CN116027200A - Lithium ion battery abnormality identification and diagnosis method based on historical data - Google Patents
Lithium ion battery abnormality identification and diagnosis method based on historical data Download PDFInfo
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Abstract
The invention relates to a lithium ion battery abnormality identification and diagnosis method based on historical data, which can be executed by one or more processors and comprises the following steps: s1, acquiring historical data of a battery system by the one or more processors, and cleaning the historical data in a mode of deleting invalid data and complementing missing data; s2, the one or more processors acquire a charging process in the historical data to further obtain a voltage threshold of a normal battery; s3, the one or more processors determine an abnormal deviation index of the battery based on the voltage threshold value, and complete screening of the abnormal battery based on the abnormal deviation index; and S4, the one or more processors can determine the fault type of the abnormal battery based on the change trend of the abnormal deviation index, and judge the fault degree of the abnormal battery based on the change speed of the abnormal deviation index.
Description
Technical Field
The invention relates to the field of new energy storage, in particular to a lithium ion battery abnormality identification and diagnosis method based on historical data.
Background
With the vigorous promotion of national policies, the electric automobile industry and the energy storage industry related to lithium ion batteries are vigorously developed, and in recent years, the lithium ion batteries are gradually applied to the field of rail transit. In recent two years, the energy density of the power battery is continuously increased, the battery grouping mode and process are further developed, the driving range and the power capability of the battery system basically meet the requirements of users, and the problem of mileage anxiety is not the focus of the industry. However, because the energy density of the battery is improved and the system energy is continuously improved through the integrated design in the grouping process, the safety of the battery body has hidden trouble. Meanwhile, the safety protection function of the system is imperfect, so that the safety problem of the battery frequently occurs, and social attention is brought. With the increase of the service time of the battery, the new energy secondary handcart market is gradually huge, and the reasonable valuation and insurance cost definition of the battery system are also the problems which need to be solved in the industry.
On one hand, the degradation of the lithium ion battery is derived from the technical problem in the manufacturing process of the battery core, and potential safety hazards are gradually developed after long-time use; on the other hand, the problems of overaging, internal short circuit and the like caused by abusive stress in the use process are solved. The battery fault type with gradual aging and continuously increased safety risk in the use process belongs to the battery fault type, and the sudden safety problem caused by mechanical stress, external short circuit and BMS fault is in some cases. The sudden safety problem is difficult to predict in advance, and the risk rate is generally reduced by improving the battery grouping process and designing the system with high strength, or the safety hazard caused by the failure of the battery is reduced by adding a fuse, a passive fire protection device, a heat spreading prevention design and the like through the passive safety protection of the system level. However, the safety problem caused by the gradual degradation of the battery in the use process can be early warned in advance through health monitoring, and human intervention is carried out, so that the probability of safety risk occurrence is reduced.
However, the BMS on-line management generally can only solve the problems of transient overvoltage, overtemperature and overcurrent occurring in the battery system, and it is difficult to locate and protect the slowly deteriorated battery during the entire operation. In recent years, the vigorous development of big data technology has prompted a method for evaluating the state of health of a battery through real vehicle operation data. At present, aiming at fault analysis of historical data, a data driving mode is used more, and body characteristics of a battery are ignored; building a model in a laboratory in too much dependence, and putting high requirements on the testing requirement of a new battery and the applicability of the model under actual working conditions; although signal decomposition based methods may rely less on data or equivalent models, no diagnosis is given of the type of fault.
Therefore, the method aims at diagnosing and identifying the abnormal battery aiming at the vehicle-mounted power battery, providing reasonable suggestions for maintenance of the vehicle, predicting the safety risk of the battery in advance, or judging the residual value of the battery through screening faults, so that the safe operation of the vehicle is ensured, and the healthy and orderly development of the industry is ensured.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a lithium ion battery abnormality identification and diagnosis method based on historical data.
The aim of the invention is achieved by the following technical scheme: a method for identifying and diagnosing anomalies in a lithium-ion battery based on historical data, the method being executable by one or more processors and comprising:
s1, acquiring historical data of a battery system by the one or more processors, and cleaning the historical data in a mode of deleting invalid data and complementing missing data;
s2, the one or more processors acquire a charging process in the historical data to further obtain a voltage threshold of a normal battery;
s3, the one or more processors determine an abnormal deviation index of the battery based on the voltage threshold value, and complete screening of the abnormal battery based on the abnormal deviation index;
and S4, the one or more processors can determine the fault type of the abnormal battery based on the change trend of the abnormal deviation index, and judge the fault degree of the abnormal battery based on the change speed of the abnormal deviation index.
Preferably, the one or more processors are based on a formula
Preferably, the one or more processors acquire the voltage threshold as follows:
acquiring all battery voltages v at time t in the cleaned historical data t ；
Based on the formula
Obtaining the average value mu of the battery voltage at the moment t t,n ；
Based on the formula
Obtaining standard value sigma of battery voltage at t moment t,n ；
The voltage threshold v t,i Can be expressed as mu t,n -3σ t,n ≤v t,i ≤μ t,n +3σ t,n ,i＝1,2,3...n。
Preferably, the one or more processors are capable of determining the abnormality deviation index d based on the voltage threshold and the standard value i,t The abnormality deviation index can be expressed as
Preferably, the one or more processors acquire a charging process of the normal battery according to the following steps:
according to the formula
Screening the charging end time, wherein SOC (t) is the residual electric quantity value at the time t, and I (k) is the current at the time k;
comparing the SOC (i) at the moment i with the SOC (i) at the moment immediately above in reverse order according to time from the end of charging, and taking a sampling point of which the first SOC (i) is smaller than the SOC (i) at the moment immediately above as the starting end of a charging section;
and eliminating charging ends with the difference value of the SOC (i) at the beginning and the tail ends of the charging section being less than 40%.
Preferably, the one or more processors complete the screening of the abnormal battery according to the following steps:
n sampling points with equal intervals are selected, and an abnormal deviation index d of the discrete battery is established based on one charging process i ＝[d i,t(SOC1) ,d i,t(SOC2) ,……，d i,t(SOCN) ]；d i,t(SOCN) The abnormal deviation index of the ith battery is the point of the nth SOC.
And when the frequency of the abnormal deviation index larger than 1 is larger than a set threshold value by 25%, judging the discrete battery as an abnormal battery.
Preferably, the one or more processors determine the type of fault of the abnormal battery according to the following steps:
performing a linear fit to the anomaly deviation index to obtain a linear fit curve thereof, the linear fit curve being capable of being represented as
m is the slope of a linear fit line, b 0 Intercept, SOC, of linear fit s And SOC (System on chip) e The start SOC and the end SOC, respectively.
Dividing the fault type into the conditions of too small residual electric quantity and smaller battery capacity, wherein the judgment basis of the fault type is as follows:
wherein m is i The slope is linear fit for cell number i.
Preferably, the one or more processors determine a trend of change of the abnormal deviation index according to the following steps: establishing a three-dimensional matrix of anomaly deviation indices for a full history of processes
Obtaining the comprehensive deviation index COV of each battery t(SOC),c ＝σ t(SOC),c /μ t(SOC),c The method comprises the steps of carrying out a first treatment on the surface of the Obtaining weight coefficients of different SOC point deviation indexes
Wherein the deviation index weighting matrix for each charging process can be expressed as ω c ＝[w SOC1,c ,w SOC2,c ,…,w SOCN,c ]The whole history process deviation index weight matrix is expressed as +.>
Obtaining the Integrated deviation index of each cell during a single Charge>
Constructing a comprehensive deviation index matrix of the whole history process>
Preferably, the one or more processors determine the degradation rate of the battery according to the following steps: constructing a variable speed matrix
Setting the judging condition of the variant too fast battery to +.>
Wherein k is i,c ＝f i,c+1 -f i,c 。
The invention has the following advantages: the invention solves the problem that a large number of laboratory tests or complex models are required to be constructed in the prior period for the lithium ion power battery in the service process, can effectively screen out abnormal batteries, distinguish the abnormal condition as SOC abnormality or capacity abnormality, provide quick operation suggestion for field maintenance personnel, and reduce the requirement on the professional degree of the practitioner; meanwhile, batteries with larger safety risks are screened, early warning information is timely given, and safety accidents are prevented.
Drawings
The invention has the following drawings:
the drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
fig. 1 is a schematic flow chart of the lithium battery abnormality recognition and diagnosis method according to the present invention.
The cleaning process of the voltage and current data of fig. 2.
FIG. 3 dynamic threshold screening process.
Fig. 4 is an abnormal battery screening process based on offset frequency.
Fig. 5 accommodates variations in deviation index for different anomaly type batteries.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings. The detailed description, while indicating exemplary embodiments of the invention, includes various details of the embodiments of the invention for the purpose of illustration only, should be considered as exemplary. Accordingly, those skilled in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
As shown in fig. 1, the present application provides a lithium battery abnormality recognition and diagnosis method based on historical data, firstly, cleaning data with respect to the historical data, deleting invalid data, and complementing missing data; secondly, reconstructing a charging curve of a normal battery by using a statistical method; calculating an abnormal deviation index again, and screening and identifying abnormal batteries through a threshold value; then, judging the abnormal type of the abnormal battery according to the change trend of the deviation index; and finally, diagnosing the fault degree of the battery according to the long-time abnormal index change speed, and giving out early warning information. Specifically, firstly, aiming at the problems of acquisition noise, packet loss (recorded as null or zero), time disorder and the like in the historical data of the battery system, the single voltage data is firstly cleaned according to a formula (1) and a formula (2).
Where V (i, t) represents the voltage value of the i-cell at time t. The current data are cleaned with the absolute value exceeding 200A as a limit. Fig. 2 is a process for cleaning voltage and current data of a vehicle. Aiming at the phenomenon that most abnormal conditions in historical data are 0 value or null value caused by data transmission problems, the single voltage range of the same type of battery is basically determined, and the situation that the battery voltage is suddenly changed to 0V generally does not occur, the application designs a linear interpolation method aiming at the 0 value and the null value. Firstly, zero values and null values in the voltage of the single bodies are searched, statistics is carried out on the numbers, for example, the number of the voltages with the zero values and the null values occurs in all the single bodies, the corresponding recording time exceeds 30 minutes, which indicates that a large amount of operation data is not effectively transmitted at the moment, the data is invalid at the moment, and the data is directly deleted. If the data of zero value and null value are generated in a certain single battery at a certain moment and the corresponding time of the data is less than 30min, interpolation is carried out according to a linear interpolation method.
Second, all charging processes in the history data are extracted. In the charging process, the SOC data is in a continuous rising state; at the end of charge, the SOC data is typically a higher SOC, which is at most 100%. Taking the SOC with the charge exceeding 90% as an effective charging section, the screening flow of the charging section is as follows:
1) Firstly, screening the charging ending time according to a formula (3);
2) Comparing the SOC value at the moment with the last moment in sequence according to the reverse time sequence from the charging end, and taking a sampling point of which the first SOC value is smaller than the last moment as the starting end of the charging section;
3) And eliminating the charging section with the SOC difference of the head and the tail ends of the charging section smaller than 40%.
Again, a standard battery reference voltage curve is constructed. The construction of the standard battery reference voltage curve comprises the following steps: firstly, screening each moment point of charging data in a single process by adopting a traditional 3 sigma criterion method, and marking a battery with a voltage range outside the 3 sigma range; removing marked batteries, and continuing to use a 3 sigma criterion method to screen the batteries; repeating the first two steps until no marked battery exists; the voltages of the remaining batteries are averaged to obtain a standard battery reference voltage curve. Specifically, a section of battery pack charging section data v is extracted t The Gaussian distribution parameters of the data matrix are calculated as shown in the formula (4) and the formula (5), and the voltage threshold value of the healthy battery is determined as shown in the formula (6):
μ t,n -3σ t,n ≤v t,i ≤μ t,n +3σ t,n ,i＝1,2,3...n(6)
wherein v is t Refers to all the cell voltages at time t, v in the data after washing t,i Refers to the voltage of the ith battery at time t, mu t,n Mean value sigma of battery voltage at t moment t,n Refers to the standard deviation of the battery voltage at time t. And adding a loop iteration process, and dynamically adjusting a screening threshold according to the actual condition of the sample. Rejecting the abnormal battery which is screened outRepeating (4) - (6), wherein n is updated to n' in the formula, and the newly-appeared outlier battery is judged by continuously reducing the threshold value in order to eliminate the number of the outlier battery, as shown in fig. 3. After the abnormal voltage value in the series battery pack is removed, the remaining battery can be regarded as a battery which is normally aged along with the use process of the vehicle, so that the battery which is not screened out is defined as a healthy battery. The average voltage value of the healthy battery can be used as a reference voltage value for diagnosing the subsequent abnormal battery.
The distance between the voltage of the single battery and the average value of the voltages of the healthy batteries is defined as a voltage deviation value, and the standard deviation of the voltages of the healthy batteries in the battery pack is used for representing the voltage dispersion degree of the ideal battery pack. The ratio of the voltage deviation value to the standard deviation of the 3 times of the voltage of the healthy battery is the voltage deviation index d at each moment i,t As in formula (7).
d i ＝[d i,t(SOC1) ,d i,t(SOC2) ,……，d i,t(SOCN) ] (8)
Selecting equidistant SOC points, and establishing a deviation index vector d of a discrete battery based on one charging process i As in formula (8). Statistics d i Of the above, the frequency of more than 1, the frequency of more than 25% of the set threshold value is regarded as an abnormal battery. As shown in fig. 4.
And then, classifying faults of the abnormal batteries screened in the previous step. Linear fitting of the degree of deviation is performed as in formula (9):
judging faults by utilizing the change trend of the fitting curve, wherein the judging basis of the battery with lower SOC and the battery with smaller capacity is shown as the formula (10):
the battery deviation index is basically negative in the whole course due to low voltage and is an SOC too low fault, as shown in fig. 5 (a); zero crossing points appear when the deviation degree of the battery changes, and the fitted curve slope is positive at the end of charging, and the battery is in a small capacity fault, as shown in fig. 5 (b).
And finally, judging the mutation trend through the deviation index change of the whole history process. Historical data of the electric automobile for 1 to 12 months is usually selected for analysis. Establishing a deviation index matrix D of a single charging process c As in formula (11).
In the formula (11), c represents the c-th charge in the whole history, i represents the battery number, t represents the time point corresponding to the selected SOC, and d i,t(SOCj) Represents an abnormal deviation index of the ith battery at the time of the jth SOC at the time of the c-th charge.
Calculating the comprehensive deviation index of each battery by adopting a coefficient of variation method, wherein the coefficient of variation is calculated as shown in a formula (12):
COV t(SOC),c ＝σ t(SOC),c /μ t(SOC),c (12)
mu in formula (12) t(SOC),c Sum sigma t(SOC),c The average value and standard deviation of the voltage at the time t (SOC) in the c-th charging process are respectively shown.
Wherein sigma t,c Represents standard deviation, mu, of all battery deviation indexes at time t in the c-th charge t,c Represents the average of all cell deviation indexes, namely, formula (13) and formula (14).
Different SOC point deviationsThe index weight coefficient is shown as formula (15), the deviation index weight matrix of each charging process is shown as formula (16), and the whole history process deviation index weight matrix W is shown as formula (17). COV in formula (15) C From equation (12), the coefficient of variation at time t, w SOCN，c And when the charge is carried out for the c-th time, the weight coefficient at the SOCN moment is W which is a weight coefficient matrix constructed in the multiple charging processes.
ω c ＝[w SOC1,c ,w SOC2,c ,…,w SOCN,c ] (16)
Thus, the integrated deviation index f of each battery during a single charge i,c As in equation (18), a comprehensive deviation index matrix F of the whole history is constructed as in equation (19). D in formula (19) represents a deviation index matrix of all charging processes, which is a 3-dimensional matrix; w is a weight coefficient matrix constructed by a plurality of charging processes constructed by the formula (17), f i,c Integrated deviation index for the nth charge of the ith battery.
To determine the degradation speed of the battery, a variable speed matrix K is set, as in equation (20), where K represents the variation of the voltage deviation of each battery, and the abrupt position and time of the battery are located and determined by the variable speed matrix. The voltage deviation is directly presented to the outside after the battery fails, namely the higher the deviation degree is, namely the higher the k is, the higher the degradation speed is, and the higher the failure risk is, so the application is specific to k i,c Threshold of (2)Set to 2. Meanwhile, in order to prevent the battery pack from being put on hold for a long time and generating the collective change of the series battery pack, the judgment condition of the excessively fast battery is the formula (22).
k i,c ＝f i,c+1 -f i,c (21)
What is not described in detail in this specification is prior art known to those skilled in the art.
It should be understood that the foregoing examples of the present invention are provided merely for the purpose of clearly illustrating the invention and are not intended to limit the embodiments of the present invention, and that various other changes and modifications may be made therein by one skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. A method for identifying and diagnosing anomalies in a lithium-ion battery based on historical data, the method being executable by one or more processors and comprising:
s1, acquiring historical data of a battery system by the one or more processors, and cleaning the historical data in a mode of deleting invalid data and complementing missing data;
s2, the one or more processors acquire a charging process in the historical data to further obtain a voltage threshold of a normal battery;
s3, the one or more processors determine an abnormal deviation index of the battery based on the voltage threshold value, and complete screening of the abnormal battery based on the abnormal deviation index;
and S4, the one or more processors can determine the fault type of the abnormal battery based on the change trend of the abnormal deviation index, and judge the fault degree of the abnormal battery based on the change speed of the abnormal deviation index.
2. The method of claim 1, wherein the one or more processors are based on a formula
Cleaning the history data, wherein ∈10>
V (i, t) represents the voltage value of the i-cell at time t, k is the first time when the cell voltage has a value of 0, and m is the first non-0 time after 0.
3. The method of claim 1, wherein the one or more processors obtain the voltage threshold by:
acquiring all battery voltages v at time t in the cleaned historical data t ；
Based on the formula
Obtaining the average value mu of the battery voltage at the moment t t,n ；
Based on the formula
Obtaining standard value sigma of battery voltage at t moment t,n ；
The voltage threshold v t,i Can be expressed as mu t,n -3σ t,n ≤v t,i ≤μ t,n +3σ t,n ,i＝1,2,3...n。
4. The method of claim 3, wherein the one or more processors are capable of determining the abnormality deviation index d based on the voltage threshold and the standard value i,t The abnormality deviation index can be expressed as
5. The method for identifying and diagnosing anomalies in a lithium-ion battery according to claim 1, wherein the one or more processors acquire a charging process for a normal battery according to the steps of:
according to the formula
Screening the charging end time, wherein SOC (t) is the residual electric quantity value at the time t, and I (k) is the current at the time k;
comparing the SOC (i) at the moment i with the SOC (i) at the moment immediately above in reverse order according to time from the end of charging, and taking a sampling point of which the first SOC (i) is smaller than the SOC (i) at the moment immediately above as the starting end of a charging section;
and eliminating charging ends with the difference value of the SOC (i) at the beginning and the tail ends of the charging section being less than 40%.
6. The method for identifying and diagnosing anomalies of a lithium-ion battery according to claim 1, wherein the one or more processors complete the screening of anomalies according to the steps of:
n sampling points with equal intervals are selected, and an abnormal deviation index d of the discrete battery is established based on one charging process i ＝[d i,t(SOC1) ,d i,t(SOC2) ,……，d i,t(SOCN) ]；d i,t(SOCN) The abnormal deviation index of the ith battery is the point of the nth SOC.
And when the frequency of the abnormal deviation index larger than 1 is larger than a set threshold value by 25%, judging the discrete battery as an abnormal battery.
7. The method of claim 1, wherein the one or more processors determine the type of fault in the abnormal battery by:
performing linear fitting on the abnormal deviation index in a single charging process to obtain a linear fitting curve thereof, wherein the linear fitting curve can be expressed as
m is the slope of a linear fit line, b 0 Intercept, SOC, of linear fit s And SOC (System on chip) e The start SOC and the end SOC, respectively.
Dividing the fault type into too small residual electric quantity and smaller battery capacity, wherein the judging basis of the fault type is as follows:
wherein m is i The slope is linear fit for cell number i.
8. The method of claim 4, wherein the one or more processors determine a trend of the abnormality deviation index by:
establishing a three-dimensional matrix of anomaly deviation indices for a full history of processes
Obtaining the comprehensive deviation index COV of each battery t(SOC),c ＝σ t(SOC),c /μ t(SOC),c ；
Obtaining weight coefficients of different SOC point deviation indexes
Wherein the deviation index weighting matrix for each charging process can be expressed as ω c ＝[w SOC1,c ,w SOC2,c ,…,w SOCN,c ]The whole history process deviation index weight matrix is expressed as +.>
/>
Obtaining the comprehensive deviation index of each battery in the single charging process
Constructing a comprehensive deviation index matrix for a full history process
9. The method of claim 8, wherein the one or more processors determine the degradation rate of the battery by:
constructing a variable speed matrix
Setting the judging condition of the excessively fast battery to be
Wherein k is i,c ＝f i,c+1 -f i,c 。/>
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116365066A (en) * | 2023-05-19 | 2023-06-30 | 东莞市易利特新能源有限公司 | BMS module-based power management system |
| CN117310543A (en) * | 2023-11-29 | 2023-12-29 | 中国华能集团清洁能源技术研究院有限公司 | Battery abnormality diagnosis method and device |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116365066A (en) * | 2023-05-19 | 2023-06-30 | 东莞市易利特新能源有限公司 | BMS module-based power management system |
| CN116365066B (en) * | 2023-05-19 | 2023-09-22 | 东莞市易利特新能源有限公司 | BMS module-based power management system |
| CN117310543A (en) * | 2023-11-29 | 2023-12-29 | 中国华能集团清洁能源技术研究院有限公司 | Battery abnormality diagnosis method and device |
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