CN115015768B - Method for predicting abnormal battery cell of battery pack - Google Patents
Method for predicting abnormal battery cell of battery pack Download PDFInfo
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- CN115015768B CN115015768B CN202210952726.XA CN202210952726A CN115015768B CN 115015768 B CN115015768 B CN 115015768B CN 202210952726 A CN202210952726 A CN 202210952726A CN 115015768 B CN115015768 B CN 115015768B
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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
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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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 pressure difference 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 method avoids 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 using working conditions, different battery cores in the battery pack have inconsistency, and some hardware faults, such as terminal aging or abnormal equalization loops, also can cause or accelerate the inconsistency problem of the battery. This inconsistency, in turn, accelerates the aging and inconsistency of the battery, causing the degradation of the battery to occur more and more rapidly. Therefore, before the battery cells in the battery pack are abnormal, the battery cells with problems need 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 fact that the traditional BMS uses the pressure difference of the battery pack to judge consistency 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 in front and at the back 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 the pressure difference V (i, k) of each battery cell between two continuous running data 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; integer i represents the number of runs of data, i =2,3,4; integer k represents cell number, k =1,2,3.;
s3, calculating the maximum value of the 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) th running data;
s4, calculating the relative pressure difference R (i, k) of each section of battery cell between the two pieces of 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 a total median Rmed _ 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 cell in the sampling time period with the total median Red _ average of the relative pressure differences of all the battery cells:
if the Rmed (k) is more than or equal to f multiplied by the 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 of the pressure difference Δ Vmax (i) 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 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.
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 in front and at the back 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 the 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, 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; integer i represents the number of runs of data, i =2,3,4; integer k represents cell number, k =1,2,3.;
s3, calculating the maximum pressure difference Δ Vmax (i) between the two running data in the following specific mode:
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)) is equal to or greater than Vo, then max (i) = max (V (i, k));
if max (Δ V (i, k)) <vo, then Δ Vmax (i) = Vo;
wherein, vo is 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;
s4, calculating the relative pressure difference R (i, k) of each section of battery cell between the two pieces of 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 pieces of operation data in the sampling time period of the battery pack and in the manner of the steps S2 to S4, sequentially obtaining the relative pressure difference R (i, k) of each battery cell between all two pieces of operation data which are continuous back and forth in the sampling time period, 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 electricity-saving core;
s6, calculating a total median Rmed _ 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 difference 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 a median Rmed (k) of the relative pressure difference of each cell in a sampling time period with a total median Rmed _ overall of the relative pressure difference of all the cells, and if the Rmed (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 operational data of 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 core is a larger value of 3250mV, and if a traditional differential pressure judgment method is directly used, the voltage of the 3 rd electricity core is 50mV higher than the voltages of other electricity cores, the electricity core is considered to belong to an abnormal electricity core; if the method is used, because 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, the problem of data collection can be filtered, the problem of misjudgment can not occur, and the stability of the result is enhanced.
The present invention is not limited to the above embodiments, and any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
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 in front and at the back 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 the 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; integer i represents the number of runs, i =2,3,4; integer k represents cell number, k =1,2,3.;
s3, calculating the maximum value of the pressure difference between the two running data, Δ Vmax (i);
wherein, the Vmax (i) represents the maximum value of the pressure difference between the ith running data and the i-1 st running data;
s4, calculating the relative pressure difference R (i, k) of each section of battery cell between the two pieces of 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 a total median Rmed _ 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 difference 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 cell in the sampling time period with the total median Red _ average of the relative pressure differences of all the battery cells:
if the Rmed (k) is more than or equal to f multiplied by the 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.
2. The method for predicting the abnormal battery cell of the battery pack according to claim 1, wherein in step S3, the maximum voltage difference Δ Vmax (i) 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.
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Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009004389A (en) * | 2008-10-02 | 2009-01-08 | Panasonic Corp | Inspection method of battery |
| WO2011137827A1 (en) * | 2010-10-09 | 2011-11-10 | 华为技术有限公司 | Battery pack and control method thereof |
| JP2014033604A (en) * | 2012-07-13 | 2014-02-20 | Sanyo Electric Co Ltd | Charge/discharge state equalization method for battery system, charge/discharge state change method for battery system, and battery system |
| EP3537730A1 (en) * | 2018-03-09 | 2019-09-11 | Oticon A/s | A method for updating a discharge battery profile |
| CN112378597A (en) * | 2020-10-28 | 2021-02-19 | 东软睿驰汽车技术（沈阳）有限公司 | Battery pack test analysis method, device and system |
| CN112924873A (en) * | 2021-02-04 | 2021-06-08 | 上海玫克生储能科技有限公司 | Online diagnosis method and system for micro short circuit of battery pack |
| WO2021169486A1 (en) * | 2020-02-24 | 2021-09-02 | 上海蔚来汽车有限公司 | Method, system and apparatus for monitoring battery impedance abnormality on basis of charging process |
| CN113466721A (en) * | 2021-08-31 | 2021-10-01 | 蜂巢能源科技有限公司 | Failure identification method and device for lithium ion battery, electronic equipment and medium |
| CN113533979A (en) * | 2021-07-15 | 2021-10-22 | 合肥力高动力科技有限公司 | Method for judging abnormal battery cell of battery pack |
| WO2022033794A1 (en) * | 2020-08-11 | 2022-02-17 | Lisa Dräxlmaier GmbH | Battery management system for classifying a battery module |
| EP3961232A1 (en) * | 2020-09-01 | 2022-03-02 | Samsung Electronics Co., Ltd. | Method and apparatus for battery state estimation |
| TWI758136B (en) * | 2021-03-22 | 2022-03-11 | 新普科技股份有限公司 | State estimation method and battery pack |
| CN114441984A (en) * | 2022-01-27 | 2022-05-06 | 泉州装备制造研究所 | Lithium battery health state estimation method |
| CN114739532A (en) * | 2022-03-23 | 2022-07-12 | 力高(山东)新能源技术有限公司 | Method for judging abnormal temperature acquisition of battery pack |
| CN114779085A (en) * | 2022-04-13 | 2022-07-22 | 上海钧正网络科技有限公司 | Real-time battery thermal runaway detection method, system, device and medium |
| CN114817827A (en) * | 2022-04-29 | 2022-07-29 | 力高(山东)新能源技术有限公司 | Battery pack voltage consistency judgment method |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110752635A (en) * | 2019-10-12 | 2020-02-04 | 山东大学 | Online capacity monitoring and charging and discharging dual-state equalization circuit and method for series battery pack |
| EP3916411B1 (en) * | 2019-11-29 | 2024-01-03 | Mintech Co., Ltd. | Device and method for estimating state of battery |
-
2022
- 2022-08-10 CN CN202210952726.XA patent/CN115015768B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009004389A (en) * | 2008-10-02 | 2009-01-08 | Panasonic Corp | Inspection method of battery |
| WO2011137827A1 (en) * | 2010-10-09 | 2011-11-10 | 华为技术有限公司 | Battery pack and control method thereof |
| JP2014033604A (en) * | 2012-07-13 | 2014-02-20 | Sanyo Electric Co Ltd | Charge/discharge state equalization method for battery system, charge/discharge state change method for battery system, and battery system |
| EP3537730A1 (en) * | 2018-03-09 | 2019-09-11 | Oticon A/s | A method for updating a discharge battery profile |
| WO2021169486A1 (en) * | 2020-02-24 | 2021-09-02 | 上海蔚来汽车有限公司 | Method, system and apparatus for monitoring battery impedance abnormality on basis of charging process |
| WO2022033794A1 (en) * | 2020-08-11 | 2022-02-17 | Lisa Dräxlmaier GmbH | Battery management system for classifying a battery module |
| EP3961232A1 (en) * | 2020-09-01 | 2022-03-02 | Samsung Electronics Co., Ltd. | Method and apparatus for battery state estimation |
| CN112378597A (en) * | 2020-10-28 | 2021-02-19 | 东软睿驰汽车技术（沈阳）有限公司 | Battery pack test analysis method, device and system |
| CN112924873A (en) * | 2021-02-04 | 2021-06-08 | 上海玫克生储能科技有限公司 | Online diagnosis method and system for micro short circuit of battery pack |
| TWI758136B (en) * | 2021-03-22 | 2022-03-11 | 新普科技股份有限公司 | State estimation method and battery pack |
| CN113533979A (en) * | 2021-07-15 | 2021-10-22 | 合肥力高动力科技有限公司 | Method for judging abnormal battery cell of battery pack |
| CN113466721A (en) * | 2021-08-31 | 2021-10-01 | 蜂巢能源科技有限公司 | Failure identification method and device for lithium ion battery, electronic equipment and medium |
| CN114441984A (en) * | 2022-01-27 | 2022-05-06 | 泉州装备制造研究所 | Lithium battery health state estimation method |
| CN114739532A (en) * | 2022-03-23 | 2022-07-12 | 力高(山东)新能源技术有限公司 | Method for judging abnormal temperature acquisition of battery pack |
| CN114779085A (en) * | 2022-04-13 | 2022-07-22 | 上海钧正网络科技有限公司 | Real-time battery thermal runaway detection method, system, device and medium |
| CN114817827A (en) * | 2022-04-29 | 2022-07-29 | 力高(山东)新能源技术有限公司 | Battery pack voltage consistency judgment method |
Non-Patent Citations (3)
| Title |
|---|
| Non-dissipative equalization with voltage-difference based on FPGA for lithium-ion battery;Zhang Fan 等;《2018 Chinese Control And Decision Conference (CCDC)》;20181231;全文 * |
| Performance of inconsistency in lithium-ion battery packs for battery energy storage systems;Man Chen 等;《2014 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)》;20141231;全文 * |
| 基于改进阈值比较法的电动汽车电池故障在线诊断方法;陈如尹等;《电气技术》;20181215(第12期);全文 * |
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