery according to any one of claims 1 to 4, wherein the operation data of each battery in the battery pack in a period of time is obtained, and the internal resistance of each battery in the battery pack in each interval is calculated by using the manner of the steps S1 to S4; the abnormal battery is judged by comparing the internal resistances of the batteries in the intervals, which is specifically as follows:
if the internal resistance difference value of a certain battery in the battery pack in two adjacent intervals exceeds a set threshold value, the battery is an abnormal battery;
if the difference between the internal resistance of a certain battery in the battery pack and the internal resistances of other batteries exceeds a set threshold value in the same interval, the battery is judged to be an abnormal battery.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116718937A (en) * | 2023-06-30 | 2023-09-08 | 惠州亿纬锂能股份有限公司 | Internal resistance estimation method, battery management system, and computer-readable medium |
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|---|---|---|---|---|
| CN116718937A (en) * | 2023-06-30 | 2023-09-08 | 惠州亿纬锂能股份有限公司 | Internal resistance estimation method, battery management system, and computer-readable medium |
| CN116718937B (en) * | 2023-06-30 | 2024-04-02 | 惠州亿纬锂能股份有限公司 | Internal resistance estimation method, battery management system, and computer-readable medium |
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