CN115015768A - Method for predicting abnormal battery cell of battery pack - Google Patents
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- 230000005611 electricity Effects 0.000 description 2
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
- G01R31/3835—Arrangements for monitoring battery or accumulator variables, e.g. SoC involving only voltage measurements
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract
The invention discloses a method for predicting an abnormal battery cell of a battery pack, which relates to the technical field of batteries, and is characterized by acquiring a plurality of pieces of running data of the battery pack within a period of time, namely a sampling time period, and calculating the pressure difference of each battery cell between two pieces of running data aiming at the two pieces of running data which are continuous front and back within the sampling time period; calculating the maximum value of the pressure difference between the two operation data; calculating the relative pressure difference of each section of the battery cell between the two pieces of operation data; calculating the median of the relative pressure difference of each electricity-saving core in the sampling time period; calculating the total median of the relative differential pressure of all the battery cores in the sampling time period; and judging whether the battery cell is abnormal or not by comparing the median of the relative pressure difference of each battery cell with the total median of the relative pressure differences of all the battery cells in the sampling time period. The invention avoids the misjudgment caused by using a fixed threshold value, and is applicable to batteries under different working conditions and aging conditions; meanwhile, the problem of unstable judgment results caused by single data is avoided, and the accuracy of the results is improved.
Description
Technical Field
The invention relates to the technical field of batteries, in particular to a method for predicting an abnormal battery cell of a battery pack.
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. Due to the difference of manufacturing processes or use conditions, different battery cells in the battery pack have inconsistency, and some hardware faults, such as terminal aging or abnormal equalization loop, can also cause or accelerate the inconsistency problem of the battery. This inconsistency, in turn, accelerates the aging and inconsistency of the battery, causing the battery to deteriorate more and more quickly. Therefore, before the battery cell in the battery pack is abnormal, the battery cell with the problem needs to be accurately found out, corresponding processing is timely carried out, hidden dangers are eliminated, and stable operation of the battery and a vehicle is guaranteed. The current Battery Management System (BMS) judges consistency by using the pressure difference of the battery pack, and when the pressure difference exceeds a certain preset threshold value, the inconsistency of the battery cell is shown. However, the effect of the judgment in this way depends on the threshold value set in advance by experience, and the judgment way does not change along with the working condition of the battery, so that the misjudgment is easy to occur for the battery used for a period of time or the battery under extreme working conditions such as large current, high and low temperature and other environments.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a method for predicting an abnormal battery cell of a battery pack, which solves the problem caused by the consistency judgment of the voltage difference of the battery pack used by the traditional BMS and accurately finds the abnormal battery cell.
In order to achieve the purpose, the invention adopts the following technical scheme that:
a method of predicting an abnormal cell of a battery pack, comprising the steps of:
s1, obtaining a plurality of running data of the battery pack within a period of time, namely a sampling period, wherein the time interval between two running data which are continuous before and after is less than Δ T;
the operation data comprises: the voltage value of each battery cell and the acquisition time of the operation data;
s2, calculating a pressure difference Δ V (i, k) of each battery cell between two running data according to the two running data which are continuous before and after the sampling time period:
∆V(i,k)=|V(i,k)-V(i-1,k)|；
wherein, Δ V (i, k) represents the pressure difference of the kth cell between the ith running data and the (i-1) th running data; v (i-1, k) represents the pressure value of the kth cell in the i-1 th operation data; v (i, k) represents the pressure value of the kth cell in the ith operation data; the integer i represents the number of pieces of operating data, i =2,3, 4.; integer k represents the cell number, k =1,2, 3.;
s3, calculating the maximum pressure difference between the two running data, Δ Vmax (i);
wherein, Δ Vmax (i) represents the maximum value of the pressure difference between the ith running data and the i-1 st running data;
and S4, calculating the relative pressure difference R (i, k) of each section of battery cell between the two operation data:
R(i,k)=∆V(i,k)/∆Vmax(i)；
wherein R (i, k) represents the relative pressure difference of the kth cell between the ith operating data and the (i-1) th operating data;
s5, calculating a median Rmed (k) of the relative pressure difference of each core in the sampling time period:
Rmed(k)=median(R(i,k))；
wherein mean (·) represents a median function; rmed (k) represents the median of the relative differential pressure of the kth cell;
s6, calculating the total median Red _ average of the relative pressure differences of all the battery cores in the sampling time period according to the median Rmed (k) of the relative pressure differences of each battery core in the sampling time period:
Rmed_overall=median(Rmed(k))；
wherein Rmed _ overall represents the total median of the relative pressure difference of all the cells in the sampling time period;
s7, comparing the median Red (k) of the relative pressure difference of each battery core in the sampling time period with the total median Red _ average of the relative pressure differences of all the battery cores:
if the Rmed (k) is more than or equal to f multiplied by Rmed _ overall, the kth cell is an abnormal cell; otherwise, the cell in the kth section is a normal cell;
wherein f is a threshold.
Preferably, in step S3, the maximum value Δ Vmax (i) of the pressure difference between the two running data is calculated as follows:
searching the maximum value max (Δ V (i, k)) from the pressure difference Δ V (i, k) of each battery cell between the two running data,
if max (Δ V (i, k)) > Δ Vo, then Δ Vmax (i) = max (Δ V (i, k));
if max (Δ V (i, k)) <vo, then Δ Vmax (i) =vo;
wherein, the Δ Vo is the threshold; max (.) represents the maximum function.
The invention has the advantages that:
(1) the invention provides a method for calculating the relative pressure difference of each battery cell, which avoids misjudgment caused by using a fixed threshold value by comparing the relative pressure differences among different battery cells, and is applicable to batteries under different working conditions and aging conditions.
(2) According to the method, the abnormal battery cell is judged by counting a large amount of running data in a period of time, so that the problem of unstable judgment result caused by single data is avoided, and the accuracy of the result is improved.
Drawings
Fig. 1 is a flowchart of a method for predicting an abnormal electric core of a battery pack 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 obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
As shown in fig. 1, a method for predicting an abnormal cell of a battery pack includes the following steps:
s1, obtaining a plurality of running data of the battery pack within a period of time, namely a sampling period, wherein the time interval between two running data which are continuous before and after is less than Δ T;
the operation data comprises: the voltage value of each battery cell and the acquisition time of the operation data;
s2, calculating a pressure difference Δ V (i, k) of each battery cell between two running data according to the two running data which are continuous before and after the sampling time period:
∆V(i,k)=|V(i,k)-V(i-1,k)|；
wherein, Δ V (i, k) represents the pressure difference of the kth cell between the ith running data and the (i-1) th running data; v (i-1, k) represents the pressure value of the kth cell in the i-1 th operation data; v (i, k) represents the pressure value of the kth cell in the ith operation data; the integer i represents the number of pieces of operating data, i =2,3, 4.; integer k represents the cell number, k =1,2, 3.;
s3, calculating the maximum pressure difference Δ Vmax (i) between the two running data in the following specific manner:
the maximum value max is searched from the pressure difference V (i, k) of each battery cell between the two operation data,
if max (Δ V (i, k)) is equal to or greater than Vo, max (i) max (V (i, k));
if max (Δ V (i, k)) <vo, then max (i) = Vo;
wherein, the Δ Vo is the threshold; Δ Vmax (i) represents the maximum value of the pressure difference between the ith running data and the i-1 st running data; max (.) represents a maximum function;
and S4, calculating the relative pressure difference R (i, k) of each section of battery cell between the two operation data:
R(i,k)=∆V(i,k)/∆Vmax(i)；
wherein R (i, k) represents the relative pressure difference of the kth cell between the ith operating data and the (i-1) th operating data;
s5, according to a plurality of running data in the battery pack sampling time period, sequentially obtaining the relative pressure difference R (i, k) of each battery cell between two running data which are continuous before and after in the sampling time period in the mode of steps S2-S4, and calculating the median Rmed (k) of the relative pressure difference of each battery cell in the sampling time period:
Rmed(k)=median(R(i,k))；
wherein mean (·) represents a median function; rmed (k) represents the median of the relative differential pressure of the kth cell;
s6, calculating the total median Red _ average of the relative pressure differences of all the battery cores in the sampling time period according to the median Rmed (k) of the relative pressure differences of each battery core in the sampling time period:
Rmed_overall=median(Rmed(k))；
wherein mean (·) represents a median function; the Rmed _ overall represents the total median of the relative pressure difference of all the cells in the sampling time period;
s7, comparing the median Red (k) of the relative pressure difference of each cell in the sampling time period with the total median Red _ overall of the relative pressure difference of all the cells, and if the Red (k) is more than or equal to f multiplied by Rmed _ overall, indicating that the kth cell is an abnormal cell; otherwise, the cell in the kth section is a normal cell; wherein f is a threshold.
The invention provides a method for calculating the relative pressure difference of each battery cell, which avoids misjudgment caused by using a fixed threshold value by comparing the relative pressure differences among different battery cells, and is applicable to batteries under different working conditions and aging conditions. According to the method, the abnormal battery cell is judged by counting a large amount of running data in a period of time, so that the problem of unstable judgment result caused by single data is avoided, and the accuracy of the result is improved.
For 6 runs over time as shown in the following table:
table 1 6 run data for a battery pack over a period of time
The table shows 6 pieces of operation data of the battery pack in a period of time, and the battery pack has 5 cells in common, and each piece of operation data includes a voltage value of each cell. When the 1 st operation data is acquired, due to acquisition errors, the voltage acquired by the 3 rd electricity-saving core is a large value 3250mV, and if the voltage of the 3 rd electricity-saving core is higher than the voltages of other electricity cores by 50mV by directly using a traditional differential pressure judgment method, the electricity-saving core is considered to belong to an abnormal electricity core; if the method is used, the median of all the voltage change value ratios is used for judgment, and the data at other moments are also used for statistical calculation, so that the collected problematic data can be filtered, the problem of erroneous judgment can not occur, and the stability of the result is enhanced.
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 (2)
1. A method for predicting abnormal cells of a battery pack is characterized by comprising the following steps:
s1, obtaining a plurality of running data of the battery pack within a period of time, namely a sampling period, wherein the time interval between two running data which are continuous before and after is less than Δ T;
the operation data comprises: the voltage value of each battery cell and the acquisition time of the operation data;
s2, calculating a pressure difference Δ V (i, k) of each battery cell between two running data according to the two running data which are continuous before and after the sampling time period:
∆V(i,k)=|V(i,k)-V(i-1,k)|；
wherein, Δ V (i, k) represents the pressure difference of the kth cell between the ith running data and the (i-1) th running data; v (i-1, k) represents the pressure value of the kth cell in the i-1 th operation data; v (i, k) represents the pressure value of the kth cell in the ith operation data; the integer i represents the number of pieces of operating data, i =2,3, 4.; integer k represents the cell number, k =1,2, 3.;
s3, calculating the maximum pressure difference between the two running data, Δ Vmax (i);
wherein, Δ Vmax (i) represents the maximum value of the pressure difference between the ith running data and the i-1 st running data;
and S4, calculating the relative pressure difference R (i, k) of each section of battery cell between the two operation data:
R(i,k)=∆V(i,k)/∆Vmax(i)；
wherein R (i, k) represents the relative pressure difference of the kth cell between the ith operating data and the (i-1) th operating data;
s5, calculating a median Rmed (k) of the relative pressure difference of each core in the sampling time period:
Rmed(k)=median(R(i,k))；
wherein mean (·) represents a median function; rmed (k) represents the median of the relative differential pressure of the kth electricity-saving core;
s6, calculating the total median Red _ average of the relative pressure differences of all the battery cores in the sampling time period according to the median Rmed (k) of the relative pressure differences of each battery core in the sampling time period:
Rmed_overall=median(Rmed(k))；
wherein Rmed _ overall represents the total median of the relative pressure difference of all the cells in the sampling time period;
s7, comparing the median Red (k) of the relative pressure difference of each battery core in the sampling time period with the total median Red _ average of the relative pressure differences of all the battery cores:
if the Rmed (k) is more than or equal to f multiplied by Rmed _ overall, the kth cell is an abnormal cell; otherwise, the kth cell is a normal cell;
wherein f is a threshold.
2. The method of claim 1, wherein in step S3, the maximum voltage difference Δ vmax (i) between the two operation data is calculated as follows:
searching the maximum value max (Δ V (i, k)) from the pressure difference Δ V (i, k) of each battery cell between the two running data,
if max (Δ V (i, k)) > Δ Vo, then Δ Vmax (i) = max (Δ V (i, k));
if max (Δ V (i, k)) <vo, then Δ Vmax (i) =vo;
wherein, the Δ Vo is the threshold; max (.) represents the maximum function.
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