CN108072848B - Analysis method for estimating discharge time of storage battery - Google Patents
Analysis method for estimating discharge time of storage battery Download PDFInfo
- Publication number
- CN108072848B CN108072848B CN201810098482.7A CN201810098482A CN108072848B CN 108072848 B CN108072848 B CN 108072848B CN 201810098482 A CN201810098482 A CN 201810098482A CN 108072848 B CN108072848 B CN 108072848B
- Authority
- CN
- China
- Prior art keywords
- discharge
- discharge curve
- curve
- storage battery
- fitted
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Secondary Cells (AREA)
Abstract
The embodiment of the invention relates to an analysis method for estimating the discharge time of a storage battery, which comprises the following steps: acquiring first discharge data of a first storage battery in the whole complete discharge process; generating a first discharge curve according to the first discharge data and the acquisition time; classifying according to the first discharge curve and curve similarity to obtain a discharge curve group; performing curve fitting on each discharge curve group to generate a fitted discharge curve of the discharge curve group; collecting second discharge data of a second storage battery in the whole partial discharge process; generating a second discharge curve section according to the second discharge data and the acquisition time; matching the second discharge curve segment with a plurality of fitting discharge curves to obtain a first fitting discharge curve with the highest matching degree; and obtaining the remaining service time of the second storage battery according to the first fitted discharge curve and the second discharge curve section.
Description
Technical Field
The invention relates to the field of storage batteries, in particular to an analysis method for estimating the discharge time of a storage battery.
Background
The storage battery has a plurality of application scenes, for example, the storage battery can be used as an important component of a base station power supply, and for a base station and a machine room, when alternating current is input, the switch power supply simultaneously supplies power to a load and the storage battery; when no AC input exists, the switch power supply controls the storage battery to supply power to the load; when the electric quantity of the storage battery is exhausted and the alternating current input is still not recovered, in order to ensure that the load is continuously supplied with power, the generator is manually carried to the base station to generate power. Once the base station quits service, immeasurable direct economic loss and huge negative effects are brought to operators, so that accurate prediction of the dischargeable time of the base station storage battery pack is particularly important.
The existing methods for predicting the dischargeable time of a battery pack mainly include the following three methods.
The first is to rely on natural power failure, when the base station has power failure records and the power failure time is long enough, the first-line personnel can count the dischargeable duration value of the base station storage battery for standby by means of memory or manual operation. The method has the disadvantages that the existing network stock and the newly-built base stations are huge in quantity, a large amount of labor cost and time cost are consumed, and the error is large.
The second method is that the personnel who are responsible for maintaining the base station for a long time depend on the experience of the personnel, and the dischargeable time of the personnel is estimated according to the discharge condition of the storage battery during the natural power failure by means of years of storage battery maintenance experience. The method has the defects that field maintainers have high mobility and low experience reproduction rate, the field maintainers are only familiar with the performance condition of the base station storage battery in the self-maintained area, once the area is replaced, the experience is almost zero, and batch reproduction and large-area application cannot be realized; and a large amount of labor cost and time cost are consumed, and the error of the obtained estimated result is large.
And thirdly, a base station management maintainer is used for regularly carrying out full-capacity discharge on the storage battery on each base station and recording the discharge time in the full-capacity discharge process, and the method has the defects that the base station is huge in base number, the maintainer is limited, large-batch full-capacity discharge tests are difficult to carry out, and a large amount of labor cost and time cost are consumed.
Disclosure of Invention
The invention aims to provide an analysis method for estimating the discharge time of a storage battery, which classifies the discharge curve of a completely discharged storage battery to obtain fitted discharge curves of different line types, and then matches a partial discharge curve of the storage battery with the fitted discharge curve to estimate the time to be estimated, so that the dischargeable time of the storage battery can be accurately estimated; and the dischargeable time can be corrected through the correction parameters, and the accuracy and the effectiveness of the estimated dischargeable time are guaranteed.
In order to achieve the above object, the present invention provides an analysis method for estimating a discharge time of a storage battery, the method comprising:
acquiring first discharge data of a first storage battery in the whole complete discharge process;
generating a first discharge curve according to the first discharge data and the acquisition time;
classifying according to the first discharge curve and curve similarity to obtain a discharge curve group;
performing curve fitting on each discharge curve group to generate a fitted discharge curve of the discharge curve group;
collecting second discharge data of a second storage battery in the whole partial discharge process;
generating a second discharge curve section according to the second discharge data and the acquisition time;
matching the second discharge curve segment with a plurality of fitting discharge curves to obtain a first fitting discharge curve with the highest matching degree;
and obtaining the remaining service time of the second storage battery according to the first fitted discharge curve and the second discharge curve section.
Preferably, before the matching processing is performed on the second discharge curve segment and the plurality of fitted discharge curves to obtain the first fitted discharge curve with the highest matching degree, the method further includes:
and segmenting the fitted discharge curve to obtain a stable-period fitted discharge curve segment and a decay-period fitted discharge curve segment.
Preferably, the acquiring of the second discharge data in the whole partial discharge process of the second storage battery specifically includes:
and acquiring second discharge data of the second storage battery in a partial discharge process from full charge.
Further preferably, the matching processing of the second discharge curve segment and the plurality of fitted discharge curves to obtain the first fitted discharge curve with the highest matching degree specifically includes:
segmenting the second discharge curve segment;
when the second discharge curve segment is a stable-period discharge curve segment, matching the second discharge curve segment with stable-period fitted discharge curve segments of a plurality of fitted discharge curves to obtain a first fitted discharge curve with the highest matching degree;
and when the second discharge curve segment comprises a stable-period discharge curve segment and an attenuation-period discharge curve segment, matching the second discharge curve segment with the stable-period fitting discharge curve segment and the attenuation-period fitting discharge curve segment of the plurality of fitting discharge curves to obtain a first fitting discharge curve with the highest matching degree.
Preferably, the matching processing of the second discharge curve segment and the plurality of fitted discharge curves to obtain the first fitted discharge curve with the highest matching degree specifically includes:
and matching the second discharge curve section with any part of the plurality of fitting discharge curves to obtain a first fitting discharge curve with the highest matching degree.
Preferably, after the curve-fitting each discharge curve group to generate a fitted discharge curve of the discharge curve group, the method further includes:
and verifying the fitted discharge curve to obtain the corresponding correction parameters of the fitted discharge curve.
Further preferably, the verifying the fitted discharge curve to obtain the correction parameters corresponding to the fitted discharge curve specifically includes:
cutting the first discharge curve of the same discharge curve group to obtain a first discharge curve section;
obtaining corresponding estimated remaining service time according to the first discharge curve segment and the corresponding fitting discharge curve;
obtaining a ratio of estimated-actual residual service time according to the estimated residual service time and the actual residual service time;
and obtaining correction parameters corresponding to the fitted discharge curve of the discharge curve group according to a plurality of estimated-actual remaining use time ratios obtained by the same discharge curve group.
Further preferably, after the cutting the first discharge curve of the same discharge curve group to obtain a first discharge curve segment, the method further includes:
and obtaining the actual remaining service time according to the first discharge curve and the first discharge curve section.
Further preferably, after the obtaining the remaining service time of the second storage battery according to the first fitted discharge curve and the second discharge curve segment, the method further includes:
acquiring correction parameters corresponding to the first fitting discharge curve;
and revising the residual service time according to the correction parameters.
Preferably, the discharge data includes a voltage, a current, and a temperature of the secondary battery.
According to the analysis method for estimating the discharge time of the storage battery, provided by the embodiment of the invention, the discharge curve of the completely discharged storage battery is classified to obtain the fitting discharge curves of different line types, and then the partial discharge curve of the storage battery with the time to be estimated is matched with the fitting discharge curve, so that the dischargeable time of the storage battery can be accurately estimated; and the dischargeable time can be corrected through the correction parameters, and the accuracy and the effectiveness of the estimated dischargeable time are guaranteed.
Drawings
FIGS. 1 a-1 c are discharge curves of a battery according to an embodiment of the present invention;
FIG. 2 is a flowchart of an analysis method for estimating a discharge duration of a storage battery according to an embodiment of the present invention;
fig. 3 a-3 c are alternative battery discharge curves provided by embodiments of the present invention.
Detailed Description
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
For the understanding of the technical solution of the present invention, the principle of the present invention will be described first.
The dischargeable duration of the storage battery can be obtained by a voltage-time curve, wherein the voltage is the discharge voltage of the storage battery, and the time is the moment corresponding to the voltage. The trend of the curve is that the discharge voltage gradually decreases as the time increases during the entire discharge. When the discharge voltage drops to a preset voltage, such as 46.5V, the charge is considered to be exhausted. According to curve data before a certain moment (at the moment, the voltage is U & gt 46.5V), the curve trend after the moment is predicted in a machine learning mode, and the time for discharging from the moment to the voltage of 46.5V is obtained within a certain error.
Theoretically, for a group of good-performing batteries, the voltage-time curve from the beginning of discharge to the predetermined voltage is nearly linear, but in the actual operation process, the discharge curve of the battery is various, specifically as shown in fig. 1, including but not limited to linear, parabolic-like, "zigzag" and other curves. And the line shape of the curve is determined by the performance of the battery. In the discharging process of the storage battery, the discharging data of the storage battery is tracked, and the performance of the storage battery can be judged by using the discharging data which are generated by using a machine learning method to a certain extent, so that the trend of the next discharging curve of the storage battery is predicted, and the residual discharging time of the storage battery is obtained.
The analysis method for estimating the discharge time of the storage battery is realized based on the principle, and fig. 2 is a flow chart of the analysis method for estimating the discharge time of the storage battery provided by the embodiment of the invention. As shown in fig. 2, the analysis method for estimating the discharge time of the storage battery provided in this embodiment includes the following steps:
it should be noted that, the first storage battery referred to in the present invention refers to a storage battery that completes a complete discharge process from a full charge state, and the storage battery includes, but is not limited to, a lead-acid battery and a lithium battery; the first discharge data refers to discharge data of the first storage battery, and the discharge data includes, but is not limited to, voltage, current, temperature and acquisition time data. The storage Battery can be a Battery installed on the base station, the number of the first storage batteries can be multiple, the storage Battery which completes the complete discharging process from the full-charged state can be the first storage Battery, and the first discharging data in the complete discharging process of the storage Battery can be collected through a Battery Management System (BMS) of the base station.
102, generating a first discharge curve according to the first discharge data and the acquisition time;
specifically, the collected first discharge data is processed into a first discharge curve of the first storage battery, and the first discharge curve includes, but is not limited to, a voltage-time curve, a current-time curve, a temperature-time curve, and a mixed parameter curve shown in the same figure. For the sake of simplicity and clarity of the discharge curve of the battery, the first discharge curve is preferably a voltage-time curve.
103, classifying according to the first discharge curve and curve similarity to obtain a discharge curve group;
in a specific example, the obtained voltage-time graphs of the plurality of first storage batteries are classified according to the similarity of curve line types, so that different discharge curve groups are obtained, wherein the curve types include but are not limited to a straight type, a parabola-like type and a zigzag type. It should be noted that, in the classification process, if a curve with a similar line shape cannot be found, the curve is separately classified into a group.
104, performing curve fitting on each discharge curve group to generate a fitted discharge curve of the discharge curve group;
specifically, various algorithms can be applied to perform curve fitting on the plurality of discharge curves of each discharge curve group through a machine learning means, so as to generate a fitted discharge curve corresponding to the discharge curve group.
In order to ensure the completeness and the comprehensiveness of the obtained line type, as long as the BMS of the base station collects complete discharge data, a first discharge curve is generated, the new first discharge curve is subjected to line type matching with the fitting discharge curve, if no fitting discharge curve matched with the new first discharge curve line type exists, the new first discharge curve is separately divided into a group, and a corresponding fitting discharge curve is generated, so that the fitting discharge curve of the new line type is continuously increased and accumulated, and the completeness and the comprehensiveness of the fitting discharge curve are ensured.
105, collecting second discharge data in the whole partial discharge process of the second storage battery;
the second battery referred to in the present invention is a battery whose remaining discharge duration is to be predicted, and the battery is only partially discharged but not fully discharged.
In a specific example, in order to facilitate management of the storage battery of the base station, the dischargeable time of the storage battery needs to be known, and specifically, a remote partial discharge method may be adopted to perform remote partial discharge on the storage battery, and second discharge data in a partial discharge process of the storage battery is collected by the BMS of the base station.
When collecting the second discharge data of the second storage battery in the partial discharge process, the two situations can be divided according to the current electric quantity state of the second storage battery: one is to collect the second discharge data of the second storage battery in the partial discharge process from the full charge; the other is to collect second discharge data in the partial discharge process of the second storage battery from the non-full charge.
106, generating a second discharge curve section according to the second discharge data and the acquisition time;
similar to step 102, the collected second discharge data is processed into a second discharge curve segment of the second battery, including, but not limited to, a voltage-time curve, a current-time curve, a temperature-time curve, and a mixing parameter curve shown in the same figure. For a brief and clear illustration of the discharge curve of the battery, the second discharge curve section is preferably a voltage-time diagram.
in one particular example, the method heretofore further comprises: and segmenting the fitted discharge curve to obtain a stable-period fitted discharge curve segment and an attenuation-period fitted discharge curve segment, wherein the stable-period fitted discharge curve segment is a discharge curve segment corresponding to the stable discharge period of the storage battery, and the attenuation-period fitted discharge curve segment is a discharge curve segment after the stable discharge period of the storage battery.
According to the two conditions of the current state of charge of the second storage battery in step 105, when the storage battery starts to discharge from a full-charge state, the obtained second discharge curve segment is segmented, and the segmentation result according to the discharge time of the storage battery comprises two conditions: one is that the discharge time of the storage battery is short, and the obtained second discharge curve section is a discharge curve section in a stable period; and the other is that the storage battery is in a state of incomplete discharge after a longer discharge time, the second discharge curve segment comprises a stable-period discharge curve segment and a decay-period discharge curve segment, and the matching process of the fitting discharge curve is detailed for two segmentation results.
Specifically, if the second discharge curve segment is a stable-period discharge curve segment, the second discharge curve segment is matched with the stable-period fitting discharge curve segments of the plurality of fitting discharge curves to obtain the first fitting discharge curve with the highest matching degree.
And if the second discharge curve segment comprises a stable-period discharge curve segment and an attenuation-period discharge curve segment, matching the second discharge curve segment with the stable-period fitting discharge curve segment and the attenuation-period fitting discharge curve segment of the plurality of fitting discharge curves to obtain a first fitting discharge curve with the highest matching degree. In order to obtain the storage battery pre-estimated dischargeable time with high accuracy, the storage battery with the full-charge state of electric quantity is preferably subjected to remote discharge, the obtained discharge curve comprises a stable-period discharge curve section and a partial decay-period discharge curve section, and the stable-period discharge curve section and the partial decay-period discharge curve section are matched with the stable-period fitting discharge curve section and the decay-period fitting discharge curve section of the plurality of fitting discharge curves, so that a first fitting discharge curve with the highest matching degree is obtained.
When the storage battery is discharged from the non-full state, the second discharge curve segment obtained according to the collected second discharge data may be a partial stationary-period discharge curve segment, a partial decay-period discharge curve segment or a sum of the partial stationary-period and partial decay-period discharge curve segments, so that the second discharge curve segment is matched with any part of the plurality of fitted discharge curves to obtain a first fitted discharge curve with the highest matching degree.
And step 108, obtaining the remaining service time of the second storage battery according to the first fitted discharge curve and the second discharge curve section.
Specifically, various algorithms (such as a least square method) are used for processing and analyzing the second discharge curve segment and the first fitted discharge curve with the highest matching degree, and the remaining service time of the second storage battery is estimated. And adding the remote discharging time and the estimated remaining service time to obtain the total discharging time of the second storage battery.
In a preferred embodiment, to ensure the accuracy of the estimated remaining usage time, the estimated remaining usage time is corrected by the correction parameters, so that after curve-fitting each discharge curve group to generate a fitted discharge curve of the discharge curve group in step 104, the method further comprises: and verifying the fitted discharge curve to obtain the corresponding correction parameters of the fitted discharge curve.
Specifically, a first discharge curve of the same discharge curve group is cut to obtain a first discharge curve segment, and then the actual remaining service time is obtained according to the first discharge curve and the first discharge curve segment; obtaining corresponding estimated remaining service time according to the first discharge curve segment and the corresponding fitting discharge curve; and calculating according to the estimated residual service time and the actual residual service time to obtain the ratio of the estimated to the actual residual service time. In a specific example, the dischargeable time period corresponding to the first discharge curve is 8.5 hours, the first discharge curve segment within the discharge time period of 0-6 hours is selected, and the corresponding estimated remaining service time is 2.3 hours according to the discharge curve segment and the corresponding fitted discharge curve, so that the ratio of the estimated-actual remaining service time of the first discharge curve is 2.3/(8.5-6) ═ 0.92.
And processing the plurality of estimated-actual remaining use time ratios obtained by the same discharge curve group, including but not limited to calculating the average value of the plurality of estimated-actual remaining use time ratios, and obtaining the correction parameters corresponding to the fitted discharge curves of the discharge curve group, wherein each linear fitted discharge curve corresponds to one correction parameter. It should be noted that, in order to ensure the accuracy and the validity of the correction parameters, when a new first discharge curve with complete discharge is generated, the new first discharge curve is divided into linear types, and the corresponding fitting discharge curve is verified by the method, so that the correction parameters corresponding to the fitting discharge curve are updated, and the accuracy and the validity of the correction parameters of the fitting discharge curve are ensured.
After obtaining the remaining usage time of the second battery from the first fitted discharge curve and the second discharge curve segment at step 108, the method further comprises: obtaining a correction parameter corresponding to a first fitting discharge curve with the highest matching degree; and revising the residual service time according to the correction parameters, thereby obtaining more accurate residual dischargeable time.
In a specific example, the power supply equipment combination of the base station is a switching power supply and a storage battery pack, and the switching power supply plays a role in converting 380V alternating current into 48V direct current; the storage battery pack is usually a lead-acid battery, and a 48V system is formed by connecting 24 2V batteries in series. The method for predicting the residual dischargeable time of the base station storage battery mainly comprises the following steps:
first, screening of data is performed. Specifically, when machine learning is used, a training set and a test set are required, and a large amount of full discharge curve data is required as the training set in the prediction of the dischargeable time of the storage battery. Therefore, the required full discharge curve data needs to be screened from a large amount of power failure data.
Next, the curves are classified. The prediction is to predict the data after the voltage U by the data before the discharge voltage U, so the curve line type and data before the voltage U determine the curve trend after the voltage U. According to a large number of curve analyses, there are mainly the following three line types in the curve segment before U: the straight line type, the parabola-like type and the zigzag type are divided by whether the curve segment has obvious inflection points and the number of the inflection points, and particularly, as shown in fig. 3, a large data set is divided into three data sets.
Again, machine learning is performed. And each data set is a type of full-discharge curve, and a prediction model is formed by learning the relation between curve data characteristics before the voltage U and dischargeable time in a machine learning mode. The method specifically comprises the following steps:
A. extracting characteristics: one of the parameters of machine learning is the data characteristics, which in this technique are the data characteristics of the curve before voltage U, including the slopes of the different voltage segments before voltage U, the load current, etc. Suppose that there are N data points before the voltage U, and each data point has three information of voltage, current, and time. The data points are arranged in time from far to near as U (1), U (2), …, U (N); i (1), I (2), …, I (N); t (1), T (2), …, T (N). The specific features can then be calculated as follows:
(1) time-voltage slope f (i), N-1 features in total:
(2) voltage, current values u (i), i (i), for a total of 2N characteristics:
u (i), i ═ 1, 2.., N (formula 2)
I (i), i ═ 1, 2.., N (formula 3)
(3) Power value p (i), N characteristics:
p (i) (u) (i) i (i), i ═ 1, 2.., N (formula 4)
(4) The voltage and time difference sequence delta U (i) and delta T (i) has 2N-2 characteristics:
Δ U (i) ═ U (i +1) -U (i), i ═ 1, 2.., N-1 (formula 5)
Δ T (i) ═ T (i +1) -T (i), i ═ 1, 2., N-1 (formula 6)
A total of 6N-3 features.
B. Selecting a model: a linear regression model was used. Assuming that there are M sets of training data, each set has N data points, i.e., (6N-3) features, and the row vector composed of the features of each set of data is set as
While the true discharge duration y of each group is knowni1, 2.., M. Order to
The linear regression problem can be expressed as the known matrices X and Y, solving the optimal estimate of the coefficient matrix a:
min|AX-Y|2(formula 9)
C. Solving the model: the coefficient matrix A of the linear regression model can be obtained by adopting a least square method:
A＝YXT(XXT)-1(formula 10)
And finally, predicting the residual dischargeable time. After the prediction model is obtained through the large data set and machine learning in the last process, in actual use, the discharge curve which has already occurred, namely the curve data before the voltage U, can be put into linear regression, and the remaining dischargeable time length can be output. The method specifically comprises the following steps:
(1) data is acquired. Acquiring curve data before the voltage U, and arranging data points into U (1), U (2), … and U (N) from far to near according to time; i (1), I (2), …, I (N); t (1), T (2), …, T (N);
(2) and calculating the characteristics. Calculating features according to equations (1) - (6); arranging the features into a column vector x;
(3) the discharge time period is predicted. Obtaining a predicted value of the discharge time period according to the following formula
According to the analysis method for estimating the discharge time of the storage battery, provided by the embodiment of the invention, the discharge curve of the completely discharged storage battery is classified to obtain the fitting discharge curves of different line types, and then the partial discharge curve of the storage battery with the time to be estimated is matched with the fitting discharge curve, so that the dischargeable time of the storage battery can be accurately estimated; and the dischargeable time can be corrected through the correction parameters, and the accuracy and the effectiveness of the estimated dischargeable time are guaranteed.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (8)
1. An analysis method for estimating the discharge time of a storage battery is characterized by comprising the following steps:
acquiring first discharge data of a first storage battery in the whole complete discharge process;
generating a first discharge curve according to the first discharge data and the acquisition time;
classifying according to the first discharge curve and curve similarity to obtain a discharge curve group;
performing curve fitting on each discharge curve group to generate a fitted discharge curve of the discharge curve group;
collecting second discharge data of a second storage battery in the whole partial discharge process;
generating a second discharge curve section according to the second discharge data and the acquisition time;
matching the second discharge curve segment with a plurality of fitting discharge curves to obtain a first fitting discharge curve with the highest matching degree;
obtaining the remaining service time of the second storage battery according to the first fitted discharge curve and the second discharge curve section;
the step of acquiring second discharge data in the whole partial discharge process of the second storage battery specifically comprises the following steps:
acquiring second discharge data of the second storage battery in a partial discharge process from full charge;
the matching processing of the second discharge curve segment and the plurality of fitting discharge curves to obtain the first fitting discharge curve with the highest matching degree specifically comprises:
segmenting the second discharge curve segment;
when the second discharge curve segment is a stable-period discharge curve segment, matching the second discharge curve segment with stable-period fitted discharge curve segments of a plurality of fitted discharge curves to obtain a first fitted discharge curve with the highest matching degree;
and when the second discharge curve segment comprises a stable-period discharge curve segment and an attenuation-period discharge curve segment, matching the second discharge curve segment with the stable-period fitting discharge curve segment and the attenuation-period fitting discharge curve segment of the plurality of fitting discharge curves to obtain a first fitting discharge curve with the highest matching degree.
2. The analysis method for estimating the discharge time of the storage battery according to claim 1, wherein before the matching processing is performed on the second discharge curve segment and the plurality of fitted discharge curves to obtain the first fitted discharge curve with the highest matching degree, the method further comprises:
and segmenting the fitted discharge curve to obtain a stable-period fitted discharge curve segment and a decay-period fitted discharge curve segment.
3. The analysis method for estimating the discharge time of the storage battery according to claim 1, wherein the matching of the second discharge curve segment with the plurality of fitted discharge curves to obtain the first fitted discharge curve with the highest matching degree specifically comprises:
and matching the second discharge curve section with any part of the plurality of fitting discharge curves to obtain a first fitting discharge curve with the highest matching degree.
4. The analysis method for estimating the discharge time of the storage battery according to claim 1, wherein after the curve fitting is performed on each discharge curve group, so as to generate the fitted discharge curve of the discharge curve group, the method further comprises the following steps:
and verifying the fitted discharge curve to obtain the corresponding correction parameters of the fitted discharge curve.
5. The analysis method for estimating the discharge time of the storage battery according to claim 4, wherein the verifying the fitted discharge curve to obtain the correction parameters corresponding to the fitted discharge curve specifically comprises:
cutting the first discharge curve of the same discharge curve group to obtain a first discharge curve section;
obtaining corresponding estimated remaining service time according to the first discharge curve segment and the corresponding fitting discharge curve;
obtaining a ratio of estimated-actual residual service time according to the estimated residual service time and the actual residual service time;
and obtaining correction parameters corresponding to the fitted discharge curve of the discharge curve group according to a plurality of estimated-actual remaining use time ratios obtained by the same discharge curve group.
6. The analysis method for estimating the discharge time of the storage battery according to claim 5, wherein after the cutting the first discharge curve of the same discharge curve group to obtain a first discharge curve segment, the method further comprises:
and obtaining the actual remaining service time according to the first discharge curve and the first discharge curve section.
7. The analysis method for estimating the discharge time of the storage battery according to claim 5, wherein after the obtaining the remaining service time of the second storage battery according to the first fitted discharge curve and the second discharge curve segment, the method further comprises:
acquiring correction parameters corresponding to the first fitting discharge curve;
and revising the residual service time according to the correction parameters.
8. The analysis method for estimating the discharge time period of a storage battery according to claim 1, wherein the discharge data comprises the voltage, the current and the temperature of the storage battery.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810098482.7A CN108072848B (en) | 2018-01-31 | 2018-01-31 | Analysis method for estimating discharge time of storage battery |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810098482.7A CN108072848B (en) | 2018-01-31 | 2018-01-31 | Analysis method for estimating discharge time of storage battery |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108072848A CN108072848A (en) | 2018-05-25 |
| CN108072848B true CN108072848B (en) | 2020-12-01 |
Family
ID=62157044
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810098482.7A Active CN108072848B (en) | 2018-01-31 | 2018-01-31 | Analysis method for estimating discharge time of storage battery |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108072848B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111896883A (en) * | 2020-07-30 | 2020-11-06 | 重庆长安汽车股份有限公司 | Vehicle-mounted storage battery supportable parking time prediction method and early warning method |
| CN112763925B (en) * | 2020-12-29 | 2023-05-23 | 深圳市爱图仕影像器材有限公司 | Method and device for determining remaining service time of battery |
| CN113671379B (en) * | 2021-08-20 | 2022-02-15 | 深圳市爱图仕影像器材有限公司 | Method and medium for determining remaining battery life and movie and television lamp equipment |
| CN114325402A (en) * | 2021-11-26 | 2022-04-12 | 北京百度网讯科技有限公司 | Storage battery management method, device, system, terminal, electronic device and medium |
| CN113937866B (en) * | 2021-12-17 | 2022-04-22 | 深圳市爱图仕影像器材有限公司 | Method and device for controlling equipment operation, terminal equipment and storage medium |
| CN115291111B (en) * | 2022-08-03 | 2023-09-29 | 苏州清研精准汽车科技有限公司 | Training method of battery rest time prediction model and rest time prediction method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101097248A (en) * | 2006-06-28 | 2008-01-02 | Lg电子株式会社 | Enhanced-accuracy battery capacity prediction |
| CN102830361A (en) * | 2012-08-24 | 2012-12-19 | 海能达通信股份有限公司 | Battery capacity quick detection method and battery capacity quick detection system |
| CN104111428A (en) * | 2014-07-14 | 2014-10-22 | 浪潮软件集团有限公司 | Method for monitoring battery electric quantity of handheld billing terminal |
| CN104502859A (en) * | 2014-12-31 | 2015-04-08 | 清华大学深圳研究生院 | Detection and diagnosis method for battery charge and battery health state |
| CN104614679A (en) * | 2015-01-22 | 2015-05-13 | 哈尔滨龙易电气有限公司 | Method for measuring surplus capacity of curve-fitting type storage battery |
| CN104793144A (en) * | 2015-03-31 | 2015-07-22 | 中国人民解放军92537部队 | Rapid detection method for battery life |
-
2018
- 2018-01-31 CN CN201810098482.7A patent/CN108072848B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101097248A (en) * | 2006-06-28 | 2008-01-02 | Lg电子株式会社 | Enhanced-accuracy battery capacity prediction |
| CN102830361A (en) * | 2012-08-24 | 2012-12-19 | 海能达通信股份有限公司 | Battery capacity quick detection method and battery capacity quick detection system |
| CN104111428A (en) * | 2014-07-14 | 2014-10-22 | 浪潮软件集团有限公司 | Method for monitoring battery electric quantity of handheld billing terminal |
| CN104502859A (en) * | 2014-12-31 | 2015-04-08 | 清华大学深圳研究生院 | Detection and diagnosis method for battery charge and battery health state |
| CN104614679A (en) * | 2015-01-22 | 2015-05-13 | 哈尔滨龙易电气有限公司 | Method for measuring surplus capacity of curve-fitting type storage battery |
| CN104793144A (en) * | 2015-03-31 | 2015-07-22 | 中国人民解放军92537部队 | Rapid detection method for battery life |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108072848A (en) | 2018-05-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108072848B (en) | Analysis method for estimating discharge time of storage battery | |
| CN110161425B (en) | Method for predicting remaining service life based on lithium battery degradation stage division | |
| US20210044119A1 (en) | Method of performing a state of health estimation for a rechargeable battery energy storage system | |
| CN108037460B (en) | Real-time capacity evaluation method for lithium ion batteries produced in batches | |
| US11698416B2 (en) | Methods and systems for in-situ leakage current testing of cells in multi-cell battery packs | |
| CN101443672B (en) | Method for managing a bank of rechargeable batteries using the coup de fouet effect on charging | |
| CN113442787B (en) | Abnormal single cell identification method and device, electronic equipment and storage medium | |
| US20230258734A1 (en) | System for estimating the state of health (soh) of battery, system and method for deriving parameters therefor | |
| CN108490366B (en) | Rapid assessment method for state of health of electric automobile retired battery module | |
| CN108318825B (en) | residual value evaluation method for waste storage battery | |
| CN110323508B (en) | Recovery system and method for power battery in electric automobile | |
| US20220404430A1 (en) | Electrochemical methods for identification of cell quality | |
| CN116774057B (en) | Method and device for training battery life prediction model and predicting battery life | |
| US20220166075A1 (en) | Method for Enhancing a Battery Module Model of a Battery Module Type | |
| CN110927609B (en) | Decline evaluation method and device for battery energy storage system by utilizing battery in echelon | |
| CN117805649A (en) | Method for identifying abnormal battery cells based on SOH quantized battery capacity attenuation | |
| WO2023052521A1 (en) | Determination of an ageing value for batteries with current-voltage time series in time domain and load domain | |
| CN113866644A (en) | Method and device for predicting usable time and capacity of battery | |
| CN114910793A (en) | SOH estimation method for series battery pack of energy storage power station | |
| JP6532374B2 (en) | Storage battery state analysis system, storage battery state analysis method, and storage battery state analysis program | |
| CN114798502B (en) | Classification grouping method, system and production line for gradient utilization batteries | |
| JP2001250590A (en) | Method for deciding deterioration of storage battery | |
| Lin et al. | Research on Inconsistency Identification of Lithium-ion Battery Pack Based on Operational Data | |
| CN115318683A (en) | Screening method and device for whole-package batteries | |
| Kim et al. | Adaptive battery diagnosis/prognosis for efficient operation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |