CN111965559B - On-line estimation method for SOH of lithium ion battery - Google Patents
On-line estimation method for SOH of lithium ion battery Download PDFInfo
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Abstract
The invention discloses an on-line estimation method for SOH of a lithium ion battery, which comprises the following steps: step 1, collecting voltage, current, time and capacity of an electric automobile battery in the constant voltage charging process and current, time and capacity of the electric automobile battery in the constant current discharging process in an off-line manner; step 2, extracting constant voltage charging time adopted in the step 1 as a health index; step 3, preprocessing health index data by adopting a Laida criterion; step 4, establishing a mapping relation between the health index and the capacity by adopting a BP neural network algorithm; and 5, acquiring on-line signals, extracting health indexes, estimating the capacity by adopting a BP neural network algorithm, and calculating the SOH value of the battery. The invention solves the problems that the existing method for estimating the SOH of the battery of the electric automobile takes the capacity or the internal resistance as a health index, the online estimation application cannot be realized, the parameters of the filtering method are acquired in a fixed charge and discharge mode, and the dynamic change characteristic of the electric automobile in the actual working process cannot be fully considered.
Description
Technical Field
The invention belongs to the technical field of lithium batteries, and relates to an on-line estimation method for SOH of a lithium ion battery.
Background
The lithium ion battery is widely used in electric vehicles and various fields and gradually becomes the key and support of important fields in the future due to the advantages of high specific energy, high working voltage, wide temperature range, low self-discharge rate, long cycle life, good safety and the like. However, the performance degradation of the lithium battery during the use process can affect the normal use of instruments and equipment, even cause serious fault accidents, and usually the state of health (SOH) of the lithium ion battery refers to the ratio of the measured capacity to the rated capacity, so that the SOH estimation of the lithium ion battery can optimize the use strategy of the battery and prolong the performance decay period of the battery; in addition, the bad battery can be replaced in time, the use cost of the battery is reduced, and the service life of the whole electric automobile is prolonged. So lithium ion battery SOH estimation is particularly important.
Internal resistance, capacity, and charge-discharge behavior and cycle number are subjects of lithium ion battery SOH estimation studies, however, these studies are generally used for verification of the SOH estimation method off-line. The scholars propose to extract Health Indicators (HI) from charge-discharge behaviors and cycle times, and establish a mapping relationship between HI and SOH for real-time online SOH estimation. However, the charging and discharging behaviors and the cycle times of the electric automobile are different in actual operation, and the method is not suitable for online SOH estimation of the lithium ion battery of the electric automobile.
The discharging process of the electric automobile is random, but the charging process of the electric automobile is regular, the electric automobile is generally divided into three stages of constant-current charging, constant-voltage charging and floating charging, the characteristic of shorter constant-current charging time is not obvious enough, and the charging starting points of the electric automobile are different, so that the monitoring information error is larger. The float charging characteristics are hardly changed, so that only the constant voltage charging stage information is fully retained, and the initial state and the termination state are the same, and are not affected by the previous incomplete discharge process.
Most of the existing methods for estimating the SOH of the battery of the electric automobile cannot realize online estimation application by taking the capacity or the internal resistance as a health index, and the parameters of the filtering method need to be obtained in a fixed charge and discharge mode, so that the problem of dynamic change characteristics in the actual working process of the electric automobile cannot be fully considered.
Disclosure of Invention
The invention aims to provide an on-line estimation method for SOH of a lithium ion battery, which solves the problems that the existing estimation method for SOH of an electric automobile takes capacity or internal resistance as a health index, on-line estimation application cannot be realized, and parameters of a filtering method need a fixed charge and discharge mode to be obtained, so that the dynamic change characteristic of the electric automobile in the actual working process cannot be fully considered.
The technical scheme adopted by the invention is that the on-line estimation method of the SOH of the lithium ion battery is implemented according to the following steps:
step 1, collecting voltage, current, time and capacity of an electric automobile battery in the constant voltage charging process and current, time and capacity of the electric automobile battery in the constant current discharging process in an off-line manner;
step 2, extracting constant voltage charging time adopted in the step 1 as a health index;
step 3, preprocessing health index data by adopting a Laida criterion;
step 4, establishing a mapping relation between the health index and the capacity by adopting a BP neural network algorithm;
and 5, acquiring on-line signals, extracting health indexes, estimating the capacity by adopting a BP neural network algorithm, and calculating the SOH value of the battery.
The present invention is also characterized in that,
the step 1 specifically comprises the following steps:
collecting voltage, current and time in the constant voltage charging process of the lithium ion battery of the electric automobile by adopting a voltage and current sensor and a time timer;
and after the electric automobile is fully charged, acquiring current and time in the constant-current discharging process of the lithium ion battery of the electric automobile by adopting a current sensor and a time timer, and multiplying the current and time in the constant-current discharging process to obtain the battery capacity.
The step 2 is specifically as follows:
calculating constant voltage charging time health index time sequence, wherein the corresponding constant voltage discharging time sequence in the ith period is i.e. the health index HI of the ith period i The method comprises the following steps:
HI i ＝t CVCend (i)-t CVCstart (i),i＝1,2,3,... (1)
wherein t is CVCstart And t CVCend The i-th period constant voltage charge start and stop times, respectively.
The step 3 is specifically as follows:
step 3.1, firstly, calculating the standard deviation sigma of the health index data, wherein the calculation formula is as follows
Wherein n is the total number of times of the health index,
is the arithmetic mean of the health index;
and 3.2, judging and correcting the health index data according to the Laida criterion, wherein the Laida criterion is judged according to the following steps:
wherein R is n Is the residual error that is present in the sample,
is a health index estimated value;
if the residual error is larger than 3 sigma, the health index data is coarse error and should be discarded or compensated, and if the residual error is smaller than or equal to 3 sigma, the health index data is normal data and is reserved.
The step 4 is specifically as follows:
step 4.1, carrying out correlation analysis between the health index data and the capacity by adopting the pearson correlation coefficient, calculating the correlation coefficient of the health index data and the capacity, and if the correlation coefficient is larger than 0.6, carrying out strong correlation, and enabling the health index time sequence to be effective;
step 4.2, taking the effective health index time sequence proved in step 4.1 as the input of the BP neural network algorithm, taking the lithium ion battery capacity data as the output of the BP neural network algorithm, establishing the relation between the health index and the lithium ion battery capacity, and training to obtain the network parameter omega ij 、ω jk 、a j And b k The method specifically comprises the following steps:
wherein C is k For the battery capacity sequence, i is the number of nodes of an input layer, j is the number of nodes of an hidden layer, and k is the number of nodes of an output layer, wherein the number of the nodes of the input layer is equal to the number of the nodes of the output layer, so i=k, omega ij For connecting weights between input layer and hidden layer, a j For input layer and hidden layer connection threshold, ω jk B, connecting weights for the input layer and the hidden layer k Thresholds are connected for the input layer and hidden layer.
In the step 4.1, the pearson correlation coefficient is adopted to perform correlation analysis between the health index data and the capacity, the correlation coefficient between the health index data and the capacity is calculated, and the calculation formula of the correlation coefficient r is as follows:
wherein N is the total charge and discharge times, C i HI for the i-th cycle battery capacity i Is the i-th cycle health index.
The specific process of the step 5 is as follows:
step 5.1, collecting charging voltage, current and time in the constant voltage charging process of the electric automobile by adopting a voltage and current sensor and a time meter on line;
step 5.2, extracting the constant voltage charging time in step 5.1 as a health index according to the method of step 2 to obtain a time sequence HI 'of the current health index' i ；
Step 5.3, time series HI 'of the current health index obtained in the step 5.2' i Inputting the current battery capacity sequence C 'obtained by performing capacity estimation in the relation between the health index and the lithium ion battery capacity established in the step 4.2' k ：
Step 5.4, calculating the SOH of the current battery according to the SOH definition formula:
C N is the nominal capacity of the battery, C' k Is the current battery capacity sequence.
The invention has the beneficial effects that
The method can effectively avoid the influence of the battery cycle times and the fixed charge and discharge modes on the filtering method, and solves the problems that most of the existing methods for estimating the SOH of the battery of the electric automobile, which take the capacity or the internal resistance as health indexes, cannot realize online estimation application, and the parameters of the filtering method need the fixed charge and discharge modes to be acquired, so that the dynamic change characteristics of the electric automobile in the actual working process cannot be fully considered. The invention provides an on-line lithium ion battery SOH estimation method suitable for the actual working condition of an electric automobile, which comprises the steps of firstly utilizing measurable parameters of the actual charging process of the electric automobile, providing constant voltage charging time as a health index, and preprocessing the health index by adopting a Laida criterion method; secondly, analyzing health indexes and battery capacity by adopting Pelson correlation coefficients, and establishing a mapping relation between the health indexes and the actual capacity of the battery by using a BP neural network algorithm; and finally, adopting a BP neural network algorithm to carry out SOH estimation.
Drawings
FIG. 1 is a general flow chart of an on-line estimation method of SOH of a lithium ion battery according to the present invention;
FIG. 2 is a chart of data acquisition of an offline lithium ion battery in an on-line estimation method of SOH of the lithium ion battery;
FIG. 3 is a diagram of an on-line lithium ion battery health index HI extraction method in an on-line estimation method of SOH of a lithium ion battery;
FIG. 4 is a flowchart for processing the health index HI outlier in the on-line estimation method of the SOH of the lithium ion battery;
FIG. 5 is a map model diagram of health index HI and lithium ion battery capacity in an on-line estimation method of SOH of a lithium ion battery;
FIG. 6 is a diagram of an on-line SOH estimation model of a lithium ion battery in an on-line SOH estimation method of the lithium ion battery;
FIG. 7 is a diagram showing the comparison of the abnormal point untreated health index HI and the lithium ion battery in the on-line estimation method of the SOH of the lithium ion battery;
FIG. 8 is a comparison chart of the health index HI and the lithium ion battery after abnormal point processing in the on-line estimation method of the SOH of the lithium ion battery;
fig. 9 is a simulation diagram of SOH estimation of an online lithium ion battery in the SOH online estimation method of a lithium ion battery according to the present invention.
Detailed Description
The invention will be described in detail below with reference to the drawings and the detailed description.
The invention discloses a method for estimating SOH of a lithium ion battery on line, which is shown in a figure 1, and is implemented according to the following steps:
step 1, collecting voltage, current, time and capacity of an electric automobile battery in the constant voltage charging process and current, time and capacity of the electric automobile battery in the constant current discharging process in an off-line manner; as shown in fig. 2, specifically:
collecting voltage, current and time in the constant voltage charging process of the lithium ion battery of the electric automobile by adopting a voltage and current sensor and a time timer;
and after the electric automobile is fully charged, acquiring current and time in the constant-current discharging process of the lithium ion battery of the electric automobile by adopting a current sensor and a time timer, and multiplying the current and time in the constant-current discharging process to obtain the battery capacity.
Step 2, extracting constant voltage charging time adopted in the step 1 as a health index; as shown in fig. 3, specifically:
calculating constant voltage charging time health index time sequence, wherein the corresponding constant voltage discharging time sequence in the ith period is i.e. the health index HI of the ith period i The method comprises the following steps:
HI i ＝t CVCend (i)-t CVCstart (i),i＝1,2,3,... (1)
wherein t is CVCstart And t CVCend Respectively, i-th period constant voltage charge start andand (5) ending time.
Step 3, preprocessing health index data by adopting a Laida criterion; as shown in fig. 4, specifically:
step 3.1, firstly, calculating the standard deviation sigma of the health index data, wherein the calculation formula is as follows
Wherein n is the total number of times of health index, generally not less than 20 times,
is the arithmetic mean of the health index;
and 3.2, judging and correcting the health index data according to the Laida criterion, wherein the Laida criterion is judged according to the following steps:
wherein R is n Is the residual error that is present in the sample,
is a health index estimated value;
if the residual error is larger than 3 sigma, the health index data is coarse error and should be discarded or compensated, and if the residual error is smaller than or equal to 3 sigma, the health index data is normal data and is reserved.
Step 4, establishing a mapping relation between the health index and the capacity by adopting a BP neural network algorithm; as shown in fig. 5, specifically:
step 4.1, carrying out correlation analysis between the health index data and the capacity by adopting the pearson correlation coefficient, calculating the correlation coefficient of the health index data and the capacity, and if the correlation coefficient is larger than 0.6, carrying out strong correlation, and enabling the health index time sequence to be effective; the pearson correlation coefficient is adopted for carrying out correlation analysis between the health index data and the capacity, the correlation coefficient of the health index data and the capacity is calculated, and the calculation formula of the correlation coefficient r is as follows:
wherein N is the total charge and discharge times, C i HI for the i-th cycle battery capacity i Is the ith periodic health index;
step 4.2, taking the effective health index time sequence proved in step 4.1 as the input of the BP neural network algorithm, taking the lithium ion battery capacity data as the output of the BP neural network algorithm, establishing the relation between the health index and the lithium ion battery capacity, and training to obtain the network parameter omega ij 、ω jk 、a j And b k The method specifically comprises the following steps:
wherein C is k For the battery capacity sequence, i is the number of nodes of an input layer, j is the number of nodes of an hidden layer, and k is the number of nodes of an output layer, wherein the number of the nodes of the input layer is equal to the number of the nodes of the output layer, so i=k, omega ij For connecting weights between input layer and hidden layer, a j For input layer and hidden layer connection threshold, ω jk B, connecting weights for the input layer and the hidden layer k Thresholds are connected for the input layer and hidden layer.
step 5.1, collecting charging voltage, current and time in the constant voltage charging process of the electric automobile by adopting a voltage and current sensor and a time meter on line;
step 5.2, extracting the constant voltage charging time in step 5.1 as a health index according to the method of step 2 to obtain a time sequence HI 'of the current health index' i ；
Step 5.3, time series HI 'of the current health index obtained in the step 5.2' i Inputting the current battery capacity sequence C 'obtained by performing capacity estimation in the relation between the health index and the lithium ion battery capacity established in the step 4.2' k ：
Step 5.4, calculating the SOH of the current battery according to the SOH definition formula:
C N is the nominal capacity of the battery, C' k Is the current battery capacity sequence.
Examples
The data set adopted by the invention is derived from a lithium ion battery test bed built by a NASA PCoE research center, and the battery adopts a commercially available 18650Li-ion battery with rated capacity of 2Ah. Lithium ion batteries (B0005, B0006 and B0007) were used to pass through 3 different operating characteristics (charge, discharge and impedance) at room temperature. Charging was performed in a 1.5A Constant Current (CC) mode until the battery voltage reached 4.2V, and then in a Constant Voltage (CV) mode until the charging current dropped to 20mA.
And extracting constant voltage charging voltage, constant current and constant time from the data, and extracting the capacity of the lithium ion battery. The health index HI and capacity versus graph are shown in FIG. 7. It can be seen that errors occur in the actual extraction process and measurement, and correction is required for the error points, so that the data is corrected by using the Laida criterion method, the error points are corrected as shown in fig. 8, and the constant voltage charging time health index and the battery capacity have strong negative correlation, and the correlation is shown in table 1.
TABLE 1 health index HI and Battery Capacity correlation
| Lithium ion battery | Before correction of abnormal point | After correction of abnormal point |
| B0005 | -0.8551 | -0.9803 |
| B0006 | -0.8634 | -0.9474 |
| B0007 | -0.8380 | -0.9632 |
And (3) establishing a mapping between three battery health indexes HI and battery capacity by adopting a BP neural network algorithm, and carrying out on-line SOH estimation verification of the lithium ion battery by taking B0005 as an example, wherein the result is shown in figure 9. Maximum absolute error 0.0749, minimum absolute error 3.2730e-07, mean absolute error 0.0180, root mean square error 0.0252. The overall SOH estimation error is less than 2%, and the method is suitable for the actual working conditions of the electric automobile.
Claims (1)
1. The on-line estimation method for the SOH of the lithium ion battery is characterized by comprising the following steps of:
step 1, collecting voltage, current, time and capacity of an electric automobile battery in the constant voltage charging process and current, time and capacity of the electric automobile battery in the constant current discharging process in an off-line manner;
the step 1 specifically comprises the following steps:
collecting voltage, current and time in the constant voltage charging process of the lithium ion battery of the electric automobile by adopting a voltage and current sensor and a time timer;
after the electric automobile is fully charged, acquiring current and time in the constant-current discharging process of the lithium ion battery of the electric automobile by adopting a current sensor and a time meter, and multiplying the current and time in the constant-current discharging process to obtain the battery capacity;
step 2, extracting constant voltage charging time adopted in the step 1 as a health index;
the step 2 specifically comprises the following steps:
calculating constant voltage charging time health index time sequence, wherein the corresponding constant voltage discharging time sequence in the ith period is i.e. the health index HI of the ith period i The method comprises the following steps:
HI i ＝t CVCend (i)-t CVCstart (i),i＝1,2,3,...(1)
wherein t is CVCstart And t CVCend Respectively the start time and the stop time of constant voltage charging in the ith period;
step 3, preprocessing health index data by adopting a Laida criterion;
the step 3 specifically comprises the following steps:
step 3.1, firstly, calculating the standard deviation sigma of the health index data, wherein the calculation formula is as follows
Wherein n is the total number of times of the health index,
is the arithmetic mean of the health index;
and 3.2, judging and correcting the health index data according to the Laida criterion, wherein the Laida criterion is judged according to the following steps:
wherein R is n Is the residual error that is present in the sample,
is a health index estimated value;
if the residual error is larger than 3 sigma, the health index data is coarse error and should be discarded or compensated, and if the residual error is smaller than or equal to 3 sigma, the health index data is normal data and is reserved;
step 4, establishing a mapping relation between the health index and the capacity by adopting a BP neural network algorithm;
the step 4 specifically comprises the following steps:
step 4.1, carrying out correlation analysis between the health index data and the capacity by adopting the pearson correlation coefficient, calculating the correlation coefficient of the health index data and the capacity, and if the correlation coefficient is larger than 0.6, carrying out strong correlation, and enabling the health index time sequence to be effective;
step 4.2, taking the effective health index time sequence proved in step 4.1 as the input of the BP neural network algorithm, taking the lithium ion battery capacity data as the output of the BP neural network algorithm, establishing the relation between the health index and the lithium ion battery capacity, and training to obtain the network parameter omega ij 、ω jk 、a j And b k The method specifically comprises the following steps:
wherein C is k For the battery capacity sequence, i is the number of nodes of an input layer, j is the number of nodes of an hidden layer, and k is the number of nodes of an output layer, wherein the number of the nodes of the input layer is equal to the number of the nodes of the output layer, so i=k, omega ij For connecting weights between input layer and hidden layer, a j For input layer and hidden layer connection threshold, ω jk B, connecting weights for the input layer and the hidden layer k Connecting thresholds for the input layer and the hidden layer;
in the step 4.1, the pearson correlation coefficient is adopted to perform correlation analysis between the health index data and the capacity, and the correlation coefficient between the health index data and the capacity is calculated, wherein the calculation formula of the correlation coefficient r is as follows:
wherein N is the total charge and discharge times, C i HI for the i-th cycle battery capacity i Is the ith periodic health index;
step 5, collecting on-line signals, extracting health indexes, estimating capacity by adopting a BP neural network algorithm, and calculating a battery SOH value;
the specific process of the step 5 is as follows:
step 5.1, collecting charging voltage, current and time in the constant voltage charging process of the electric automobile by adopting a voltage and current sensor and a time meter on line;
step 5.2, extracting the constant voltage charging time in step 5.1 as a health index according to the method of step 2 to obtain a time sequence HI 'of the current health index' i ；
Step 5.3, time series HI 'of the current health index obtained in the step 5.2' i Inputting the current battery capacity sequence C 'obtained by performing capacity estimation in the relation between the health index and the lithium ion battery capacity established in the step 4.2' k ：
Step 5.4, calculating the SOH of the current battery according to the SOH definition formula:
C N is the nominal capacity of the battery, C' k Is the current battery capacity sequence.
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| CN114693043A (en) * | 2020-12-31 | 2022-07-01 | 奥动新能源汽车科技有限公司 | Method, system, electronic device, and medium for evaluating health condition of vehicle battery |
| US12044737B2 (en) * | 2021-02-08 | 2024-07-23 | Hong Kong Applied Science and Technology Research Institute Company Limited | Fast screening of rechargeable batteries using sectional constant-current impulse ratio (SCCIR) calibration with constant-current followed by constant-voltage charging |
| CN113109715B (en) * | 2021-03-29 | 2022-07-05 | 东华大学 | Battery health condition prediction method based on feature selection and support vector regression |
| CN113391214A (en) * | 2021-07-30 | 2021-09-14 | 湖北工业大学 | Battery micro-fault diagnosis method based on battery charging voltage ranking change |
| CN113740736B (en) * | 2021-08-31 | 2024-04-02 | 哈尔滨工业大学 | Electric vehicle lithium battery SOH estimation method based on deep network self-adaption |
| CN113721151B (en) * | 2021-11-03 | 2022-02-08 | 杭州宇谷科技有限公司 | Battery capacity estimation model and method based on double-tower deep learning network |
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