CN113640683A - Method for identifying abnormal battery - Google Patents
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- 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]
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- 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]
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Abstract
The invention discloses a method for identifying an abnormal battery, which is suitable for a formation and capacity grading process link of the battery, and comprises the following steps: s1, judging the type of the battery to be detected; if the battery is a newly produced brand new battery, the step S2 is performed; if the recovered battery is used, the flow proceeds to step S3; s2, comparing values of a plurality of characteristic quantities of the battery to be tested with upper and lower threshold values of the characteristic quantities corresponding to the time periods, wherein if the value of at least one characteristic quantity is larger than the corresponding upper threshold value or smaller than the corresponding lower threshold value, the battery to be tested is an abnormal battery; s3, judging whether the battery to be tested is an abnormal battery or not through the neural network corresponding to the time period; the steps S2 and S3 are both performed when the battery to be tested is charged and discharged, and the method divides the charging and discharging process of the battery into a plurality of time periods with the same time interval. The method provided by the invention can identify the abnormal battery in time so as to avoid the situation that the abnormal battery swells and even fires are caused.
Description
Technical Field
The invention relates to the field of battery production and manufacturing, in particular to a method for identifying an abnormal battery.
Background
In the production process of the battery, the battery needs to be subjected to chemical composition and capacity grading, namely, the battery is subjected to specified charging and discharging steps according to a specified process so as to activate the positive and negative electrode substances in the battery and improve the charging and discharging performance, self-discharging performance, storage performance and other comprehensive performances of the battery. In the formation and grading link, if an abnormal battery which does not meet the standard exists, the battery is easy to bulge and even cause fire.
In the prior art, various protections (such as overvoltage protection, undervoltage protection, over-temperature protection, overcurrent protection and the like) are set to prevent the battery from bulging and even fire. Protection threshold values are preset for various types of protection, and when corresponding parameters of the battery exceed the set protection threshold values, the charging and discharging of the battery can be automatically suspended, and an alarm is prompted. However, since the protection threshold set for each type of protection is a fixed value, the protection threshold cannot be adjusted in different time periods in the charge and discharge link to identify an abnormal battery at the first time, and therefore, there may be a case where the protection action is delayed, and the time efficiency and safety thereof are low.
Disclosure of Invention
In order to solve the problem that the abnormal battery is easy to cause battery swelling and even fire in the formation and capacity division link, the invention aims to provide a method for identifying the abnormal battery, which can identify the abnormal battery in time so as to avoid the occurrence of subsequent conditions.
In order to achieve the above purpose, the invention provides the following technical scheme: a method for identifying abnormal batteries, which is suitable for a formation and capacity grading process link of the batteries, comprises the following steps:
s1, judging the type of the battery to be detected; if the battery is the first type battery, the step S2 is proceeded to; if the battery is the second type battery, the process proceeds to step S3; wherein, the first battery is a newly produced battery with the same manufacturing process, and the second battery is a used recovered battery;
s2, comparing values of a plurality of characteristic quantities of the battery to be tested with upper and lower thresholds of the plurality of characteristic quantities corresponding to the time periods respectively, and if the value of at least one characteristic quantity is larger than the corresponding upper threshold or smaller than the corresponding lower threshold, determining that the battery to be tested is an abnormal battery;
s3, judging whether the battery to be detected is an abnormal battery or not through the neural network corresponding to the time period;
the steps S2 and S3 are both performed when the battery to be tested is charged and discharged, and the method divides the charging and discharging process of the battery into a plurality of time periods with the same time interval.
In the above technical solution, preferably, the characteristic quantity includes at least one of a voltage, a current change rate, and a temperature of the battery to be measured.
In the above technical solution, preferably, the time interval is not greater than 10 minutes, and each of the time periods measures at least once a plurality of characteristic quantities of the battery to be tested and performs at least one of step S2 or step S3.
In the above technical solution, preferably, the step S1 is determined according to the production process and the used time of the battery.
In the above technical solution, preferably, the specific steps of generating the upper threshold and the lower threshold are as follows:
s21, collecting the characteristic quantity values of a plurality of brand-new batteries with the same type in each time period in the charging and discharging process;
s22, generating a characteristic curve based on the collected values;
s23, removing abnormal curves from the characteristic curves;
s24, generating an upper limit curve and a lower limit curve based on the residual characteristic curves;
the abscissa of the characteristic curve, the abscissa of the upper limit curve and the ordinate of the lower limit curve are time, the value of the upper limit curve in a time period is the upper threshold of the characteristic quantity corresponding to the upper limit curve in the time period, and the value of the lower limit curve in a time period is the lower threshold of the characteristic quantity corresponding to the lower limit curve in the time period. Still further preferably, the method for rejecting the abnormal curve in step S23 is as follows: and performing Grabbs criterion test on the values of the characteristic curves in each time period, and if an abnormal value is identified by the test, rejecting the characteristic curve to which the abnormal value belongs.
In the above technical solution, preferably, each of the time periods has a corresponding neural network.
In the above technical solution, preferably, the neural network is a hidden layer neural network.
In the above preferred embodiment, it is further preferred that the input layer of the neural network has 4 characteristic quantities, and the 4 characteristic quantities respectively correspond to the voltage X of the battery to be measured1Current X2Current change rate X3And temperature X4. Still further preferably, the first hidden layer perception formula of the neural network is:
the second hidden layer perception formula is:
the output layer perception formula is:
wherein each character is defined as follows:
first hiddenThe value of the ith sensor in the hidden layer, G, the excitation function,
Xjthe weight to the ith perceptron in the first hidden layer,
the offset value of the ith sensor in the first hidden layer,
the value of the ith sensor in the second hidden layer,
the weight to the ith sensor of the second hidden layer,
the bias value of the ith sensor in the second hidden layer,
the value of the ith sensor in the output layer,
the weight to the ith sensor of the output layer,
the offset value of the ith sensor in the layer is input. Still further preferably, the excitation function G is a hyperbolic tangent function, and the formula thereof is:
in the above-mentioned preferred embodiment, it is further preferred that the number of the sensors in the output layer is 1, and the output result is used to indicate that the battery to be tested is a normal battery or an abnormal battery.
In the above technical solution, it is further preferable that the method further includes the steps of:
and S4, if the battery to be tested is an abnormal battery, stopping charging and discharging the battery to be tested and giving an alarm.
Compared with the prior art, the method provided by the invention can produce the following beneficial effects: 1. the invention can monitor the battery in real time, and identify the abnormal battery before the battery bulges or even before a fire disaster according to the characteristics of the characteristic quantity of the battery in each time period, thereby greatly improving the timeliness of identifying the abnormal battery and reducing the risk of battery production. 2. After the abnormal battery is identified, the charging and discharging operation of the battery is independently stopped, the production of other normal batteries is not influenced, and the production efficiency of the battery is improved. 3. The early stop of the charge and discharge operation of the abnormal battery can save electric energy, particularly when charging and discharging a recovered battery with a large number of abnormal batteries.
Drawings
FIG. 1 is a flow chart of a method for identifying an abnormal battery according to the present invention;
FIG. 2 is a flowchart of a method for obtaining an upper threshold and a lower threshold provided by the present invention;
FIG. 3 is an exemplary illustration of the present invention;
fig. 4 is a schematic diagram of a neural network provided by the present invention.
Detailed Description
To explain technical contents, structural features, achieved objects and effects of the invention in detail, the technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only a part of the embodiments of the present application, and not all embodiments. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a detailed description of various exemplary embodiments or implementations of the invention. However, various exemplary embodiments may be practiced without these specific details or with one or more equivalent arrangements. Moreover, the various exemplary embodiments may be different, but are not necessarily exclusive. For example, the particular shapes, configurations and characteristics of the exemplary embodiments may be used or implemented in another exemplary embodiment without departing from the inventive concept.
The term "characteristic quantity" herein refers to a quantity capable of reacting into the physical and chemical characteristics of the battery in the partial capacity stage, such as: voltage, current, capacity, rate of change of voltage, temperature, etc.
Fig. 1 illustrates a method for identifying an abnormal battery provided by the present invention, which is suitable for identifying an abnormal battery in the chemical composition and capacity stage of battery production to prevent the abnormal battery from swelling and even fire during the charging and discharging process. The method provided by the invention comprises the following steps:
s1, judging the battery type to be tested; if the battery is the first type battery, the step S2 is proceeded to; if the battery is the second type battery, the process proceeds to step S3.
In step S1, the judgment is based on the production process and the used time of the battery. The newly produced batteries with the same manufacturing process (which can be batteries with the same batch or a plurality of batches and the same specification manufactured based on the same process method) have stable physical performance, and the fitting degree of the characteristic quantity curve of the batteries in the charging and discharging process is high, so that the abnormal batteries can be identified through the characteristic quantity curve of the batteries in the charging and discharging process. Meanwhile, the number of abnormal batteries in the newly produced batteries is small, and sufficient data cannot be provided to support the accuracy of the neural network, so that it is not suitable for identifying abnormal batteries using the neural network. And the method that the difference of the physical properties of the recycled batteries is large, namely the consistency of the physical properties is poor, the fitting degree of characteristic quantity curves of the batteries of the same type in the charging and discharging processes is low, and the method is not suitable for judging the characteristic quantity curves is used. Meanwhile, the number of abnormal batteries in the used recycled batteries is large, enough sample data can be provided for the neural network to learn, and therefore the abnormal batteries in the used recycled batteries can be identified through the neural network. In actual implementation, the step S1 may be performed manually or by measuring the power and capacity of the battery to be measured.
In this example, the first type of battery is a newly produced battery produced by the same manufacturing process, and the second type of battery is a used recovered battery.
S2, comparing the values of the characteristic quantities of the battery to be detected with the upper threshold and the lower threshold corresponding to the characteristic quantities in the time periods, and if the value of at least one characteristic quantity is larger than the corresponding upper threshold or smaller than the corresponding lower threshold, determining that the battery is abnormal.
In the method, the charging and discharging process of the battery is divided into a plurality of time periods at the same time interval, each time interval is not more than 10 minutes, each time period at least measures the value of the characteristic quantity once and judges to execute the step S2 once at least. In practical implementation, the smaller the time interval, the more the time periods, the more the number of times step S2 is executed per time period, the closer the acquired data is to the real situation, the higher the timeliness and sensitivity of identifying an abnormal battery.
The characteristic quantities include voltage, current, temperature, power, and the like. In actual implementation, the number of the sampled characteristic quantities can be set according to the performance of the server, and the larger the number of the characteristic quantities, the more the generated characteristic curves are, and the higher the timeliness and the sensitivity of identifying the abnormal battery are.
As shown in fig. 2, the steps of generating the upper threshold and the lower threshold corresponding to the feature quantity are as follows:
and S21, collecting the characteristic quantity values of a plurality of batteries of the same type in each time period in the charging and discharging process.
And S22, generating a characteristic curve based on the collected values.
And S23, removing abnormal curves from the generated characteristic curves.
The abnormal curve is a characteristic curve generated by the abnormal battery and shows that the abnormal curve has a large deviation from most of the characteristic curves. The method for eliminating the abnormal curve comprises the following steps: and carrying out Grabbs criterion test on the values of the characteristic curves in each time period, and if an abnormal value is identified by the test, rejecting the characteristic curve to which the abnormal value belongs. In other embodiments, rejecting outlier curves can also be tested using the Lauda criterion or rejected manually.
S24, an upper limit curve and a lower limit curve of the characteristic amount are generated based on the remaining characteristic curves.
The upper limit curve is formed by adding a positive offset value to the highest value of each of the remaining characteristic curves in each time period, and similarly, the lower limit curve is formed by adding a negative offset value to the lowest value of each of the remaining characteristic curves in each time point. The offset values of the upper limit curve and the lower limit curve can be manually set and adjusted to adapt to batteries of different manufacturers, different standards or different types.
Fig. 3 shows an example of the practical application of step S2, in which the selected characteristic quantity is the battery voltage. As can be seen from the figure, the voltage value of the abnormal cell exceeds the upper threshold corresponding to the time period at the 47 th minute, and it is shown in the figure that the voltage curve of the abnormal cell crosses the upper limit curve of the voltage at the 47 th minute, so that the abnormal cell can be identified at the 47 th minute, in contrast to the abnormal cell in fig. 3 which requires about 210 minutes to be identified by the method of fixing the protection threshold. Therefore, the method can greatly improve the timeliness and effectiveness of identifying the abnormal battery so as to avoid the subsequent battery swelling and even fire hazard.
And S3, judging whether the battery to be detected is an abnormal battery or not through the neural network corresponding to the current time period.
A large number of samples are collected when the abnormal battery is judged through the neural network, misjudgment is reduced in the process of training the samples, and the accuracy of judgment of the neural network is improved. In practical implementation, when the misjudgment rate of the trained function is less than 5%, the trained function is used as a neural network for judging the abnormal battery. The output result of the neural network is used for indicating that the tested battery is a normal battery or an abnormal battery.
FIG. 4 is a schematic diagram of a neural network provided in the present invention, which is a dual hidden layer neural network. The input layer of the neural network inputs the characteristic quantity of the battery to be detected, the number of the input characteristic quantity is not too large for preventing over-fitting, and the input characteristic quantity has better effect when the input characteristic quantity is four of voltage, current change rate and temperature through comparison. Each time period is provided with a corresponding neural network, and the corresponding neural network is formed by training the value of the characteristic quantity in the time period.
The voltage, the current change rate and the temperature are respectively X1、X2、X3And X4The formula for each sensor at each layer can be written:
a first layer:
a second layer:
an output layer:
in this example, a hyperbolic tangent function is used as the excitation function, and the function formula is as follows:
the individual characters are defined as follows:
the value of the ith sensor in the first hidden layer, G, the excitation function,
Xjthe weight to the ith perceptron in the first hidden layer,
the offset value of the ith sensor in the first hidden layer,
the value of the ith sensor in the second hidden layer,
the weight to the ith sensor of the second hidden layer,
the bias value of the ith sensor in the second hidden layer,
the value of the ith sensor in the output layer,
the weight to the ith sensor of the output layer,
the offset value of the ith sensor in the layer is input. The number of the sensors of the output layer is 1, and the sensors are used for indicating that the detected battery is a normal battery or an abnormal battery.
After practical test, the number of the first layer of sensors is not less than 5, and the number of the second layer of sensors is not less than 3, so that the effect is better.
And S4, if the battery to be tested is an abnormal battery, stopping charging and discharging the battery.
The charging and discharging process of the abnormal battery is interrupted, and the battery is prevented from swelling and even causing fire. During this time, the remaining normal cells continue the charging and discharging process, thereby allowing uninterrupted production of the cells. Therefore, the electricity consumption can be saved by stopping charging and discharging the abnormal battery, and the production cost of the battery is further reduced.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the foregoing description only for the purpose of illustrating the principles of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the invention as defined by the appended claims, specification, and equivalents thereof.
Claims (13)
1. A method for identifying abnormal batteries is suitable for a formation and capacity grading process link of battery production, and is characterized by comprising the following steps:
s1, judging the type of the battery to be detected; if the battery is the first type battery, the step S2 is proceeded to; if the battery is the second type battery, the process proceeds to step S3; wherein, the first battery is a newly produced battery with the same manufacturing process, and the second battery is a used recovered battery;
s2, comparing values of a plurality of characteristic quantities of the battery to be tested with upper and lower thresholds of the plurality of characteristic quantities corresponding to the time periods respectively, and if the value of at least one characteristic quantity is larger than the corresponding upper threshold or smaller than the corresponding lower threshold, determining that the battery to be tested is an abnormal battery;
s3, judging whether the battery to be detected is an abnormal battery or not through the neural network corresponding to the time period;
the steps S2 and S3 are both performed when the battery to be tested is charged and discharged, and the method divides the charging and discharging process of the battery into a plurality of time periods with the same time interval.
2. The method of claim 1, wherein the characteristic quantity comprises at least one of a voltage, a current change rate and a temperature of the battery under test.
3. The method of claim 1, wherein said time interval is not greater than 10 minutes, and said steps of measuring said characteristic quantities of said battery under test at least once and performing said step S2 or said step S3 at least once for each of said time periods.
4. The method of claim 1, wherein the step S1 is based on the manufacturing process and the used time of the battery.
5. The method according to claim 1, wherein the specific steps of generating the upper threshold and the lower threshold are as follows:
s21, collecting the characteristic quantity values of a plurality of brand-new batteries with the same type in each time period in the charging and discharging process;
s22, generating a corresponding characteristic curve based on the collected values;
s23, removing abnormal curves from the characteristic curves;
s24, generating an upper limit curve and a lower limit curve based on the residual characteristic curves;
the abscissa of the characteristic curve, the abscissa of the upper limit curve and the ordinate of the lower limit curve are time, the value of the upper limit curve in a time period is the upper threshold of the characteristic quantity corresponding to the upper limit curve in the time period, and the value of the lower limit curve in a time period is the lower threshold of the characteristic quantity corresponding to the lower limit curve in the time period.
6. The method according to claim 5, wherein the abnormal curve is eliminated in step S23 as follows: and performing Grabbs criterion test on the values of the characteristic curves in each time period, and if an abnormal value is identified by the test, rejecting the characteristic curve to which the abnormal value belongs.
7. The method of claim 1, wherein each of said time periods has a corresponding one of said neural networks.
8. The method of claim 1, wherein the neural network is a hidden layer neural network.
9. The method of claim 8, wherein the spiritThe input layer of the network has 4 characteristic quantities, and the 4 characteristic quantities respectively correspond to the voltage X of the battery to be detected1Current X2Current change rate X3And temperature X4。
10. The method of claim 9, wherein the first layer perception formula of the neural network is:
the second layer perception formula is:
the output layer perception formula is:
wherein each character is defined as follows:
the value of the ith sensor in the first hidden layer, G, the excitation function,
Xjthe weight to the ith perceptron in the first hidden layer,
the offset value of the ith sensor in the first hidden layer,
the value of the ith sensor in the second hidden layer,
the weight to the ith sensor of the second hidden layer,
the bias value of the ith sensor in the second hidden layer,
the value of the ith sensor in the output layer,
the weight to the ith sensor of the output layer,
the offset value of the ith sensor in the layer is input.
11. The method according to claim 10, wherein the excitation function G is a hyperbolic tangent function, which is expressed by the formula:
12. the method according to claim 8, wherein the number of sensors in the output layer is 1, and the output result is used for indicating that the battery to be tested is a normal battery or an abnormal battery.
13. The method of claim 1, further comprising the steps of:
and S4, if the battery to be tested is an abnormal battery, stopping charging and discharging the battery to be tested and giving an alarm.
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| CN111751756A (en) * | 2020-07-31 | 2020-10-09 | 中国汽车工程研究院股份有限公司 | Method for identifying fault battery monomer by using accumulated power battery temperature curve |
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