CN112255560B - Battery cell health degree prediction method - Google Patents
Battery cell health degree prediction method Download PDFInfo
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- CN112255560B CN112255560B CN202011117863.9A CN202011117863A CN112255560B CN 112255560 B CN112255560 B CN 112255560B CN 202011117863 A CN202011117863 A CN 202011117863A CN 112255560 B CN112255560 B CN 112255560B
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- 238000000034 method Methods 0.000 title claims abstract description 34
- 230000036541 health Effects 0.000 title claims abstract description 16
- 230000002159 abnormal effect Effects 0.000 claims abstract description 21
- 238000001514 detection method Methods 0.000 claims abstract description 6
- 238000007600 charging Methods 0.000 claims description 24
- 239000000178 monomer Substances 0.000 claims description 16
- 230000032683 aging Effects 0.000 claims description 12
- 238000013210 evaluation model Methods 0.000 claims description 7
- 230000015556 catabolic process Effects 0.000 claims description 6
- 238000006731 degradation reaction Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 3
- 238000010277 constant-current charging Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 230000006866 deterioration Effects 0.000 claims 1
- 230000000694 effects Effects 0.000 abstract description 5
- 238000012544 monitoring process Methods 0.000 abstract description 4
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000000523 sample Substances 0.000 description 1
Classifications
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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]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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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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- General Physics & Mathematics (AREA)
- Secondary Cells (AREA)
- Tests Of Electric Status Of Batteries (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention relates to the technical field of electric automobiles, and provides a battery cell health degree prediction method, which comprises the following steps: s1, preliminarily predicting whether a battery cell with poor consistency exists or not based on an entropy failure prediction model; s2, if the detection result is yes, determining the abnormal battery cell based on the deviation fault prediction model. The platform data is utilized, the real-time data of the battery pack is screened by the monitoring platform regularly, the prediction effect is achieved through modeling, and the accident early warning is achieved, so that unpacking or factory return is avoided to a certain extent, the cost is not increased, and the stability of the single battery of the vehicle can be monitored.
Description
Technical Field
The invention relates to the technical field of power batteries, and provides a battery cell health degree prediction method.
Background
The new energy safety management mechanism is relatively lacking in the market, the safety accidents in the market are comprehensively planned, and the battery problem occupies 75% -80%. In order to improve the safety of the new energy vehicle, the battery of the new energy vehicle is monitored and pre-warned, so that early warning of accidents and big data are striven for. However, the current big data platform is mainly applied to fault monitoring, measures can be taken after faults occur, the battery pack is very passive, the problem can be thoroughly solved only by returning to factories or unpacking, time and money are consumed, and the battery pack can analyze the battery cell problem by analyzing the message data of the accident vehicle.
Disclosure of Invention
The invention provides a battery cell health degree prediction method, which aims to improve the problems.
The invention is realized in such a way that a battery cell health degree prediction method comprises the following steps:
S1, preliminarily predicting whether a battery cell with poor consistency exists or not based on an entropy failure prediction model;
s2, if the detection result is yes, determining the abnormal battery cell based on the deviation fault prediction model.
Further, the battery cell consistency prediction method based on the entropy failure prediction model specifically comprises the following steps:
s11, taking the monomer voltage in a time window as input data, and calculating the entropy value of each monomer cell under the time window;
s12, calculating the variation coefficient of each single cell, and if the variation coefficient is larger than a coefficient threshold value, primarily judging the consistency difference of the corresponding cells.
Further, the calculation formula of the variation coefficient of the single cell is specifically as follows:
Wherein E i represents the entropy of the ith monomer cell, E ave represents the average value of the entropy of all the monomer cells in the time window, and a i represents the standard deviation of the ith monomer cell.
Further, the abnormal cell determination method based on the deviation fault prediction model specifically comprises the following steps:
s21, obtaining a single voltage in a set time period;
S22, judging whether the set time period is a valid time period, if yes, executing step S23, if no, executing step S21,
S23, dividing the effective time period into a plurality of sub-time periods, calculating the voltage deviation sum of all the single battery cells in each sub-time period, and counting the abnormal frequency of the corresponding single battery cell once if the voltage deviation sum is larger than a deviation threshold value to obtain the abnormal frequency of each single battery cell;
S24, the single battery cell with the abnormal frequency value larger than the counting threshold value is regarded as the abnormal battery cell.
Further, the voltage deviation and H i calculation formula of the sub-period single cell are as follows:
Wherein t 1 represents the start time of the sub-period, t 2 represents the end time of the sub-period, u i,t represents the cell voltage of each cell at time t, u o,t represents the total voltage of the battery at time t, and n represents the number of cells in the battery pack.
Further, after step S2, the method further includes:
And S3, evaluating the aging degree of the current battery based on the battery degradation evaluation model.
Further, the battery aging degree evaluation method based on the battery degradation evaluation model specifically comprises the following steps:
S31, collecting charging data including voltage and capacitance in the battery charging process;
S32, screening the charging data to obtain effective charging data;
and S33, calculating the charge quantity of the battery based on the voltage and the capacitance in the effective charge data, and determining the aging degree of the battery based on the charge quantity.
Further, the effective charging data refers to charging data that the initial charge quantity is lower than 50%, the final charge quantity SOC is not lower than 90%, the charging current is not higher than 1.5C, and abrupt change does not exist in the constant-current charging current in the charging process.
The method for predicting the health of the battery cell has the following beneficial technical effects:
the platform data is utilized, the real-time data of the battery pack is screened by the monitoring platform regularly, the prediction effect is achieved through modeling, and the accident early warning is achieved, so that unpacking or factory return is avoided to a certain extent, the cost is not increased, and the stability of the single battery of the vehicle can be monitored. The problems that the internal data of the battery are difficult to find, the problem points are difficult to reproduce, the faults cannot be prevented and the like in the market are solved, the problem battery cells can be quickly found without unpacking, the detection time is long, and the complaints of customers are reduced.
Drawings
FIG. 1 is a flowchart of a method for predicting the health of a battery cell according to an embodiment of the present invention;
fig. 2 is a graph of simulated statistical results of abnormal cells based on a deviation fault prediction model according to an embodiment of the present invention.
Detailed Description
The following detailed description of the invention refers to the accompanying drawings, which illustrate preferred embodiments of the invention in further detail.
The vehicle data is uploaded to a national data platform through a T-BOX, the national data platform is monitored regularly by a background, the data comprising time, single voltage, single probe temperature, highest temperature, lowest temperature, total voltage, total current, mileage, SOC and the like are screened, and the single problem is analyzed through an entropy failure prediction model, a deviation failure prediction model and a battery degradation evaluation model, so that the health of a battery cell is predicted, and early warning of accidents is achieved.
Fig. 1 is a flowchart of a method for predicting the health of a battery cell according to an embodiment of the present invention, where the method specifically includes the following steps:
S1, preliminarily predicting whether a battery cell with poor consistency exists or not based on an entropy failure prediction model;
In the embodiment of the invention, the battery cell consistency prediction method based on the entropy failure prediction model specifically comprises the following steps:
S11, taking the monomer voltage in a time window as input data, wherein the value of the monomer voltage is between 2 and 5V, calculating the entropy value of each monomer cell under the time window, and forming a cell-entropy curve;
in the embodiment of the invention, the battery is formed by connecting a plurality of battery modules in series, wherein each battery module is formed by connecting a plurality of single battery cells in series, and the single battery is the voltage value of the battery cells.
S12, calculating the variation coefficient of each single cell, if the variation coefficient is larger than a coefficient threshold, primarily judging that the corresponding cell is poor in consistency, if the variation coefficient is smaller than the coefficient threshold, primarily judging that the corresponding cell is good in consistency, if all the cells are good in consistency, executing step S2 is not needed, and if the cells with poor consistency exist, executing step S2.
In the embodiment of the invention, the calculation formula of the variation coefficient of the single cell is specifically as follows:
Wherein E i represents the entropy of the ith monomer cell, E ave represents the average value of the entropy of all the monomer cells in the time window, and a i represents the standard deviation of the ith monomer cell.
S2, if the detection result is yes, determining the abnormal battery cell based on the deviation fault prediction model.
In the embodiment of the invention, the abnormal cell determination method based on the deviation fault prediction model specifically comprises the following steps:
s21, obtaining a single voltage in a set time period;
S22, judging whether the set time period is a valid time period, if yes, executing step S23, if no, executing step S21,
The effective period of time refers to: the time period comprises at least 2 charge data of 50% -80% of the electric quantity SOC interval, and the time period covers a time window for inputting the entropy value fault prediction model.
In the embodiment of the present invention, if the size of the time window is 1 month, for example, 2020.08.15 to 2020.09.15, the duration of the effective set time period is longer than the time length of the time window, and the effective set time period includes the time window, the set time period is 2020.07.15 to 2020.09.15, and at least 2 charge data of 50% -80% of the SOC interval of the electric quantity is required in the effective set time period, and the sub-time period in the following may be calculated in days or weeks.
S23, dividing the effective time period into a plurality of sub-time periods, calculating the voltage deviation sum of all the single battery cells in each sub-time period, and counting the abnormal frequency of the corresponding single battery cell once if the voltage deviation sum is larger than a deviation threshold value to obtain the abnormal frequency of each single battery cell;
In the embodiment of the invention, the voltage deviation and H i calculation formulas of the sub-time period single battery cell are as follows:
Wherein t 1 represents the start time of the sub-period, t 2 represents the end time of the sub-period, u i,t represents the cell voltage of each cell at time t, u o,t represents the total voltage of the battery at time t, and n represents the number of cells in the battery pack.
S24, identifying the single cell with the abnormal frequency value larger than the counting threshold as an abnormal cell, and fig. 2 is a simulated statistical result diagram of the abnormal cell based on a deviation fault prediction model;
in the embodiment of the present invention, after step S2, the method further includes:
And S3, evaluating the aging degree of the current battery based on the battery degradation evaluation model.
In the embodiment of the invention, the battery aging degree evaluation method based on the battery degradation evaluation model specifically comprises the following steps:
S31, collecting charging data including voltage and capacitance in the battery charging process;
S32, screening charging data to obtain effective charging data, wherein the effective charging data refers to charging data that the initial charge quantity is lower than 50%, the end charge quantity SOC is not lower than 90%, the charging current is not higher than 1.5C, and no abrupt change (fluctuation within 5A at most) exists in the constant-current charging current in the charging process.
And S33, calculating the charge quantity of the battery based on the voltage and the capacitance in the effective charge data, and determining the aging degree of the battery based on the charge quantity.
When the battery leaves the factory, a battery manufacturer can provide a mapping table of the charge quantity and the aging degree of the battery, the aging degree of the battery can be determined based on the charge point, and whether the battery pack is an early battery or an aging battery can be analyzed, so that certain avoidance is made in the production and operation processes.
The method for predicting the health of the battery cell has the following beneficial technical effects:
the platform data is utilized, the real-time data of the battery pack is screened by the monitoring platform regularly, the prediction effect is achieved through modeling, and the accident early warning is achieved, so that unpacking or factory return is avoided to a certain extent, the cost is not increased, and the stability of the single battery of the vehicle can be monitored. The problems that the internal data of the battery are difficult to find, the problem points are difficult to reproduce, the faults cannot be prevented and the like in the market are solved, the problem battery cells can be quickly found without unpacking, the detection time is long, and the complaints of customers are reduced.
It is obvious that the specific implementation of the present invention is not limited by the above-mentioned modes, and that it is within the scope of protection of the present invention only to adopt various insubstantial modifications made by the method conception and technical scheme of the present invention.
Claims (6)
1. A method for predicting the health of a battery cell, the method comprising the steps of:
S1, preliminarily predicting whether a battery cell with poor consistency exists or not based on an entropy failure prediction model;
s2, if the detection result is yes, determining an abnormal battery cell based on a deviation fault prediction model;
the battery cell consistency prediction method based on the entropy failure prediction model specifically comprises the following steps:
s11, taking the monomer voltage in a time window as input data, and calculating the entropy value of each monomer cell under the time window;
s12, calculating the variation coefficient of each single cell, and if the variation coefficient is larger than a coefficient threshold value, primarily judging the consistency difference of the corresponding cells;
The calculation formula of the variation coefficient of the single cell is specifically as follows:
Wherein E i represents the entropy of the ith monomer cell, E ave represents the average value of the entropy of all the monomer cells in the time window, and a i represents the standard deviation of the ith monomer cell.
2. The method for predicting the health of a battery cell according to claim 1, wherein the method for determining abnormal cells based on the deviation fault prediction model specifically comprises the steps of:
s21, obtaining a single voltage in a set time period;
S22, judging whether the set time period is a valid time period, if yes, executing step S23, if no, executing step S21,
S23, dividing the effective time period into a plurality of sub-time periods, calculating the voltage deviation sum of all the single battery cells in each sub-time period, and counting the abnormal frequency of the corresponding single battery cell once if the voltage deviation sum is larger than a deviation threshold value to obtain the abnormal frequency of each single battery cell;
S24, the single battery cell with the abnormal frequency value larger than the counting threshold value is regarded as the abnormal battery cell.
3. The method for predicting the health of a battery cell according to claim 2, wherein the voltage deviation and H i of the sub-period unit cell are calculated as follows:
Wherein t 1 represents the start time of the sub-period, t 2 represents the end time of the sub-period, u i,t represents the cell voltage of each cell at time t, u o,t represents the total voltage of the battery at time t, and n represents the number of cells in the battery pack.
4. The method for predicting the health of a battery cell of claim 1, further comprising, after step S2:
And S3, evaluating the aging degree of the current battery based on the battery degradation evaluation model.
5. The method for predicting the health of a battery cell according to claim 4, wherein the method for evaluating the aging degree of the battery based on the battery deterioration evaluation model comprises the steps of:
S31, collecting charging data including voltage and capacitance in the battery charging process;
S32, screening the charging data to obtain effective charging data;
and S33, calculating the charge quantity of the battery based on the voltage and the capacitance in the effective charge data, and determining the aging degree of the battery based on the charge quantity.
6. The method of claim 5, wherein the effective charging data is charging data in which the initial charge power is less than 50%, the final charge power SOC is not less than 90%, the charging current is not more than 1.5C, and no abrupt change exists in the constant current charging current during charging.
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| CN112926753A (en) * | 2021-02-10 | 2021-06-08 | 北汽鹏龙(沧州)新能源汽车服务股份有限公司 | Recovery method and device of power battery |
| CN113361128A (en) * | 2021-06-24 | 2021-09-07 | 东莞塔菲尔新能源科技有限公司 | Abnormal battery cell screening method and system, computer equipment and storage medium |
| CN113533979B (en) * | 2021-07-15 | 2022-09-23 | 合肥力高动力科技有限公司 | Method for judging abnormal battery cell of battery pack |
| CN114726042A (en) * | 2022-04-14 | 2022-07-08 | 广州奔想智能科技有限公司 | Early warning method for monitoring battery health by using charging curve and battery charging cabinet |
| CN115877230A (en) * | 2022-11-30 | 2023-03-31 | 上海玫克生储能科技有限公司 | Method, system, device and medium for determining fault of battery module |
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