CN111965559A - Lithium ion battery SOH online estimation method - Google Patents
Lithium ion battery SOH online estimation method Download PDFInfo
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Abstract
The invention discloses an online estimation method of lithium ion battery SOH, which specifically comprises the following steps: step 1, acquiring voltage, current and time in the constant voltage charging process of an electric vehicle battery and current, time and capacity in the constant current discharging process of the electric vehicle battery in an off-line manner; step 2, extracting the constant voltage charging time adopted in the step 1 as a health index; step 3, adopting Layida criterion to preprocess the health index data; 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 can not realize online estimation application by taking the capacity or the internal resistance as a health index, and the dynamic change characteristic of the electric automobile in the actual working process can not be fully considered due to the fact that the parameter acquisition of the filtering method needs a fixed charging and discharging mode.
Description
Technical Field
The invention belongs to the technical field of lithium batteries, and relates to an online estimation method for SOH of a lithium ion battery.
Background
The lithium ion battery has the advantages of high specific energy, high working voltage, wide temperature range, low self-discharge rate, long cycle life, good safety and the like, so that the lithium ion battery is widely applied to electric vehicles and various fields and gradually becomes a key and support for important fields in the future. However, the degradation of the performance of the lithium battery in the using process can affect the normal use of the instrument and equipment, and even can cause serious fault accidents, generally, the state of health (SOH) of the lithium battery refers to the ratio of the measured capacity to the rated capacity, and the SOH of the lithium battery can be estimated to optimize the using strategy of the battery and prolong the performance decay period of the battery; in addition, the damaged 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 batteries SOH are estimated to be of particular importance.
Internal resistance, capacity, and charge-discharge behavior and cycle number are the subjects of lithium ion battery SOH estimation studies, however these studies are typically used for offline SOH estimation method validation. Some researchers propose to extract a Health Indicator (HI) from the charging and discharging behaviors and the cycle number, establish a mapping relation between the HI and the SOH, and use the mapping relation for real-time online SOH estimation. However, the charging and discharging behaviors and the cycle times of the electric vehicle are different in actual operation, and the method is not suitable for online SOH estimation of the lithium ion battery of the electric vehicle.
The discharging process of the electric automobile is random, but the charging process of the electric automobile is regular, the charging of the electric automobile is generally divided into three stages of constant-current charging, constant-voltage charging and floating charging, the characteristic that the constant-current charging time is short is not obvious enough, the charging starting points of the electric automobiles are different, and the monitoring information error is large. The floating charge characteristic is almost unchanged, so only the constant voltage charge stage information is completely retained, and the initial state and the termination state are the same and are not influenced by the previous incomplete discharge process.
Most of the existing methods for estimating the SOH of the battery of the electric automobile by taking the capacity or the internal resistance as the health index cannot realize online estimation application, and the problem that the dynamic change characteristic of the electric automobile in the actual working process cannot be fully considered due to the fact that the parameter acquisition of the filtering method needs a fixed charging and discharging mode.
Disclosure of Invention
The invention aims to provide an online estimation method for SOH of a lithium ion battery, which solves the problems that the conventional SOH estimation method for the battery of an electric vehicle cannot realize online estimation application by taking capacity or internal resistance as a health index, and dynamic change characteristics of the electric vehicle in the actual working process cannot be fully considered due to the fact that parameters of a filtering method need to be fixed in a charging and discharging mode.
The invention adopts the technical scheme that an online estimation method for SOH of a lithium ion battery is implemented according to the following steps:
step 1, acquiring voltage, current and time in the constant voltage charging process of an electric vehicle battery and current, time and capacity in the constant current discharging process of the electric vehicle battery in an off-line manner;
step 2, extracting the constant voltage charging time adopted in the step 1 as a health index;
step 3, adopting Layida criterion to preprocess the health index data;
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;
after the electric automobile is fully charged, the current sensor and the time timer are adopted to collect the current and the time of the lithium ion battery of the electric automobile in the constant current discharging process, and the battery capacity is obtained by multiplying the current and the time in the constant current discharging process.
The step 2 specifically comprises the following steps:
calculating a constant voltage charging time health index time sequence, wherein the corresponding constant voltage discharging time sequence in the ith period is the health index HI of the ith periodiComprises the following steps:
HIi＝tCVCend(i)-tCVCstart(i),i＝1,2,3,... (1)
in the formula, tCVCstartAnd tCVCendThe starting time and the ending time of the constant voltage charging of the ith period are respectively.
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
Wherein n is the total number of health index,
is the arithmetic mean of the health indicators;
step 3.2, judging and correcting the health index data according to Lauda criterion, wherein the Lauda criterion is judged according to the following steps:
in the formula, RnIs a residual error that is a function of the error,
is a health index estimate;
if the residual error is greater than 3 sigma, the health index data is a coarse error and should be discarded or compensated, and if the residual error is less than or equal to 3 sigma, the health index data is normal data and is reserved.
The step 4 specifically comprises the following steps:
step 4.1, performing correlation analysis between the health index data and the capacity by adopting a Pearson correlation coefficient, calculating the correlation coefficient between the health index data and the capacity, and if the correlation coefficient is more than 0.6, performing strong correlation, and determining that the health index time sequence is effective;
step 4.2, taking the effective health index time sequence proved by the 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 relationship between the health index and the lithium ion battery capacity, and training to obtain the network parameter omegaij、ωjk、ajAnd bkThe method specifically comprises the following steps:
in the formula, CkFor the battery capacity sequence, i is the number of nodes of the input layer, j is the number of nodes of the hidden layer, k is the number of nodes of the output layer, wherein the number of nodes of the input layer and the output layer is equal, so i is k, omegaijFor the input layer and the hidden layer connection weight, ajConnecting thresholds, ω, for input and hidden layersjkFor the input layer and the hidden layer connection weight, bkThe thresholds are connected for the input layer and the hidden layer.
In step 4.1, a Pearson correlation coefficient is adopted to carry out 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 number of charge and discharge, CiFor the ith cycle battery capacity, HIiIs the health index of the ith cycle.
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 using a voltage and current sensor and a time timer on line;
step 5.2, extracting the constant voltage charging time in the step 5.1 as a health index according to the method in the step 2 to obtain a time sequence HI 'of the current health index'i；
Step 5.3, obtaining the time sequence HI 'of the current health index obtained in the step 5.2'iInputting the health index and the lithium ion battery capacity relationship established in the step 4.2, and estimating the capacity to obtain a current battery capacity sequence C'k：
And 5.4, calculating to obtain the SOH of the current battery according to the SOH definition formula:
CNis the nominal capacity, C'kIs the current battery capacity sequence.
The invention has the advantages that
The method can effectively avoid the influence of the battery cycle number and the fixed charging and discharging mode on the filtering method, and solves the problems that most methods using capacity or internal resistance as health indexes in the conventional electric vehicle battery SOH estimation method cannot realize online estimation application, and the dynamic change characteristic of the electric vehicle in the actual working process cannot be fully considered due to the fact that parameters of the filtering method need to be fixed in the charging and discharging mode. 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 Lauda criterion method; secondly, analyzing the health index of the Pearson correlation coefficient and the battery capacity, and establishing a mapping relation between the health index and the actual capacity of the battery by using a BP neural network algorithm; and finally, carrying out SOH estimation by adopting a BP neural network algorithm.
Drawings
FIG. 1 is a general flow chart of an online estimation method of SOH of a lithium ion battery according to the present invention;
FIG. 2 is a data acquisition diagram of an offline lithium ion battery in an online estimation method of SOH of the lithium ion battery according to the present invention;
FIG. 3 is a diagram of an online lithium ion battery health indicator HI extraction method in an online lithium ion battery SOH estimation method according to the present invention;
FIG. 4 is a flow chart of the processing of abnormal points of the health index HI in the lithium ion battery SOH online estimation method of the present invention;
FIG. 5 is a diagram of a lithium ion battery capacity mapping model and a health index HI in the lithium ion battery SOH online estimation method according to the present invention;
FIG. 6 is a diagram of an online SOH estimation model of a lithium ion battery according to the method for online SOH estimation of a lithium ion battery of the present invention;
FIG. 7 is a comparison graph of the health index HI and the lithium ion battery unprocessed at the abnormal point in the online estimation method for the SOH of the lithium ion battery of the present invention;
FIG. 8 is a comparison graph of the health index HI and the lithium ion battery after abnormal point processing in the online estimation method of the SOH of the lithium ion battery of the present invention;
fig. 9 is a simulation diagram of online estimation of SOH of a lithium ion battery in the method for online estimation of SOH of a lithium ion battery of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention discloses an online estimation method of SOH of a lithium ion battery, the flow of which is shown in figure 1 and is specifically implemented according to the following steps:
step 1, acquiring voltage, current and time in the constant voltage charging process of an electric vehicle battery and current, time and capacity in the constant current discharging process of the electric vehicle battery 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;
after the electric automobile is fully charged, the current sensor and the time timer are adopted to collect the current and the time of the lithium ion battery of the electric automobile in the constant current discharging process, and the battery capacity is obtained by multiplying the current and the time in the constant current discharging process.
Step 2, extracting the constant voltage charging time adopted in the step 1 as a health index; as shown in fig. 3, specifically:
calculating a constant voltage charging time health index time sequence, wherein the corresponding constant voltage discharging time sequence in the ith period is the health index HI of the ith periodiComprises the following steps:
HIi＝tCVCend(i)-tCVCstart(i),i＝1,2,3,... (1)
in the formula, tCVCstartAnd tCVCendThe starting time and the ending time of the constant voltage charging of the ith period are respectively.
Step 3, adopting Layida criterion to preprocess the health index data; 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
Wherein n is the total number of health indicators, generally not less than 20,
is the arithmetic mean of the health indicators;
step 3.2, judging and correcting the health index data according to Lauda criterion, wherein the Lauda criterion is judged according to the following steps:
in the formula, RnIs a residual error that is a function of the error,
is a health index estimate;
if the residual error is greater than 3 sigma, the health index data is a coarse error and should be discarded or compensated, and if the residual error is less 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, performing correlation analysis between the health index data and the capacity by adopting a Pearson correlation coefficient, calculating the correlation coefficient between the health index data and the capacity, and if the correlation coefficient is more than 0.6, performing strong correlation, and determining that the health index time sequence is effective; the correlation analysis between the health index data and the capacity is carried out by adopting a Pearson correlation coefficient, 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 number of charge and discharge, CiFor the ith cycle battery capacity, HIiIs the health index of the ith cycle;
step 4.2, taking the effective health index time sequence proved by the 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 relationship between the health index and the lithium ion battery capacity, and training to obtain the network parameter omegaij、ωjk、ajAnd bkThe method specifically comprises the following steps:
in the formula, CkFor the battery capacity sequence, i is the number of nodes of the input layer, j is the number of nodes of the hidden layer, k is the number of nodes of the output layer, wherein the number of nodes of the input layer and the output layer is equal, so i is k, omegaijFor the input layer and the hidden layer connection weight, ajConnecting thresholds, ω, for input and hidden layersjkAs input layer and hidden layerInter connection weight, bkThe thresholds are connected for the input layer and the hidden layer.
step 5.1, collecting charging voltage, current and time in the constant-voltage charging process of the electric automobile by using a voltage and current sensor and a time timer on line;
step 5.2, extracting the constant voltage charging time in the step 5.1 as a health index according to the method in the step 2 to obtain a time sequence HI 'of the current health index'i；
Step 5.3, obtaining the time sequence HI 'of the current health index obtained in the step 5.2'iInputting the health index and the lithium ion battery capacity relationship established in the step 4.2, and estimating the capacity to obtain a current battery capacity sequence C'k：
And 5.4, calculating to obtain the SOH of the current battery according to the SOH definition formula:
CNis the nominal capacity, C'kIs 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, the battery adopts a commercially available 18650Li-ion battery, and the rated capacity is 2 Ah. Lithium ion batteries (B0005, B0006 and B0007) passed 3 different operating characteristics (charge, discharge and impedance) at room temperature. Charging in 1.5A Constant Current (CC) mode until the battery voltage reaches 4.2V, and then charging in Constant Voltage (CV) mode until the charging current drops to 20 mA.
Constant voltage charging voltage, current and time, and lithium ion battery capacity are extracted from the data. Fig. 7 shows a comparison of the health index HI and the capacity. It can be seen that errors occur in the actual extraction process and measurement, and the error point needs to be corrected, so that the data is corrected by using the Lauda criterion method, as shown in FIG. 8 after correction, the error point is already corrected, 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 correlation of health index HI and Battery Capacity
| Lithium ion battery | Before correction of abnormal point | After abnormal point correction |
| B0005 | -0.8551 | -0.9803 |
| B0006 | -0.8634 | -0.9474 |
| B0007 | -0.8380 | -0.9632 |
A BP neural network algorithm is adopted to establish mapping between three battery health indicators HI and battery capacity, and B0005 is taken as an example to perform online SOH estimation verification on the lithium ion battery, and the result is shown in fig. 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 of the invention is less than 2%, and the invention is suitable for the actual working condition of the electric automobile.
Claims (7)
1. An online estimation method for SOH of a lithium ion battery is characterized by comprising the following steps:
step 1, acquiring voltage, current and time in the constant voltage charging process of an electric vehicle battery and current, time and capacity in the constant current discharging process of the electric vehicle battery in an off-line manner;
step 2, extracting the constant voltage charging time adopted in the step 1 as a health index;
step 3, adopting Layida criterion to preprocess the health index data;
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.
2. The online estimation method of the SOH of the lithium ion battery according to claim 1, wherein the step 1 specifically comprises:
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, the current sensor and the time timer are adopted to collect the current and the time of the lithium ion battery of the electric automobile in the constant current discharging process, and the battery capacity is obtained by multiplying the current and the time in the constant current discharging process.
3. The online estimation method of the SOH of the lithium ion battery according to claim 1, wherein the step 2 specifically comprises:
calculating a constant voltage charging time health index time sequence, wherein the corresponding constant voltage discharging time sequence in the ith period is the health index HI of the ith periodiComprises the following steps:
HIi＝tCVCend(i)-tCVCstart(i),i＝1,2,3,...(1)
in the formula, tCVCstartAnd tCVCendThe starting time and the ending time of the constant voltage charging of the ith period are respectively.
4. The online estimation method of the SOH of the lithium ion battery according to claim 1, wherein the step 3 specifically comprises:
step 3.1, firstly, calculating the standard deviation sigma of the health index data, wherein the calculation formula is
Wherein n is the total number of health index,
is the arithmetic mean of the health indicators;
step 3.2, judging and correcting the health index data according to Lauda criterion, wherein the Lauda criterion is judged according to the following steps:
in the formula, RnIs a residual error that is a function of the error,
is a health index estimate;
if the residual error is greater than 3 sigma, the health index data is a coarse error and should be discarded or compensated, and if the residual error is less than or equal to 3 sigma, the health index data is normal data and is reserved.
5. The online estimation method of the SOH of the lithium ion battery according to claim 3, wherein the step 4 specifically comprises:
step 4.1, performing correlation analysis between the health index data and the capacity by adopting a Pearson correlation coefficient, calculating the correlation coefficient between the health index data and the capacity, and if the correlation coefficient is more than 0.6, performing strong correlation, and determining that the health index time sequence is effective;
step 4.2, taking the effective health index time sequence proved by the 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 relationship between the health index and the lithium ion battery capacity, and training to obtain the network parameter omegaij、ωjk、ajAnd bkThe method specifically comprises the following steps:
in the formula, CkFor the battery capacity sequence, i is the number of nodes of the input layer, j is the number of nodes of the hidden layer, k is the number of nodes of the output layer, wherein the number of nodes of the input layer and the output layer is equal, so i is k, omegaijFor the input layer and the hidden layer connection weight, ajConnecting thresholds, ω, for input and hidden layersjkFor the input layer and the hidden layer connection weight, bkThe thresholds are connected for the input layer and the hidden layer.
6. The method according to claim 5, wherein the step 4.1 of performing correlation analysis between the health index data and the capacity by using Pearson correlation coefficient, calculates the correlation coefficient between the health index data and the capacity, and calculates the correlation coefficient r by the following formula:
wherein N is the total number of charge and discharge, CiFor the ith cycle battery capacity, HIiIs the health index of the ith cycle.
7. The method for estimating the SOH of the lithium ion battery according to claim 6, wherein 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 using a voltage and current sensor and a time timer on line;
step 5.2, extracting the constant voltage charging time in the step 5.1 as a health index according to the method in the step 2 to obtain a time sequence HI 'of the current health index'i；
Step 5.3, obtaining the time sequence HI 'of the current health index obtained in the step 5.2'iInputting the health index and the lithium ion battery capacity relationship established in the step 4.2, and estimating the capacity to obtain a current battery capacity sequence C'k：
And 5.4, calculating to obtain the SOH of the current battery according to the SOH definition formula:
CNis the nominal capacity, C'kIs the current battery capacity sequence.
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