CN112415402A - Method and system for lithium battery capacity estimation and battery core abnormity prediction - Google Patents
Method and system for lithium battery capacity estimation and battery core abnormity prediction Download PDFInfo
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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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- G—PHYSICS
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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]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
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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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
- G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/44—Methods for charging or discharging
- H01M10/446—Initial charging measures
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- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Abstract
The application relates to a method and a system for lithium battery capacity estimation and battery core abnormity prediction, wherein the method for lithium battery capacity estimation comprises the following steps: acquiring historical sample data; calculating a first dynamic weight ratio corresponding to the first capacity estimation value according to the first sample standard deviation and the first actual deviation value; calculating the first dynamic weight ratio, the first capacity estimation value and the first capacity test value to obtain a second capacity estimation value; updating the first capacity measurement value and the first capacity estimation value into a second capacity measurement value and a second capacity estimation value; and repeating the steps to predict the total capacity estimation value of the lithium battery. And carrying out weight calculation through the deviation between the last predicted capacity and the measured capacity and the standard deviation of the sample to obtain an optimal expected evaluation weight ratio, thereby providing a prediction formula of the next capacity. By the formula, samples are continuously expanded, the final capacity of the whole partial capacity can be predicted according to the discharge capacity in a short period of time, and the battery capacity precision is improved.
Description
Technical Field
The application relates to the field of lithium battery formation and capacity grading, in particular to a method and a system for lithium battery capacity estimation and battery core abnormity prediction.
Background
The capacity is an important index for measuring the performance of the lithium battery, and the capacity attenuation degree of the lithium battery represents the service life of the lithium battery, so the capacity of the lithium battery needs to be estimated.
At present, most of capacity estimation of the lithium battery of the automobile is designed based on a Kalman filter. For example, CN110320473A proposes "a method for estimating the capacity of an automotive lithium battery based on kalman filtering and fuzzy logic" to perform estimation and correction by using a kalman filter. The Kalman filtering considers that the variables (in this example, the estimated voltage, the estimated capacity, the actual voltage and the actual capacity) have errors in the environment, a better predicted voltage is calculated by utilizing the normal distribution of the estimated voltage and the actual voltage, and the predicted voltage is more in line with the normal distribution with smaller variance (sigma). At the same time, if kalman filtering is used, the covariance between the optimal prediction voltage and the optimal prediction capacity is also taken into account, since this is considered to be external noise in kalman and has an influence on the prediction.
However, it is somewhat superfluous in the prior art to use a kalman filter to estimate the capacity of the lithium battery. On the first hand, as the precision of the detection equipment is improved, the current precision reaches one ten thousandth, so the actual voltage tested by the detection equipment can be considered to be accurate, and the Kalman considers more that in an open and changeable environment, any test has errors and larger uncertainty, so the current technology and the cognition of the Kalman are different. In a second aspect, the capacity of a lithium battery is calculated using ampere-hour integration, and only time and current are associated with the capacity. The capacity of the chemical composition capacity field is tested under a constant current condition, the rest time is stable, and under the condition that the time and the current are constant, a Kalman filter is not needed to be used for estimating the capacity.
In addition, lithium batteries need to be activated during production before they can be used, and the first activation process is called formation. Cell test abnormity may exist in the lithium battery formation process, such as a safety accident caused by cell short circuit or test equipment problem; and the battery cell of the lithium battery has no abnormity in a short time of charging in a reverse connection mode of positive and negative polarities, so that factory staff cannot find the battery cell in time, and the battery cell of the lithium battery has serious safety accidents such as bulge and fire and the like along with the passage of time.
In summary, no effective solution is currently available for the above problems of capacity estimation and battery core abnormality prediction of the current automobile lithium battery.
Disclosure of Invention
The embodiment of the application provides a method and a system for lithium battery capacity estimation and battery core abnormity prediction, and aims to at least solve the problems of battery capacity precision and battery core abnormity detection in the related technology.
In a first aspect, an embodiment of the present application provides a method for estimating a capacity of a lithium battery, where the method includes: constructing a capacitance prediction model, comprising the following steps:
s101, obtaining historical sample data, wherein the historical sample data comprises a first capacity estimation value, a first capacity test value, a second capacity test value, a first sample standard deviation obtained through calculation according to the first capacity estimation value and the first capacity test value, and a first actual deviation value, wherein the first capacity estimation value samples the capacity;
s102, calculating according to the first sample standard deviation and the first actual deviation value to obtain a first dynamic weight ratio corresponding to the first capacity estimation value;
s103, calculating the first dynamic weight ratio, the first capacity estimation value and the first capacity test value to obtain a second capacity estimation value;
s104, updating the first capacity measured value and the first capacity estimated value into a second capacity measured value and a second capacity estimated value;
and S105, repeating the steps from S101 to S104, and predicting the total capacity estimation value of the lithium battery.
In a second aspect, an embodiment of the present application provides a method for predicting a cell anomaly, including the following steps:
s201, collecting charging data sections of the lithium battery under positive connection and negative connection, wherein each charging data section comprises charging starting time, charging ending time, charging time and current data of each charging time;
s202, acquiring voltage data corresponding to each current data, and drawing a forward connection curve and a backward connection curve by taking the current data and the voltage data as coordinate axes;
s203, grading the capacity of the lithium battery, constructing a voltage and current data model by the method for constructing the capacity prediction model, and predicting a charging and discharging curve in the next period of time;
and S204, judging the consistency of the charging and discharging curve and the positive connection curve, and if the charging and discharging curve and the positive connection curve are not similar, judging that the battery cell test is abnormal.
In a third aspect, an embodiment of the present application provides a system for estimating capacity of a lithium battery, including
The acquisition module is used for acquiring historical sample data;
the calculation module is used for calculating a first dynamic weight ratio corresponding to the first capacity estimation value according to the first sample standard deviation and the first actual deviation value, and calculating the first dynamic weight ratio, the first capacity estimation value and the first capacity test value to obtain a second capacity estimation value;
and the estimation module is used for updating the first capacity measured value and the first capacity estimation value into a second capacity measured value and a second capacity estimation value, repeating S101 to S104, and predicting the total capacity estimation value of the lithium battery.
In a fourth aspect, an embodiment of the present application provides a system for predicting battery core abnormality, including:
the acquisition module is used for acquiring charging data sections of the lithium battery under positive connection and negative connection, wherein each charging data section comprises charging starting time, charging ending time, charging time and current data of each charging time, voltage data corresponding to each current data is acquired, and a positive connection curve and a negative connection curve are drawn by taking the current data and the voltage data as coordinate axes;
the prediction module is used for grading the capacity of the lithium battery, constructing a voltage and current data model by the method for constructing the capacity prediction model and predicting a charging and discharging curve in the next period of time;
and the judging module is used for judging the consistency of the charging and discharging curve and the positive connection curve, and if the charging and discharging curve and the positive connection curve are not similar, the battery cell test is judged to be abnormal.
In a fifth aspect, the present application proposes a storage medium having a computer program stored therein, wherein the computer program is configured to, when executed, perform the method for estimating the capacity of a lithium battery according to the first aspect and/or the method for predicting a cell abnormality according to the second aspect.
Compared with the related art, the method for estimating the capacity of the lithium battery provided by the embodiment of the application carries out weight calculation through the deviation between the last predicted capacity and the last measured capacity and the standard deviation of the sample to obtain an optimal expected evaluation weight ratio, so that a prediction formula of the capacity of the next step is given. By the formula, samples are continuously expanded, the final capacity of the whole partial capacity can be predicted according to the discharge capacity in a short period of time, errors of lithium battery capacity estimation can be reduced, and the battery capacity precision is improved.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a flow chart of a method of lithium battery capacity estimation according to an embodiment of the application;
fig. 2 is a flowchart of a method for predicting cell anomaly according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a first method of calculating a dynamic weight ratio according to an embodiment of the present application;
fig. 4 is a charge-discharge curve diagram of a normal cell according to an embodiment of the present application;
FIG. 5 is a block diagram of a system for lithium battery capacity estimation according to an embodiment of the present application;
fig. 6 is a block diagram of a system for predicting cell anomaly according to an embodiment of the present application;
FIG. 7 is a diagram of a hardware configuration of an electronic device according to an embodiment of the present application;
fig. 8 is a positive connection and negative connection curve diagram of a battery cell according to an embodiment of the application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with one or more embodiments of the present specification. Rather, they are merely examples of apparatus and methods consistent with certain aspects of one or more embodiments of the specification, as detailed in the claims which follow.
It should be noted that: in other embodiments, the steps of the corresponding methods are not necessarily performed in the order shown and described herein. In some other embodiments, the method may include more or fewer steps than those described herein. Moreover, a single step described in this specification may be broken down into multiple steps for description in other embodiments; multiple steps described in this specification may be combined into a single step in other embodiments.
The present embodiment provides a method for estimating a capacity of a lithium battery, and fig. 1 is a flowchart of a method for estimating a capacity of a lithium battery according to an embodiment of the present application, as shown in fig. 1, the method including:
s101, obtaining historical sample data, wherein the historical sample data comprises a first capacity estimation value, a first capacity test value, a second capacity test value, a first sample standard deviation obtained through calculation according to the first capacity estimation value and the first capacity test value, and a first actual deviation value, wherein the first capacity estimation value samples the capacity;
s102, calculating according to the first sample standard deviation and the first actual deviation value to obtain a first dynamic weight ratio corresponding to the first capacity estimation value;
s103, calculating the first dynamic weight ratio, the first capacity estimation value and the first capacity test value to obtain a second capacity estimation value;
s104, updating the first capacity measured value and the first capacity estimated value into a second capacity measured value and a second capacity estimated value;
and S105, repeating the steps from S101 to S104, and predicting the total capacity estimation value of the lithium battery.
In S101, the collected original data is stored in a historical sample database, and the historical sample data may be extracted from the sample database.
Specifically, before S101, the method further includes: collecting at least two charging data sections of a lithium battery in a formation and grading process; each charging data segment comprises charging starting time, charging ending time, charging time and current data of each charging time; acquiring a first capacity measured value and a second measured value within a period of time corresponding to each charging data section through ampere-hour integration, and calculating to obtain a first mean value capacity and a first sample standard deviation; storing the first capacity measurement, the second capacity measurement, the first mean capacity, and the first sample standard deviation in a historical sample database.
Illustratively, the present application collects current data by, for example, a current detection device, and acquires a charging start time and a charging end time by a timing device. In the related art, a constant voltage source and a constant current source are arranged on the component capacitance equipment through an MOS (metal oxide semiconductor) tube, the voltage, the current, the capacity and the time are important data which need to be acquired by charging a test battery core through an inverter power supply or reversely discharging the battery core to the inverter power supply, and the mode of acquiring the current, the voltage and the time is a conventional technical means in the industry and is not introduced much here. The above-described detection device can reach 0.1mA in accuracy, so the actual voltage tested can be considered accurate.
In S102, a weight calculation is performed by using the deviation between the last predicted capacity and the measured capacity and the standard deviation of the sample, so as to obtain an optimal expected evaluation weight ratio. Specifically, the first dynamic weight ratio is calculated by an optimal mean square error ratio, wherein,
the first dynamic weight ratio is expressed as: kf = dist ^ 2/(sstd ^2 + dist ^ 2)
kf is the first dynamic weight ratio, sstd is the first sample standard deviation, dist is the first actual deviation value.
In this embodiment, referring to fig. 3, the nth weight is assigned to the nth measured capacity and the nth sample capacity, so as to obtain the (n + 1) th estimated capacity, and by analogy, the current weight is continuously updated to estimate the next weight value. The method comprises the steps that a historical sample value is taken as a 1 st capacity estimation value, historical sample data of each point to be tested can be automatically stored when equipment is subjected to component capacity changing, the historical sample data are stored in an internal flash chip, and the historical sample data are actually used for capacity prediction of each point to be tested. In the scheme, the weight depends on sstd and dist, the smaller the standard deviation of the sample is, the larger the capacity weight ratio of the system to the sample is; on the contrary, the system takes the actually sampled capacity-weight ratio to be larger, that is, the weight is dynamically obtained through the similarity between the estimated value and the actual value in the historical data, if the estimated value is accurate, the estimated weight ratio is larger, and if the estimated value is inaccurate (the sample standard deviation is large), the actually sampled weight ratio is larger. The first dynamic weight ratio is updated according to the optimal mean square error ratio, so that the estimation result is higher in precision and is not easy to deviate, and the actual estimation result is more in line with the real capacity condition of the lithium battery.
In S103, the first dynamic weight value calculated in S102 is assigned to the first capacity estimation value, and the second capacity estimation value is expressed as: x (k | k) = (1-kf) × (k-1 | k-1) + kf × y (k-1)
Where X (k | k) is the second capacity estimate, X (k-1 | k-1) is the first capacity estimate, y (k-1) is the first capacity measure, and k is the current prediction time instant.
In S105, a first weight value and a first capacity estimation value are obtained through continuous iteration, and a total capacity estimation value of the lithium battery is finally predicted. Specifically, in the field of formation and partial capacitance, the device has a large amount of sample capacity data, a stable and closed test environment and high-precision test equipment, so that measurement errors and behavior change errors can be almost ignored, a Kalman filter is unnecessary to select, the fixed weight is inherently simple to calculate, the implementation is convenient, the estimated result precision is low, the deviation is easy to occur, and the real condition of the capacity of the lithium battery is difficult to meet. According to the method, the dynamic weight is selected to replace the fixed weight, the next iteration is an optimal weight of the sample capacity counted for a plurality of times in the early period and the actual capacity of the test, and the prediction model is closer to the true value more and more when the capacity is estimated through the optimal weight. The estimation method can be used in the existing high-precision test equipment, and not only is the calculation simple, but also the estimation result has high precision.
Through the steps 101 to 105, the invention provides a method for estimating the capacity of a lithium battery, which can solve the problem that the accuracy of an estimation result cannot be improved and the calculation is simple and convenient in the related art. Specifically, in the scheme, the weight calculation is carried out through the deviation between the last predicted capacity and the measured capacity and the standard deviation of the sample to obtain an optimal expected estimation weight ratio, so that the next predicted capacity is given, each measured value, estimated value and weight are placed in a historical sample database, referring to table 1, the error of battery capacity estimation is composed of data of the measured capacity, the sample capacity and the estimated capacity of 900 groups of battery cells, 5 groups of data are intercepted in the table 1, as can be seen from the table 1, after multiple iterations of the sample, the error of the estimated capacity is reduced to 0.01%, and in the field of lithium battery capacity estimation, as the number of samples is enough, and the measurement error between the measured capacity and the sample capacity is small, after the multiple iterations, the estimated capacity is closer to the actual value.
TABLE 1
| Measured capacity (mAh) | Sample Capacity (mAh) | Predicted capacity (mAh) | Error of the measurement |
| 3.9 | 4 | 3.9 | 2.50% |
| 66.6 | 67 | 66.8 | 0.29% |
| 199 | 200 | 199.4 | 0.3% |
| 1040.1 | 1041 | 1040.5 | 0.04% |
| 2996.4 | 2997 | 2996.7 | 0.01% |
Based on the same technical concept, fig. 2 exemplarily shows a method for predicting a cell anomaly according to an embodiment of the present invention, where the method for constructing a capacitance prediction model as described above is adopted in a step of predicting a charging and discharging curve in a next period of time, and specifically includes the following steps:
s201, collecting charging data sections of the lithium battery under positive connection and negative connection, wherein each charging data section comprises charging starting time, charging ending time, charging time and current data of each charging time;
s202, acquiring voltage data corresponding to each current data, and drawing a forward connection curve and a backward connection curve by taking the current data and the voltage data as coordinate axes;
s203, grading the capacity of the lithium battery, constructing a voltage and current data model by the method for constructing the capacity prediction model, and predicting a charging and discharging curve in the next period of time;
and S204, judging the consistency of the charging and discharging curve and the positive connection curve, and if the charging and discharging curve and the positive connection curve are not similar, judging that the battery cell test is abnormal.
For example, fig. 4 shows a graph of normal charge and discharge of a battery cell, fig. 8 shows a graph of positive and negative connection of a battery cell of a lithium battery, in fig. 8, a positive connection curve and a negative connection curve are drawn through a large number of abnormal and normal samples in component capacitance, and it can be clearly seen through comparison of the curves that rising slopes of the lithium battery under normal and abnormal conditions are obviously different, wherein a voltage curve of the lithium battery under normal conditions is in an upward trend along with time, and a voltage curve under reverse connection conditions has small-range fluctuation along with time. In addition, the similarity comparison between the sample curve and the prediction curve can be performed, specifically, the forward or backward connection curve and the prediction curve are divided into linear curves of a plurality of time slices, the voltage rising slope of each time slice is calculated and compared, and then the slope difference of each segment is accumulated to obtain the similarity of the two curves.
xiIs the slope of the rise of the voltage in the i period of the sample curve, yiThe voltage rising slope of the actually measured curve in the i time period is obtained; cos (θ) is the degree of similarity, and the calculated value is more similar as it is closer to 1, and is less similar otherwise.
After S204, the method further comprises: and judging the consistency of the charging and discharging curve and the reverse connection curve, calculating the reverse connection probability of the battery cell, judging the reverse connection of the battery cell if the reverse connection probability exceeds a threshold value, cutting off a charging power supply, and closing a channel. In this step, the threshold is a value obtained through multiple tests to determine whether the battery cell is abnormal, and may be adjusted according to actual conditions, such as the type of the lithium battery.
Through the foregoing S201 to S204, the present embodiment provides a method for predicting a cell anomaly, which is used to estimate whether a cell is internally short-circuited and reversely connected. In the prior art, since there is no hardware device that can detect whether there is an internal short circuit in a produced battery cell, or a user neglects to cause a reverse connection of a battery, and these abnormalities may bring about a serious consequence of a bulge combustion of the battery, the battery formation reverse polarity and the internal short circuit abnormality are pain points inside the formation field of the lithium battery. Specifically, slope deviation d (Xn-Xn-1) of two adjacent times is calculated through differentiation, and integral calculation is used for calculating integral; obtaining the most appropriate coefficient kf, kd, b value through multiple experiments, and finally calculating the occurrence probability f = kf ^ integral multiple n + kd ^ integral multiple n + b; wherein kf represents the voltage accumulation value (integral) of a plurality of time segments of the curve, kd represents the voltage deviation value (differential) of an adjacent time segment, and kf and kd coefficients are adjusted according to production practical conditions, such as: if the voltage overall change of the battery cell charging is sensitive, the overall change trend of the voltage of the battery cell in the formation and grading process is more emphasized, the specific gravity of the kf coefficient is increased, and if the voltage instantaneous change of the battery cell charging is sensitive, the voltage sudden change of the battery cell in the formation and grading process is more emphasized, and the specific gravity of the kd coefficient is increased.
In the scheme, the abnormity prediction is judged by analyzing the similarity of voltage curves of a plurality of battery cells (whole cabinet) in the same time period, wherein most of the battery cell voltage curves are normal, and the voltage curves of a few battery cells are obviously different from others; at this time, the abnormal voltage curve and the normal voltage curve are greatly different, and whether the abnormal voltage curve is abnormal or not is judged by calculating the similarity of the voltage curves of the abnormal battery cells. In order to improve the accuracy of judgment and reduce erroneous judgment, the most reasonable coefficients kf, kd and b are obtained from multiple experiments by taking the formula f = kf × xn + kd × dn + b into consideration of the voltage integral and the voltage differential of the curve.
Based on the same technical concept, fig. 5 exemplarily shows a lithium battery capacity estimation system provided by an embodiment of the present invention, which includes:
an obtaining module 401, configured to obtain historical sample data;
a calculating module 402, configured to calculate a first dynamic weight ratio corresponding to the first capacity estimation value according to the first sample standard deviation and the first actual deviation value, and calculate the first dynamic weight ratio, the first capacity estimation value, and the first capacity test value to obtain a second capacity estimation value;
and an estimating module 403, configured to update the first capacity measurement value and the first capacity estimation value to a second capacity measurement value and a second capacity estimation value, repeat S101 to S104, and predict a total capacity estimation value of the lithium battery.
Fig. 6 exemplarily shows a system for predicting a cell anomaly according to an embodiment of the present invention, including:
the acquisition module 501 is configured to acquire charging data segments of the lithium battery under positive connection and negative connection, where each charging data segment includes a charging start time, a charging end time, a charging time, and current data of each charging time, acquire voltage data corresponding to each current data, and draw a positive connection curve and a negative connection curve with the current data and the voltage data as coordinate axes;
the prediction module 502 is configured to divide the capacity of the lithium battery, construct a voltage-current data model by the method for constructing the capacity prediction model, and predict a charging and discharging curve in the next period of time;
the determining module 503 is configured to determine consistency between the charging and discharging curve and the forward connection curve, and if the charging and discharging curve is not similar to the forward connection curve, determine that the battery cell test is abnormal.
The present embodiment also provides an electronic apparatus, see fig. 7, comprising a memory 304 and a processor 302, wherein the memory 304 stores a computer program, and the processor 302 is configured to execute the computer program to perform the steps of any of the above method embodiments.
Specifically, the processor 302 may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.
The processor 302 may be configured to read and execute the computer program instructions stored in the memory 304 to implement any of the methods for estimating the capacity of the lithium battery or predicting the cell abnormality in the above embodiments.
Optionally, the electronic apparatus may further include a transmission device 306 and an input/output device 308, where the transmission device 306 is connected to the processor 302, and the input/output device 308 is connected to the processor 302.
The transmitting device 306 may be used to receive or transmit data via a network. Specific examples of the network described above may include wired or wireless networks provided by communication providers of the electronic devices. In one example, the transmission device includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmitting device 306 can be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.
The input/output device 308 is used to input or output information. For example, the input/output device may be a display screen, a mouse, a keyboard, or other devices. In this embodiment, the input device is used to input the collected information, the input information may be data, tables, images, real-time videos, and the output information may be texts, graphs, and the like of the prediction results.
Alternatively, in this embodiment, the processor 302 may be configured to execute the following steps by a computer program:
s101, obtaining historical sample data, wherein the historical sample data comprises a first capacity estimation value, a first capacity test value, a second capacity test value, a first sample standard deviation and a first actual deviation value, wherein the first sample standard deviation and the first actual deviation value are obtained through calculation according to the first capacity estimation value and the first capacity test value;
s102, calculating according to the first sample standard deviation and the first actual deviation value to obtain a first dynamic weight ratio corresponding to the first capacity estimation value;
s103, calculating the first dynamic weight ratio, the first capacity estimation value and the first capacity test value to obtain a second capacity estimation value;
s104, updating the first capacity measured value and the first capacity estimated value into a second capacity measured value and a second capacity estimated value;
and S105, repeating the steps from S101 to S104, and predicting the total capacity estimation value of the lithium battery.
S201, collecting charging data sections of the lithium battery under positive connection and negative connection, wherein each charging data section comprises charging starting time, charging ending time, charging time and current data of each charging time;
s202, acquiring voltage data corresponding to each current data, and drawing a forward connection curve and a backward connection curve by taking the current data and the voltage data as coordinate axes;
s203, grading the capacity of the lithium battery, constructing a voltage and current data model by the method for constructing the capacity prediction model, and predicting a charging and discharging curve in the next period of time;
and S204, judging the consistency of the charging and discharging curve and the positive connection curve, and if the charging and discharging curve and the positive connection curve are not similar, judging that the battery cell test is abnormal.
It should be noted that, for specific examples in this embodiment, reference may be made to examples described in the foregoing embodiments and optional implementations, and details of this embodiment are not described herein again.
In addition, in combination with the method for estimating the capacity of the lithium battery or the method for predicting the battery cell abnormality in the foregoing embodiments, the embodiments of the present application may provide a storage medium to implement. The storage medium having stored thereon a computer program; the computer program, when executed by a processor, implements any of the methods for lithium battery capacity estimation or cell anomaly prediction in the above embodiments.
It should be understood by those skilled in the art that various features of the above embodiments can be combined arbitrarily, and for the sake of brevity, all possible combinations of the features in the above embodiments are not described, but should be considered as within the scope of the present disclosure as long as there is no contradiction between the combinations of the features. The above examples are merely illustrative of several embodiments of the present application, and the description is more specific and detailed, but not to be construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (10)
1. A method of lithium battery capacity estimation, the method comprising: constructing a capacitance prediction model, comprising the following steps:
s101, obtaining historical sample data, wherein the historical sample data comprises a first capacity estimation value, a first capacity test value, a second capacity test value, a first sample standard deviation and a first actual deviation value, wherein the first capacity estimation value is a first sample measurement value;
s102, calculating according to the first sample standard deviation and the first actual deviation value to obtain a first dynamic weight ratio corresponding to the first capacity estimation value;
s103, calculating the first dynamic weight ratio, the first capacity estimation value and the first capacity test value to obtain a second capacity estimation value;
s104, updating the first capacity measured value and the first capacity estimated value into a second capacity measured value and a second capacity estimated value;
and S105, repeating the steps from S101 to S104, and predicting the total capacity estimation value of the lithium battery.
2. The method of lithium battery capacity estimation according to claim 1, wherein before S101, the method comprises:
collecting at least two charging data sections of a lithium battery in a formation and grading process; each charging data segment comprises charging starting time, charging ending time, charging time and current data of each charging time;
acquiring a first capacity measured value and a second measured value within a period of time corresponding to each charging data section through ampere-hour integration, and calculating to obtain a first mean value capacity and a first sample standard deviation;
storing the first capacity measurement, the second capacity measurement, the first mean capacity, and the first sample standard deviation in a historical sample database.
3. The method for lithium battery capacity estimation according to claim 1, wherein, in S102,
the first dynamic weight ratio is calculated by an optimal mean square error ratio, wherein,
the first dynamic weight ratio is expressed as: kf = dist ^ 2/(sstd ^2 + dist ^ 2)
kf is the first dynamic weight ratio, sstd is the first sample standard deviation, dist is the first actual deviation value.
4. The method for lithium battery capacity estimation according to claim 1, wherein in S103:
the second capacity estimate value is expressed as: x (k | k) = (1-kf) × (k-1 | k-1) + kf × y (k-1)
X (k | k) is the second capacity estimate, X (k-1 | k-1) is the first capacity estimate, y (k-1) is the first capacity measure, and k is the current prediction time instant.
5. A method for predicting battery core abnormity is characterized by comprising the following steps:
s201, collecting charging data sections of the lithium battery under positive connection and negative connection, wherein each charging data section comprises charging starting time, charging ending time, charging time and current data of each charging time;
s202, acquiring voltage data corresponding to each current data, and drawing a forward connection curve and a backward connection curve by taking the current data and the voltage data as coordinate axes;
s203, grading the capacity of the lithium battery, constructing a voltage and current data model by the method for constructing the capacity prediction model, and predicting a charging and discharging curve in the next period of time;
and S204, judging the consistency of the charging and discharging curve and the positive connection curve, and if the charging and discharging curve and the positive connection curve are not similar, judging that the battery cell test is abnormal.
6. The method for predicting battery cell abnormality according to claim 5, wherein S204 specifically is:
dividing the forward connection curve and the charging and discharging curve into linear curves of a plurality of time slices, calculating the rising slope of each time slice of the two curves, and accumulating the time slices of each curve to calculate the similarity of the two curves;
the similarity is expressed as:
xiis the slope of the rise of the voltage in the i period of the sample curve, yiThe voltage rising slope of the actually measured curve in the i time period is obtained; cos (θ) is the degree of similarity.
7. The method for predicting cell anomaly according to claim 5, wherein after S204, the method further comprises:
and judging the consistency of the charging and discharging curve and the reverse connection curve, calculating the reverse connection probability of the battery cell, judging the reverse connection of the battery cell if the reverse connection probability exceeds a threshold value, cutting off a charging power supply, and closing a channel.
8. A system for lithium battery capacity estimation, comprising:
the acquisition module is used for acquiring historical sample data;
the calculation module is used for calculating a first dynamic weight ratio corresponding to the first capacity estimation value according to the first sample standard deviation and the first actual deviation value, and calculating the first dynamic weight ratio, the first capacity estimation value and the first capacity test value to obtain a second capacity estimation value;
an estimation module, configured to update the first capacity measurement value and the first capacity estimation value to the second capacity measurement value and the second capacity estimation value, repeat S101 to S104 in the method for estimating the capacity of a lithium battery according to claims 1 to 4, and predict a total capacity estimation value of the lithium battery.
9. A system for cell anomaly prediction, comprising:
the acquisition module is used for acquiring charging data sections of the lithium battery under positive connection and negative connection, wherein each charging data section comprises charging starting time, charging ending time, charging time and current data of each charging time, voltage data corresponding to each current data is acquired, and a positive connection curve and a negative connection curve are drawn by taking the current data and the voltage data as coordinate axes;
the prediction module is used for grading the capacity of the lithium battery, constructing a voltage and current data model by the method for constructing the capacity prediction model and predicting a charging and discharging curve in the next period of time;
and the judging module is used for judging the consistency of the charging and discharging curve and the positive connection curve, and if the charging and discharging curve and the positive connection curve are not similar, the battery cell test is judged to be abnormal.
10. A storage medium having a computer program stored thereon, wherein the computer program is configured to execute the method for estimating capacity of a lithium battery according to any one of claims 1 to 4 and/or the method for predicting cell abnormality according to any one of claims 5 to 7 when the computer program is executed.
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Denomination of invention: Methods and systems for estimating the capacity of lithium batteries and predicting abnormal battery cells Granted publication date: 20210427 Pledgee: Hangzhou High-tech Financing Guarantee Co.,Ltd. Pledgor: Zhejiang newlink Technology Co.,Ltd. Registration number: Y2024980003747 |