CN115799667A - Method and device for acquiring parameters of nickel-hydrogen battery of on-orbit satellite - Google Patents
Method and device for acquiring parameters of nickel-hydrogen battery of on-orbit satellite Download PDFInfo
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Abstract
The invention provides a method and a device for acquiring parameters of an in-orbit satellite hydrogen-nickel battery, relates to the field of in-orbit health management of a spacecraft, and can solve the problem that a Thevenin battery equivalent model in the related technology cannot evaluate the performance of the satellite hydrogen-nickel battery. The specific technical scheme is as follows: acquiring a second moment when the storage battery is in the discharging process and the satellite generates a total shadow; acquiring discharge current time sequence data and storage battery output voltage time sequence data between a first moment when a storage battery is in a discharge process and a satellite enters a silhouette and a second moment when the storage battery is in the discharge process and the satellite generates the silhouette; acquiring a plurality of groups of observed values according to a preset identification equation, the discharge current time series data and the storage battery output voltage time series data; establishing a multiple linear regression equation, and acquiring a regression coefficient corresponding to the multiple linear regression equation according to a least square regression principle; and acquiring the performance parameters of the storage battery according to the regression coefficients.
Description
Technical Field
The disclosure relates to the field of on-orbit health management of spacecrafts, in particular to a method and a device for acquiring parameters of a hydrogen-nickel battery of an on-orbit satellite.
Background
The hydrogen-nickel storage battery (hereinafter referred to as storage battery) has the advantages of long service life and storage life, high mass specific energy, high reliability and the like, and is widely used in a geosynchronous satellite energy system at present. Because the storage battery has the problems of charge and discharge management, performance degradation and the like, the detection of the working state, the performance degradation analysis, the residual life prediction and the like of the storage battery become the key of the research in the field of satellite fault prediction and health management. The on-line evaluation of the performance of the storage battery can obtain the charging and discharging capacities of the battery in different charge states, the relation between the optimal matching battery pack and the load performance is the core and the basis of battery management and residual electric quantity prediction, and the method has important theoretical significance and practical value for reasonably utilizing the battery to the maximum extent, avoiding the overcharge/overdischarge phenomenon of the battery and prolonging the service life of the battery.
The charging and discharging process of the storage battery is a complex chemical process, and factors such as temperature, pressure and the like can greatly influence the charging and discharging performance of the storage battery. Therefore, it is very difficult to accurately evaluate the on-orbit satellite battery performance. In general, a mixed pulse test is adopted for evaluating the performance of the storage battery on the ground, and information such as internal resistance, open-circuit voltage and SOC is obtained through a parameter identification method. However, for the on-orbit satellite, no measuring equipment is added in the use process of the storage battery, so that the performance estimation can be carried out only by utilizing the charge and discharge current and voltage data of the storage battery based on a certain storage battery model.
A common simulation model for a battery is the Thevenin model, as shown in FIG. 1, where I is the total current, I is p Is a current through a polarization resistor, U oc Is open circuit voltage, U L Is the load voltage. R p And C p The capacitance characteristics of the battery are described. When current passes through the loop, the terminal voltage of the battery changes abruptly and gradually, and the abrupt change is represented by the polarization resistance R p The gradual change is shown in the polarization capacitance C p The above. The mathematical model listed according to kirchhoff's law is shown in formula (1).
According to the mathematical expression of Thevenin model in the formula (1), the state equation of the discretization model can be obtained as
U L (k)+a 1 ·U L (k-1)＝U oc (k)+a 1 ·U oc (k-1)-b 1 ·I(k)-b 2 ·I(k-1)(2)
Wherein the content of the first and second substances,
where T is the sampling period of the data point. In practical application, the battery current I and the load voltage U L Is a known quantity, and can be seen from formula (2), R can be obtained by adopting a certain parameter identification method 0 、R p And C p 。
The above method is not practical to directly evaluate the performance of the battery of the orbit satellite. First, the battery telemetry parameters transmitted from the orbiting satellite include charge current, discharge current and discharge voltage, and the open circuit voltage data in equation (2) is not available. Secondly, the high-orbit satellite battery has a complex working mode, different charging and discharging processes are carried out in the earth shadow period, the semi-earth shadow period and the full-light period, and the traditional ground HPPC testing mode has no reference significance.
Disclosure of Invention
The embodiment of the disclosure provides a method and a device for acquiring parameters of an on-orbit satellite nickel-hydrogen battery, which can solve the problem that the equivalent model of the Thevenin battery in the related art cannot evaluate the performance of the satellite nickel-hydrogen battery. The technical scheme is as follows:
according to a first aspect of the embodiments of the present disclosure, there is provided a method for acquiring parameters of an in-orbit satellite nickel-hydrogen battery, the method including:
according to the time of the satellite in the penumbra and the total shadow period, a first time when the storage battery is in the discharging process and the satellite enters the total shadow and a second time when the storage battery is in the discharging process and the satellite generates the total shadow are obtained;
acquiring discharge current time-series data between the first moment and the second moment, and outputting voltage time-series data by a storage battery;
acquiring a plurality of groups of observed values according to a preset identification equation, the discharge current time sequence data and the storage battery output voltage time sequence data;
establishing a multiple linear regression equation according to the discharge current time series data and the storage battery output voltage time series data, and acquiring a regression coefficient corresponding to the multiple linear regression equation according to a least square regression principle;
and acquiring the performance parameters of the storage battery according to the regression coefficient.
The invention provides a method for acquiring parameters of an on-orbit satellite nickel-hydrogen battery, which comprises the following steps: according to the time of the satellite in the penumbra and the total shadow period, a first moment when the storage battery is in the discharging process and the satellite enters the total shadow and a second moment when the storage battery is in the discharging process and the satellite shows the total shadow are obtained; acquiring discharge current time-series data between a first moment and a second moment, and outputting voltage time-series data by a storage battery; acquiring a plurality of groups of observed values according to a preset identification equation, the discharge current time series data and the storage battery output voltage time series data; establishing a multiple linear regression equation according to the discharge current time series data and the storage battery output voltage time series data, and acquiring a regression coefficient corresponding to the multiple linear regression equation according to a least square regression principle; and acquiring the performance parameters of the storage battery according to the regression coefficients. On the basis of the conventional Thevenin battery equivalent model, the satellite current/voltage remote measurement parameter characteristic is combined, the parameter identification process is improved by setting a reasonable modeling time period, the adaptability of the identification method is enhanced, and effective battery performance parameters can be obtained.
In one embodiment, the expression of the predetermined identification equation is:
U L (k)+a 1 ·U L (k-1)＝(1+a 1 )·U oc -b 1 ·I d (k)-b 2 ·I d (k-1)；
wherein, I d (k) And I d (k-1) is data in the discharge current time-series data, { I d (t),t＝t s1 +i·t interval I =0,1,2, … N-1}, where t interval A time interval representing the discharge current time-series data,
points representing the discharge current time-series data; u shape L (k) And U L (k-1) data in the time-series data of the output voltage of the battery, { U L (t),t＝t s1 +i·t interval ,i＝0,1,2,…N-1}；U oc Is a preset open circuit voltage; a is 1 、b 1 And b 2 Is a preset coefficient.
In one embodiment, the obtaining multiple sets of observation values according to a preset identification equation, the discharge current time-series data and the storage battery output voltage time-series data includes:
acquiring N-1 groups of observed values according to a preset identification equation, the discharge current time series data and the storage battery output voltage time series data;
the expression of the N-1 set of observations is:
(U L (k),U L (k-1),I d (k),I d (k-1)),k＝2,......,N。
in one embodiment, the expression of the cubic multiple linear regression equation is:
Y＝β 1 +β 2 X 2 +β 3 X 3 +β 3 X 3 +β 4 X 4 +e；
wherein Y is a dependent variable corresponding to U L (k)；X 2 、X 3 、X 4 Are independent variables, each corresponding to U L (k-1)，I d (k) And I d (k-1)；β 1 、β 2 、β 3 、β 4 Is the regression coefficient, e is the random error and follows a normal distribution N (0, σ) 2 )。
In one embodiment, the obtaining the regression coefficient corresponding to the multiple linear regression equation according to the least squares regression principle includes:
according to
Acquiring an unbiased estimation value;
according to the above
Obtaining a regression coefficient corresponding to the multiple linear regression equation;
wherein the content of the first and second substances,
in one embodiment, the multiple linear regression equation corresponds to regression coefficients:
in one embodiment, the obtaining the battery performance parameter according to the regression coefficient includes:
obtaining the battery performance parameters according to the following formula:
wherein R is p Is a polarization resistance; c p Is a polarization capacitor; r 0 Is the ohmic internal resistance.
In one embodiment, after the obtaining the battery performance parameter according to the regression coefficient, the method further comprises:
and verifying the performance parameters of the storage battery in preset simulation software.
In one embodiment, the method further comprises:
and acquiring the preset open-circuit voltage according to the average value of the whole charging or discharging process of the storage battery.
According to a second aspect of the embodiments of the present disclosure, there is provided a parameter acquisition apparatus for an in-orbit satellite nickel-hydrogen battery, the apparatus including:
the first acquisition module is used for acquiring a first moment when the storage battery is in the discharging process and the satellite enters the full shadow and a second moment when the storage battery is in the discharging process and the satellite shows the full shadow according to the time when the satellite is in the penumbra and the full shadow period;
the second acquisition module is used for acquiring discharge current time series data between the first time and the second time and outputting voltage time series data by the storage battery;
the third acquisition module is used for acquiring a plurality of groups of observed values according to a preset identification equation, the discharge current time series data and the storage battery output voltage time series data;
the fourth acquisition module is used for establishing a multiple linear regression equation according to the discharge current time series data and the storage battery output voltage time series data and acquiring a regression coefficient corresponding to the multiple linear regression equation according to a least square regression principle;
and the fifth obtaining module is used for obtaining the storage battery performance parameters according to the regression coefficients.
In one embodiment, the expression of the predetermined identification equation is:
U L (k)+a 1 ·U L (k-1)＝(1+a 1 )·U oc -b 1 ·I d (k)-b 2 ·I d (k-1)；
wherein, I d (k) And I d (k-1) is data in the discharge current time-series data, { I d (t),t＝t s1 +i·t interval I =0,1,2, … N-1}, where t interval A time interval representing the discharge current time-series data,
points representing the discharge current time-series data; u shape L (k) And U L (k-1) data in the time-series data of the output voltage of the battery, { U L (t),t＝t s1 +i·t interval ,i＝0,1,2,…N-1}；U oc Is a preset open circuit voltage; a is 1 、b 1 And b 2 Is a preset coefficient.
In one embodiment, the third obtaining module includes:
the first acquisition submodule is used for acquiring N-1 groups of observed values according to a preset identification equation, the discharge current time series data and the storage battery output voltage time series data;
the expression of the N-1 set of observations is:
(U L (k),U L (k-1),I d (k),I d (k-1)),k＝2,......,N。
in one embodiment, the expression of the cubic multiple linear regression equation is:
Y＝β 1 +β 2 X 2 +β 3 X 3 +β 3 X 3 +β 4 X 4 +e；
wherein Y is a dependent variable corresponding to U L (k)；X 2 、X 3 、X 4 Are independent variables, each corresponding to U L (k-1)，I d (k) And I d (k-1)；β 1 、β 2 、β 3 、β 4 Is the regression coefficient, e is the random error and suitFrom a normal distribution N (0, σ) 2 )。
In one embodiment, the fourth obtaining module includes:
a second obtaining submodule for obtaining
Acquiring an unbiased estimation value;
a third obtaining submodule for obtaining a result of the first obtaining submodule
Obtaining a regression coefficient corresponding to the multiple linear regression equation;
wherein, the first and the second end of the pipe are connected with each other,
in one embodiment, the multiple linear regression equation corresponds to regression coefficients:
in one embodiment, the fifth obtaining module includes:
obtaining the battery performance parameters according to the following formula:
wherein R is p Is a polarization resistance; c p Is a polarization capacitor; r 0 Is the ohmic internal resistance.
In one embodiment, the apparatus further comprises:
and the verification module is used for verifying the performance parameters of the storage battery in preset simulation software.
In one embodiment, the apparatus further comprises:
and the sixth acquisition module is used for acquiring the preset open-circuit voltage according to the average value of the whole charging or discharging process of the storage battery.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a flowchart of a parameter acquisition method for an in-orbit satellite nickel-hydrogen battery according to an embodiment of the present disclosure;
FIG. 2 is a flowchart of a method for acquiring parameters of an in-orbit satellite nickel-hydrogen battery according to an embodiment of the disclosure;
FIG. 3 is a schematic diagram of discharge process telemetry data provided by an embodiment of the disclosure;
FIG. 4 is a circuit diagram of a simulation model provided by an embodiment of the present disclosure;
FIG. 5 is a diagram illustrating comparison results between simulated output voltages and actual output voltages provided by the embodiment of the disclosure;
FIG. 6 is a graph illustrating the corresponding relative error shown in FIG. 4 provided by an embodiment of the present disclosure;
fig. 7 is a structural diagram of a parameter acquisition device for an in-orbit satellite nickel-hydrogen battery according to an embodiment of the disclosure.
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 exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
Fig. 1 is a flowchart of a method for acquiring parameters of an in-orbit satellite nickel-hydrogen battery according to an embodiment of the present disclosure, as shown in fig. 1: the method comprises the following steps:
s101, acquiring a first time when a storage battery is in a discharging process and a satellite enters a full shadow and a second time when the storage battery is in the discharging process and the satellite generates the full shadow according to the time when the satellite is in a half shadow period and a full shadow period;
and extracting discharge current and output voltage data in a storage battery charging and discharging period. The charging process of the on-orbit satellite storage battery is slow, the current and voltage values are small, and the process is not suitable for parameter identification.
Selecting the storage battery discharge process to divide the storage battery discharge process according to the time of the satellite in the half shadow period and the full shadow period, and recording the discharge starting time of the storage battery as t s0 The first moment when the storage battery is in the discharging process and the satellite enters the total shadow is recorded as t s1 And the second moment when the storage battery is in the discharging process and the satellite generates the total shadow is recorded as t e1 And the end time of the battery discharge is recorded as t e0 。
S102, acquiring discharge current time-series data between a first moment and a second moment, and outputting voltage time-series data by a storage battery;
extracting t s1 To t e1 Discharge current time series data of time segments, denoted as { I } d (t),t＝t s1 +i·t interval I =0,1,2, … N-1}, where t interval The time interval at which the data is represented,
indicating the number of sequence data points. Similarly, the time sequence data of the output voltage of the storage battery is recorded as { U } L (t),t＝t s1 +i·t interval ,i＝0,1,2,…N-1}。
The steps are mainly executed as follows: battery discharge current, charge current, output voltage data preparation, and key time point (first time and second time) selection.
S103, acquiring a plurality of groups of observed values according to a preset identification equation, the discharge current time series data and the storage battery output voltage time series data;
in one embodiment, the predetermined recognition equation is expressed as:
U L (k)+a 1 ·U L (k-1)＝(1+a 1 )·U oc -b 1 ·I d (k)-b 2 ·I d (k-1)；
wherein, I d (k) And I d (k-1) data in discharge current time-series data, { I d (t),t＝t s1 +i·t interval I =0,1,2, … N-1}, where tinterval represents a time interval of discharge current time-series data,
points representing discharge current time-series data; u shape L (k) And U L (k-1) data in the time-series data of the output voltage of the storage battery, { U L (t),t＝t s1 +i·t interval ,i＝0,1,2,…N-1}；U oc -is a preset open circuit voltage; a is 1 、b 1 And b 2 Is a preset coefficient.
Where t equals time k.
R can be directly identified and obtained through discretization mathematical expression of Thevenin model 0 、R p And C p But wherein the open circuit voltage U is oc Is a variable with respect to time, and cannot be obtained during the in-orbit operation of the satellite, so that the formula (2) needs to be adaptively processed. Will U oc The average value of the entire charging or discharging process of the battery is characterized as a constant value, and equation (2) is then rewritten as:
U L (k)+a 1 ·U L (k-1)＝(1+a 1 )·U oc -b 1 ·I d (k)-b 2 ·I d (k-1)(3)
acquiring N-1 groups of observed values according to a preset identification equation (3), the discharge current time series data and the storage battery output voltage time series data;
the expression for the N-1 set of observations is:
(U L (k),U L (k-1),I d (k),I d (k-1)),k＝2,......,N。
s104, establishing a multiple linear regression equation according to the discharge current time series data and the storage battery output voltage time series data, and acquiring a regression coefficient corresponding to the multiple linear regression equation according to a least square regression principle;
the expression of the multiple linear regression equation is:
Y＝β 1 +β 2 X 2 +β 3 X 3 +β 3 X 3 +β 4 X 4 +e；
wherein Y is a dependent variable corresponding to U L (k)；X 2 、X 3 、X 4 Are independent variables, each corresponding to U L (k-1)，I d (k) And I d (k-1)；β 1 、β 2 、β 3 、β 4 Is the regression coefficient, e is the random error and follows a normal distribution N (0, σ) 2 )。
Solving the unbiased estimation value of the formula (3) according to the least square regression principle
Obtaining:
wherein the content of the first and second substances,
according to the least squares regression principle, it is easy to know
Is the regression coefficient [ beta ] 1 β 2 β 3 β 4 ] T By means of unbiased estimation, it is thus possible to:
and S105, acquiring the performance parameters of the storage battery according to the regression coefficient.
By least squares regressionResults
And the coefficient a in the formula (2) 1 、b 1 、b 2 And the relation between the capacity and the open-circuit voltage, the polarization resistance, the polarization capacitance and the ohmic resistance, the easily obtained performance parameters of the storage battery are as follows:
wherein R is p Is a polarization resistance; c p Is a polarization capacitor; r is 0 Is the ohmic internal resistance.
In one embodiment, after obtaining the battery performance parameters according to the regression coefficients, the battery performance parameters are verified in the preset simulation software.
For example, battery performance parameters may be verified in Simulink.
Specifically, according to the formula (2), a simulation model is established in Matlab Simulink. And reading the discharge current of the storage battery and the output telemetering data in a list form during each discharge process.
The discharge current is used as an input quantity, and the output voltage is used as an output quantity. Running simulation to output the simulated output voltage U s (t) and the actual output voltage U L (t) printing into Matlab space for comparative mapping analysis. And (4) analyzing the relative error delta between the simulated value and the actual value in a key way, and if the relative error is small, indicating that the parameter identification result is good.
The invention provides a method for acquiring parameters of an on-orbit satellite nickel-hydrogen battery, which comprises the following steps: according to the time of the satellite in the penumbra and the total shadow period, a first moment when the storage battery is in the discharging process and the satellite enters the total shadow and a second moment when the storage battery is in the discharging process and the satellite shows the total shadow are obtained; acquiring discharge current time-series data between a first moment and a second moment, and outputting voltage time-series data by a storage battery; acquiring a plurality of groups of observed values according to a preset identification equation, the discharge current time sequence data and the storage battery output voltage time sequence data; establishing a multiple linear regression equation according to the discharge current time series data and the storage battery output voltage time series data, and acquiring a regression coefficient corresponding to the multiple linear regression equation according to a least square regression principle; and acquiring the performance parameters of the storage battery according to the regression coefficients. According to the method, on the basis of the conventional Thevenin battery equivalent model, the satellite current/voltage telemetering parameter characteristics are combined, the parameter identification process is improved by setting a reasonable modeling time period, the adaptability of the identification method is enhanced, and effective battery performance parameters can be obtained.
The technical scheme of the disclosure is described in detail by the following embodiments:
the method is based on the previous Thevenin battery equivalent model, combines the satellite current/voltage remote measurement parameter characteristics, and improves the parameter identification process by setting a reasonable modeling time period, so that the adaptability of the identification method is enhanced, effective battery performance parameters can be obtained, the performance change analysis of the battery under long-term operation is carried out, and the simulation verification is carried out in Simulink.
As shown in fig. 2, the method specifically includes the following steps:
the first step is as follows: preparing data of discharging current, charging current and output voltage of the storage battery, and selecting a key time point;
specifically, the first step includes:
step 1.1: and extracting discharge current and output voltage data in a storage battery charging and discharging period. The charging process of the on-orbit satellite storage battery is slow, the current and voltage values are small, and the process is not suitable for parameter identification.
Step 1.2: selecting the storage battery discharge process to divide the storage battery discharge process according to the time of the satellite in the half shadow period and the full shadow period, and recording the discharge starting time of the storage battery as t s0 The storage battery is in the discharging process and the time when the satellite enters the total image is recorded as t s1 The storage battery is in the discharging process and the satellite full shadow moment is recorded as t e1 And the end time of the battery discharge is recorded as t e0 。
Step 1.3: extracting t s1 To t e1 Discharge current time series data of time segmentsIs marked as
{I d (t),t＝t s1 +i·t interval I =0,1,2, … N-1}, where tinterval represents a time interval of data,
indicating the number of sequence data points. Similarly, the time sequence data of the output voltage of the storage battery is recorded as { U } L (t),t＝t s1 +i·t interval ,i＝0,1,2,…N-1}。
The second step is that: establishing an identification state equation;
specifically, the second step includes:
step 2.1: r can be directly identified and obtained through discretization mathematical expression of Thevenin model 0 、R p And C p But wherein the open circuit voltage U is oc Is a variable with respect to time, and cannot be obtained during the in-orbit operation of the satellite, so that the formula (2) needs to be adaptively processed. Will U oc The average value of the entire charging or discharging process of the battery is represented as a constant value, and the formula (2) is rewritten to
U L (k)+a 1 ·U L (k-1)＝(1+a 1 )·U oc -b 1 ·I d (k)-b 2 ·I d (k-1)(3)
Step 2.2: from equation (3) and the discharge current and output voltage, N-1 sets of observations can be obtained:
(U L (k),U L (k-1),I d (k),I d (k-1)),k＝2,......,N(4)
the third step: obtaining identification parameters by least square fitting;
specifically, the third step includes:
step 3.1: establishing a multiple linear regression equation according to the formula (3):
Y＝β 1 +β 2 X 2 +β 3 X 3 +β 3 X 3 +β 4 X 4 +e(5)
in the formula, Y is a dependent variable corresponding to U L (k)；X 2 、X 3 、X 4 Is an independent variableRespectively correspond to U L (k-1)，I d (k) And I d (k-1)；β 1 、β 2 、β 3 、β 4 Is the regression coefficient, e is the random error and follows a normal distribution N (0, σ) 2 )。
Step 3.2: solving the regression coefficient of the formula (3) according to the least square regression principle
Obtaining:
wherein the content of the first and second substances,
step 3.3: according to the least squares regression principle, it is easy to know
Is the regression coefficient [ beta ] 1 β 2 β 3 β 4 ] T By means of unbiased estimation, it is thus possible to:
from least squares regression results
And the coefficient a in the formula (2) 1 、b 1 、b 2 And the relation between the voltage and the open-circuit voltage, the polarization resistance, the polarization capacitance and the ohmic resistance, the performance parameters of the easily obtained storage battery are as follows:
the fourth step: the parameter identification effect is verified in Simulink.
Specifically, the fourth step includes:
step 4.1: and (3) establishing a simulation model in Matlab Simulink according to the formula (2). And reading the discharge current of the storage battery and the output telemetering data in a list form during each discharge process.
Step 4.2: the discharge current is used as an input quantity, and the output voltage is used as an output quantity. Running simulation to output the simulated output voltage U s (t) and the actual output voltage U L (t) printing into Matlab space for comparative mapping analysis. And (4) analyzing the relative error delta between the simulated value and the actual value in a key way, and if the relative error is small, indicating that the parameter identification result is good.
The invention has the beneficial effects that:
1) Cell performance parameters can be obtained from on-orbit satellite battery discharge current and output voltage timing data.
2) The method is favorable for evaluating the comprehensive performance of the on-orbit satellite storage battery after long-term operation.
The invention is further illustrated by the following examples in conjunction with the drawings.
1) And (3) telemetering data of the nickel-hydrogen storage battery of a certain satellite in the discharging process of 2020-03-18, wherein a red line is output voltage, a blue line is discharging current, and a green line is charging current as shown in figure 3. The discharge process starts from 22.
2) And (4) selecting key time according to the step 1. Marking the discharge initial time t according to the conversion time from the half-shadow period to the full-shadow period of the satellite s0 22 s1 And 15 e1 23 e0 23. Wherein, the time t s1 To t e1 There are 991 sets of data points in between.
3) According to step 2, an identification process equation (3) is first established, and then data points I are obtained according to the discharge current and the output voltage d (t) and U L (t) 990 sets of observations can be obtained, i.e. (U) L (k),U L (k-1),I d (k),I d (k-1)),k＝2,......,991。
4) According to step 3, with U L (k) As a dependent variable, with U L (k-1)，I d (k) And I d And (k-1) is an independent variable, a least square regression coefficient is obtained according to the formula (6), and a storage battery performance parameter identification result is obtained according to the formula (8). In this example, U is calculated OC 、C p 、R 0 And R p 56.52V, 485.46F, 0.0224 Ω, and 0.671 Ω, respectively.
5) According to step 4.1, a simulation model is built on Simulink, as shown in fig. 4. A storage battery t s1 To t e1 The discharging current and output voltage sequence data in the time period are read in a list form and are placed in an Import _ G roup1, wherein Signal1 is the discharging current, and Signal2 is the output voltage.
6) And 4.2, operating the simulation model to obtain a comparison result of the simulation output voltage and the actual output voltage. With ts1 as the start time, drawing ts1 to t s1 The results in +3000s are shown in fig. 5, and the corresponding relative error is shown in fig. 6. Therefore, the maximum value of the relative error is not more than 1.2%, which indicates that the parameter identification result is good, and the obtained performance parameter U OC 、C p R0 and R p Is trusted.
Based on the method for acquiring the parameters of the nickel-hydrogen battery of the on-orbit satellite described in the embodiment corresponding to fig. 1, the following is an embodiment of the apparatus of the present disclosure, which can be used to implement the embodiment of the method of the present disclosure.
The embodiment of the present disclosure provides a parameter obtaining apparatus for nickel-hydrogen battery of on-orbit satellite, as shown in fig. 7,
the device comprises:
the first obtaining module 11 is configured to obtain a first time when the storage battery is in a discharging process and the satellite enters the full shadow and a second time when the storage battery is in the discharging process and the satellite shows the full shadow according to the time when the satellite is in the penumbra and the full shadow period;
a second obtaining module 12, configured to obtain discharge current time-series data between the first time and the second time, and output voltage time-series data of the storage battery;
the third acquisition module 13 is configured to acquire multiple sets of observation values according to a preset identification equation, the discharge current time-series data, and the storage battery output voltage time-series data;
a fourth obtaining module 14, configured to establish a multiple linear regression equation according to the discharge current time-series data and the storage battery output voltage time-series data, and obtain a regression coefficient corresponding to the multiple linear regression equation according to a least square regression principle;
and a fifth obtaining module 15, configured to obtain the battery performance parameter according to the regression coefficient.
In one embodiment, the expression of the predetermined identification equation is:
U L (k)+a 1 ·U L (k-1)＝(1+a 1 )·U oc -b 1 ·I d (k)-b 2 ·I d (k-1)；
wherein, I d (k) And I d (k-1) is data in the discharge current time-series data, { I d (t),t＝t s1 +i·t interval I =0,1,2, … N-1}, where t interval A time interval representing the discharge current time-series data,
points representing the discharge current time-series data; u shape L (k) And U L (k-1) data in the time-series data of the output voltage of the battery, { U L (t),t＝t s1 +i·t interval ,i＝0,1,2,…N-1}；U oc Is a preset open circuit voltage; a is a 1 、b 1 And b 2 Is a preset coefficient.
In one embodiment, the third obtaining module 13 includes:
the first obtaining submodule 131 is configured to obtain N-1 sets of observation values according to a preset identification equation, the discharge current time-series data, and the storage battery output voltage time-series data;
the expression of the N-1 observation groups is as follows:
(U L (k),U L (k-1),I d (k),I d (k-1)),k＝2,......,N。
in one embodiment, the expression of the cubic multiple linear regression equation is:
Y＝β 1 +β 2 X 2 +β 3 X 3 +β 3 X 3 +β 4 X 4 +e；
wherein Y is a dependent variable corresponding to U L (k)；X 2 、X 3 、X 4 Are independent variables, each corresponding to U L (k-1)，I d (k) And I d (k-1)；β 1 、β 2 、β 3 、β 4 Is the regression coefficient, e is the random error and follows a normal distribution N (0, σ) 2 )。
In one embodiment, the fourth obtaining module 14 includes:
a second obtaining submodule 141 for obtaining
Acquiring an unbiased estimation value;
a third obtaining submodule 142 for obtaining a value according to
Obtaining a regression coefficient corresponding to the multiple linear regression equation;
wherein the content of the first and second substances,
in one embodiment, the multiple linear regression equation corresponds to regression coefficients:
in one embodiment, the fifth obtaining module includes:
obtaining the battery performance parameters according to the following formula:
wherein R is p Is a polarization resistance; c p Is a polarization capacitor; r 0 Is the ohmic internal resistance.
In one embodiment, the apparatus further comprises:
and the verification module is used for verifying the performance parameters of the storage battery in preset simulation software.
In one embodiment, the apparatus further comprises:
and the sixth acquisition module is used for acquiring the preset open-circuit voltage according to the average value of the whole charging or discharging process of the storage battery.
Based on the method for acquiring parameters of the nickel-hydrogen battery for the on-orbit satellite described in the embodiment corresponding to fig. 1, the embodiment of the present disclosure further provides a computer-readable storage medium, for example, the non-transitory computer-readable storage medium may be a Read Only Memory (ROM), a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like. The storage medium stores computer instructions for executing the data transmission method described in the embodiment corresponding to fig. 1, which is not described herein again.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A method for acquiring parameters of an in-orbit satellite nickel-hydrogen battery is characterized by comprising the following steps:
according to the time of the satellite in the penumbra and the total shadow period, a first moment when the storage battery is in the discharging process and the satellite enters the total shadow and a second moment when the storage battery is in the discharging process and the satellite shows the total shadow are obtained;
acquiring discharge current time-series data between the first moment and the second moment, and outputting voltage time-series data by a storage battery;
acquiring a plurality of groups of observed values according to a preset identification equation, the discharge current time series data and the storage battery output voltage time series data;
establishing a multiple linear regression equation according to the discharge current time series data and the storage battery output voltage time series data, and acquiring a regression coefficient corresponding to the multiple linear regression equation according to a least square regression principle;
and acquiring the performance parameters of the storage battery according to the regression coefficient.
2. The method of claim 1, wherein the predetermined identification equation is expressed as:
U L (k)+a 1 ·U L (k-1)＝(1+a 1 )·U oc -b 1 ·I d (k)-b 2 ·I d (k-1)；
wherein, I d (k) And I d (k-1) is data in the discharge current time-series data, { I d (t)，t＝t s1 +i·t interval I =0,1,2, … N-1}, where t interval A time interval representing the discharge current time-series data,
points representing the discharge current time-series data; u shape L (k) And U L (k-1) data in the time-series data of the output voltage of the storage battery, { U L (t)，t＝t s1 +i·t interval ，i＝0，1，2，…N-1}；U oc Is a preset open circuit voltage; a is 1 、b 1 And b 2 Is a preset coefficient.
3. The method of claim 2, wherein the obtaining a plurality of sets of observations from a preset identification equation, the discharge current time-series data, and the battery output voltage time-series data comprises:
acquiring N-1 groups of observed values according to a preset identification equation, the discharge current time series data and the storage battery output voltage time series data;
the expression of the N-1 observation groups is as follows:
(U L (k)，U L (k-1)，I d (k)，I d (k-1))，k＝2，......，N。
4. the method of claim 3, wherein the expression of the multivariate linear regression equation is:
Y＝β 1 +β 2 X 2 +β 3 X 3 +β 3 X 3 +β 4 X 4 +e；
wherein Y is a dependent variable corresponding to U L (k)；X 2 、X 3 、X 4 Are independent variables, each corresponding to U L (k-1)，I d (k) And I d (k-1)；β 1 、β 2 、β 3 、β 4 Is the regression coefficient, e is the random error and follows a normal distribution N (0, σ) 2 )。
5. The method according to claim 4, wherein the obtaining the regression coefficients corresponding to the multiple linear regression equation according to the least squares regression principle comprises:
according to
Acquiring an unbiased estimation value;
according to the above
Obtaining a regression coefficient corresponding to the multiple linear regression equation;
wherein the content of the first and second substances,
6. the method of claim 5, wherein the multiple linear regression equation corresponds to regression coefficients of:
7. the method of claim 6, wherein said obtaining the battery performance parameter from the regression coefficient comprises:
obtaining the battery performance parameters according to the following formula:
wherein R is p Is a polarization resistance; c p Is a polarization capacitor; r is 0 Is the ohmic internal resistance.
8. The method of claim 1, wherein after obtaining the battery performance parameter from the regression coefficient, the method further comprises:
and verifying the performance parameters of the storage battery in preset simulation software.
9. The method of claim 2, further comprising:
and acquiring the preset open-circuit voltage according to the average value of the whole charging or discharging process of the storage battery.
10. An on-orbit satellite hydrogen-nickel battery parameter acquisition device is characterized by comprising:
the first acquisition module is used for acquiring a first moment when the storage battery is in the discharging process and the satellite enters the full shadow and a second moment when the storage battery is in the discharging process and the satellite shows the full shadow according to the time when the satellite is in the penumbra and the full shadow period;
the second acquisition module is used for acquiring discharge current time series data between the first time and the second time and outputting voltage time series data by the storage battery;
the third acquisition module is used for acquiring a plurality of groups of observed values according to a preset identification equation, the discharge current time series data and the storage battery output voltage time series data;
the fourth acquisition module is used for establishing a multiple linear regression equation according to the discharge current time series data and the storage battery output voltage time series data and acquiring a regression coefficient corresponding to the multiple linear regression equation according to a least square regression principle;
and the fifth obtaining module is used for obtaining the storage battery performance parameters according to the regression coefficients.
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