CN116774077A - Method, device, equipment and storage medium for detecting health of energy storage power station battery - Google Patents
Method, device, equipment and storage medium for detecting health of energy storage power station battery Download PDFInfo
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Abstract
The invention discloses a health detection method, a device, equipment and a storage medium of an energy storage power station battery, wherein the method comprises the following steps: collecting original data of a plurality of parameters for retired batteries in an energy storage power station; selecting candidate feature data from the original data by taking the original data with a specified distance as a reference; screening target feature data from the candidate feature data by taking adjacent candidate feature data as a reference; filtering out partial invalid target characteristic data; if filtering is finished, constructing battery characteristic data with unchanged scale and unchanged rotation according to the target characteristic data; and generating an index representing the health state for the energy storage power station battery according to the battery characteristic data. According to the embodiment, the characteristics are reduced from high dimension to low dimension, information redundancy is reduced, the characteristics of the battery can be comprehensively represented by the battery characteristic data, the independence of the characteristics is improved, and the index related to the high evaluation health is fitted, so that the accuracy of evaluating the health state is improved.
Description
Technical Field
The present invention relates to the field of power grid technologies, and in particular, to a method, an apparatus, a device, and a storage medium for detecting health of a battery in an energy storage power station.
Background
Along with the gradual increase of the permeability of clean energy in the power system, a plurality of energy storage power stations are deployed in the power system, and scheduling targets such as waste reduction wind and light, peak clipping and valley filling can be realized through batteries of the energy storage power stations.
To ensure the safety and reliability of the batteries in the energy storage power stations, it is currently most often the case that the batteries in the energy storage power stations are evaluated for SOH (state of health), which is generally considered as the ratio of the actual available capacity to the rated capacity, i.e. soh=q aged /Q rated X 100%, where Q aged To actually available capacity, Q rated Is rated capacity.
Since the increase in internal resistance is one of the main causes of the battery capacity degradation and accompanies the entire life cycle, SOH, that is, soh= (R), can be estimated based on the relationship between the actual internal resistance of the battery and the internal resistance of the new battery EOL -R Current )/(R EOL -R BOL ) X 100%, where R BOL R is the internal resistance of the new battery when leaving the factory EOL R is the internal resistance at the end of the life cycle of the battery Current Is the actual content of the battery.
However, the capacity of the battery decays due to other factors, and the accuracy of detecting SOH based on the internal resistance test alone is low.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for detecting the health of a battery of an energy storage power station, which are used for solving the problem of how to improve the accuracy of detecting the health state of the battery in the energy storage power station.
According to an aspect of the present invention, there is provided a health detection method of an energy storage power station battery, including:
collecting original data of a plurality of parameters for retired batteries in an energy storage power station;
selecting candidate feature data from the original data by taking the original data with a specified distance as a reference;
screening target feature data from the candidate feature data by taking the adjacent candidate feature data as a reference;
filtering out the target characteristic data which are partially invalid;
if filtering is finished, constructing battery characteristic data with unchanged scale and unchanged rotation according to the target characteristic data;
and generating an index representing the health state for the energy storage power station battery according to the battery characteristic data.
Optionally, the selecting candidate feature data from the original data with the original data at a specified distance as a reference includes:
generating a circular boundary by taking the current original data as a circle center and a specified distance as a diameter;
judging whether a plurality of other original data which are continuous in sequence and have a value larger than or equal to the current original data exist on the boundary;
if yes, determining the current original data as candidate feature data.
Optionally, the screening the target feature data from the candidate feature data with reference to the adjacent candidate feature data includes:
loading a decision tree constructed based on a greedy algorithm;
and inputting a plurality of other candidate feature data adjacent to the current candidate feature data into the decision tree to decide whether the current candidate feature data is target feature data or not.
Optionally, filtering out the target feature data that is partially invalid includes:
performing non-maximum suppression on the target feature data;
if the non-maximum suppression is completed, screening out a plurality of other target feature data adjacent to the current target feature data;
taking absolute values of differences between the current target feature data and a plurality of other target feature data and summing the absolute values to serve as the sensitivity of the current target feature data;
and if the sensitivity is smaller than a preset threshold value, filtering the current target characteristic data.
Optionally, the constructing battery characteristic data with unchanged scale and/or unchanged rotation according to the target characteristic data includes:
inquiring a preset scaling factor and the number of layers;
Calculating the product of the scaling factor and each layer number to obtain a scaling coefficient;
calculating the ratio between the target feature data and each scaling coefficient to obtain the target feature data after scaling;
and writing the target characteristic data after scaling into a priority matrix of each layer to serve as battery characteristic data with unchanged scale.
Optionally, the constructing battery feature data with unchanged scale and unchanged rotation according to the target feature data further includes:
in the priority matrix, constructing a circular neighborhood by taking the scaled target characteristic data as a circle center;
locating a centroid in the neighborhood;
generating a main direction of the target feature data after scaling in the neighborhood as rotation-invariant battery feature data, wherein the main direction is a direction pointing from the target feature data after scaling to the centroid.
Optionally, the generating an index for representing the health state for the energy storage power station battery according to the battery characteristic data includes:
and generating constant-amplitude discharge time, direct current internal resistance and constant-amplitude discharge temperature rise for the energy storage power station battery according to the battery characteristic data, and taking the constant-amplitude discharge time, the direct current internal resistance and the constant-amplitude discharge temperature rise as indexes for representing the health state.
According to another aspect of the present invention, there is provided a health detection device for an energy storage power station battery, comprising:
the original data acquisition module is used for acquiring original data of a plurality of parameters for the retired battery in the energy storage power station;
the candidate feature data screening module is used for screening candidate feature data from the original data by taking the original data with a specified distance as a reference;
the target feature data screening module is used for screening target feature data from the candidate feature data by taking the adjacent candidate feature data as a reference;
the target characteristic data filtering module is used for filtering out the partially invalid target characteristic data;
the battery characteristic data generation module is used for constructing battery characteristic data with unchanged scale and unchanged rotation according to the target characteristic data if filtering is completed;
and the health index generation module is used for generating indexes representing the health states of the energy storage power station batteries according to the battery characteristic data.
Optionally, the candidate feature data screening module includes:
the boundary generation module is used for generating a circular boundary by taking the current original data as a circle center and a specified distance as a diameter;
The boundary value judging module is used for judging whether a plurality of other original data which are continuous in sequence and have a value larger than or equal to the current original data exist on the boundary; if yes, executing a candidate feature data determining module;
and the candidate feature data determining module is used for determining the current original data as candidate feature data.
Optionally, the target feature data screening module includes:
the decision tree loading module is used for loading a decision tree constructed based on a greedy algorithm;
and the target feature data decision module is used for inputting a plurality of other candidate feature data adjacent to the current candidate feature data into the decision tree so as to decide whether the current candidate feature data is the target feature data or not.
Optionally, the target feature data filtering module includes:
the non-maximum value suppression module is used for performing non-maximum value suppression on the target characteristic data;
the adjacent characteristic data screening module is used for screening a plurality of other target characteristic data adjacent to the current target characteristic data if the non-maximum suppression is completed;
the sensitivity calculation module is used for taking absolute values of differences between the current target characteristic data and a plurality of other target characteristic data and summing the absolute values to serve as the sensitivity of the current target characteristic data;
And the sensitivity filtering module is used for filtering the current target characteristic data if the sensitivity is smaller than a preset threshold value.
Optionally, the battery characteristic data generating module includes:
the scaling parameter query module is used for querying a preset scaling factor and the number of layers;
the scaling factor calculation module is used for calculating the product between the scaling factor and each layer number to obtain a scaling factor;
the feature scaling module is used for calculating the ratio between the target feature data and each scaling coefficient to obtain the target feature data after scaling;
and the priority matrix generation module is used for writing the target characteristic data after scaling into the priority matrix of each layer to be used as battery characteristic data with unchanged scale.
Optionally, the battery characteristic data generating module further includes:
the neighborhood construction module is used for constructing a circular neighborhood by taking the scaled target characteristic data as a circle center in the priority matrix;
a centroid locating module for locating a centroid in the neighborhood;
and a main direction generating module, configured to generate a main direction of the target feature data after scaling in the neighborhood as rotation-invariant battery feature data, where the main direction is a direction pointing from the target feature data after scaling to the centroid.
Optionally, the health indicator generating module includes:
and the health factor calculation module is used for generating constant-amplitude discharge time, direct-current internal resistance and constant-amplitude discharge temperature rise for the energy storage power station battery according to the battery characteristic data, and taking the constant-amplitude discharge time, the direct-current internal resistance and the constant-amplitude discharge temperature rise as indexes for representing the health state.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of health detection of an energy storage power station battery according to any of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing a computer program for causing a processor to execute the method for detecting the health of the battery of the energy storage power station according to any embodiment of the present invention.
In the embodiment, raw data of a plurality of parameters are collected for retired batteries in an energy storage power station; selecting candidate feature data from the original data by taking the original data with a specified distance as a reference; screening target feature data from the candidate feature data by taking adjacent candidate feature data as a reference; filtering out partial invalid target characteristic data; if filtering is finished, constructing battery characteristic data with unchanged scale and unchanged rotation according to the target characteristic data; and generating an index representing the health state for the energy storage power station battery according to the battery characteristic data. According to the embodiment, the battery characteristic data is built through coarse screening, fine screening, effectiveness screening and scale/rotation invariance, the characteristic is reduced from high dimension to low dimension, information redundancy is reduced, the battery characteristic data can comprehensively represent the characteristic of the battery, the independence of the characteristic is improved, and the index highly related to the evaluation of health is fitted, so that the accuracy of the evaluation of the health state is improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for detecting health of an energy storage power station battery according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of a battery aging mechanism according to a first embodiment of the present invention;
FIG. 3 is a graph showing the relationship between time and voltage decay for different cycles for a battery according to one embodiment of the present invention;
FIG. 4 is a graph showing the relationship between voltage and discharge rate for a battery according to a first embodiment of the present invention at different cycle times;
FIG. 5 is a graph showing the relationship between health factor and SOH for a battery at various cycle times according to a first embodiment of the present invention;
fig. 6 is a schematic structural diagram of a health detection device of an energy storage power station battery according to a second embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a method for detecting the health of an energy storage power station battery according to an embodiment of the present invention, where the method may be performed by a health detection device of the energy storage power station battery, the health detection device of the energy storage power station battery may be implemented in a form of hardware and/or software, and the health detection device of the energy storage power station battery may be configured in an electronic device. As shown in fig. 1, the method includes:
step 101, collecting original data of a plurality of parameters for retired batteries in the energy storage power station.
The SOH decay process is related to a number of external factors and also to the aging of materials within the battery during the life cycle of an energy storage power station battery (e.g., lithium battery).
As shown in fig. 2, aging factors include time, high temperature, low temperature, high SOC (State of Charge), low SOC, high current intensity, high voltage, etc., and attenuation mechanisms that may be applied by these aging factors include SEI (Solid Electrolyte Interphase, solid electrolyte interface) growth, SEI degradation, electrolyte degradation, separator damage, graphite exfoliation, lithium precipitation/dendrite, electrical contact degradation, electrode structure collapse, current collector corrosion, and attenuation modes generated by these attenuation mechanisms include internal resistance increase, active material loss, lithium ion loss, self-discharge increase, and effects generated by these attenuation modes include capacity attenuation, performance attenuation.
In this embodiment, most of retired batteries deployed in the energy storage power station, such as power batteries of new energy vehicles, for retired batteries, a battery Pack body (Pack) test may be performed on the energy storage power station battery in a test platform, and the retired batteries are classified as a sampling period, where raw data (i.e., parameter values) of a plurality of parameters related to SOH to a higher degree, such as charge-discharge voltage, circuit, temperature, and the like, are collected.
Step 102, taking the original data with a specified distance as a reference, and screening candidate feature data from the original data.
When the battery of the energy storage power station is extracted as the characteristic parameter, the original data which is actually measured is usually high-dimensional, and contains information which is redundant, so that the disaster of the data dimension is easily caused, and the real-time accurate evaluation and analysis are not facilitated, so that the dimension of the original data can be reduced.
In this embodiment, all the original data may be traversed, the current original data is evaluated with reference to other original data separated from the current original data by a specified distance, all the original data are coarsely screened, and part of the original data is screened out and recorded as candidate feature data.
In a specific implementation, a circular boundary can be generated in a two-dimensional space by taking current original data (regarded as a point) as a circle center and a specified distance (such as 6) as a diameter, and whether a plurality of (k) other original data with continuous ordering and a numerical value greater than or equal to the current original data exist on the boundary is judged; if yes, determining the current original data as candidate feature data.
Illustratively, k has a value of 12. In some cases, in order to make the candidate feature data take values more rapidly, so as to ensure the timeliness of the candidate feature data, the values are taken on k=1, 3, 5, 9 and 13, and if the conditions are not met, the values are compared again according to 12 points (namely, the original data).
And 103, screening target feature data from the candidate feature data by taking adjacent candidate feature data as a reference.
In this embodiment, all candidate feature data may be traversed, and based on a machine learning method, the current candidate feature data is evaluated with reference to other candidate feature data adjacent to the current candidate feature data, and the candidate feature data is refined, from which part of the candidate feature data is selected and recorded as target feature data.
In a specific implementation, a decision tree constructed based on the greedy algorithm ID3 may be loaded, and a plurality of (e.g., 16) other candidate feature data adjacent to the current candidate feature data may be input into the decision tree, so as to determine whether the current candidate feature data is the target feature data.
Step 104, filtering out the target characteristic data which are partially invalid.
In this embodiment, validity screening may be performed on the target feature data, and part of the invalid target feature data and the remaining valid target feature data may be filtered out from all the target feature data.
In a specific implementation, to reduce errors caused by local excessive aggregation of non-maxima, non-maxima suppression may be performed on the target feature data, ensuring that multiple target feature data does not occur.
And if the non-maximum suppression is finished, screening out a plurality of other target feature data adjacent to the current target feature data, taking absolute values of differences between the current target feature data and the plurality of other target feature data, summing the absolute values, taking the absolute values as the sensitivity of the current target feature data, and comparing the sensitivity with a preset threshold.
If the sensitivity is smaller than the preset threshold, the sensitivity of the current target feature data is lower, and the current target feature data is filtered, so that the target feature data with higher sensitivity is reserved.
And 105, if filtering is completed, constructing battery characteristic data with unchanged scale and unchanged rotation according to the target characteristic data.
After filtering out the partially invalid target feature data, for the remaining valid target feature data, battery feature data with unchanged scale and/or unchanged rotation can be constructed by using the target feature data, wherein the scale is unchanged, the proportion of the feature is unchanged, the rotation is unchanged, and the direction of the feature is unchanged in the zooming process.
In a specific implementation, a preset scaling factor scaleFactor (e.g., 1.2) and a layer number nlevels (e.g., the upper limit of nlevels is 8) may be queried.
For each layer number, calculating the product of the scaling factor and each layer number to obtain scaling factors, calculating the ratio between the target feature data and each scaling factor to obtain target feature data after scaling, and writing the target feature data after scaling into the priority matrix of each layer to serve as battery feature data with unchanged scale in each dimension.
Then the scaling procedure is expressed as: i '=i/(scalef actor x k), where I' is a priority matrix, I is a set of target feature data, k=1, 2, … …, nlevels.
In addition, in the priority matrix, a circular neighborhood is constructed by taking the scaled target characteristic data r as a circle center; and locating the centroid in the neighborhood, and generating a main direction of the scaled target feature data in the neighborhood as the battery feature data with unchanged rotation, wherein the main direction is the direction pointing to the centroid from the scaled target feature data.
Further, the priority matrix is defined as m pq ＝∑ x,y∈r x p y q I (x, y), I (x, y) is the value of the target feature data after scaling, then the centroid of the priority matrix isAssuming an angular coordinate of 0, the angle of the vector is noted as the principal direction +.>
And 106, generating an index representing the health state for the battery of the energy storage power station according to the battery characteristic data.
In this embodiment, appropriate battery characteristic data may be selected, and these battery characteristic data may be operated to generate an indicator for representing the health status of the energy storage power station battery, which may be denoted as a health factor.
In the specific implementation, the constant-amplitude discharge time, the direct-current internal resistance and the constant-amplitude discharge temperature rise can be generated for the energy storage power station battery according to the battery characteristic data and used as indexes for representing the health state, the defect of larger deviation of the single index in evaluating the health state is overcome, the accuracy in evaluating the health state of the battery is further improved, and the measurement evaluation time is shortened.
The constant-amplitude discharge time may refer to the time of discharging electric energy consumption with the same amplitude, the direct-current internal resistance may refer to the ratio of the voltage change of the battery to the corresponding discharge current change under the working condition, and the constant-amplitude discharge temperature rise may refer to the temperature of the battery which is raised by the electric energy with the same amplitude.
Further, the change of the effective capacity of the battery can be directly estimated by a real-time integration method:
wherein DeltaQ is the change value of effective capacity, i is the instantaneous current value of charge and discharge, t is time, t e T is the start time of charge and discharge s Is the end time of charge and discharge.
If the charging current and the discharging current of the battery are constant, the effective capacity of the battery can be predicted according to the charging time and the discharging time, and the calculation process of an ampere-hour integration method is simplified.
ΔQ＝I×(t e -t s )
Wherein DeltaQ is the change value of effective capacity, I is the effective current value of charge and discharge, t e T is the start time of charge and discharge s Is the end time of charge and discharge.
Fig. 3 shows a graph of time versus voltage decay for a battery at different cycles, with a cycle of 1 for curve 301, 50 for curve 302, and 200 for curve 303.
As shown in fig. 3, the internal voltage of the battery rapidly drops when the battery is operated for 2000s to 3000s, the chemical energy in the battery is gradually converted into electric energy as the discharging time of the battery increases, the total energy is reduced, and the ions in an unstable state increase so that the voltage drops rapidly. The voltage drop rate is relatively uniform and has a strong linearity when the battery just begins to discharge. The linear stage accounts for about 90% of the whole battery discharging process, so the ratio of the secondary linear region can be used as an evaluation index of the battery attenuation degree.
Prior to evaluation, the discharge of the battery generally meets the following conditions:
1. the discharge time cannot be too short in time span to observe the interval in which the voltage is nonlinearly decreased, resulting in excessive difference.
2. In order to ensure the real-time performance of the test, the discharge time span must not be too large.
3. Different batteries have different health conditions, so that the health state distinguishing capability of the battery in the election interval is ensured.
To select the appropriate battery discharge time interval span, fig. 4 plots the relationship between voltage and discharge rate for the battery at different cycles, with a curve 401 having 1 cycle, a curve 402 having 50 cycles, and a curve 403 having 200 cycles.
As shown in fig. 4, when the voltage of the battery is 3.7V-4.2V, the effect of the discharge time on the discharge rate is large, the batteries in different health states show obvious differences, and 3.30V-3.35V is suitable as the discharge interval in combination with the comparison of multiple sets of data.
After long-term use, the battery has poor health condition, various equipment losses lead to the rise of direct current internal resistance, the battery with high direct current internal resistance can generate more heat in the charging and discharging process, the heat lapse leads to the reduction of chemical energy and electric energy conversion efficiency, the volt-ampere characteristic is softer, and the accessed nominal discharging interval is shortened. The internal chemical solvent composition ratio of the battery at different SOCs is different, so that the direct current internal resistance of the battery is changed.
The battery is accompanied by polarization reaction in the charge and discharge processes, so the heat source of the battery also comprises chemical reaction heat release and polarization reaction heat release. The service life of the battery can be influenced by the excessively high working temperature of the battery, the aging process of the battery can be accelerated by the excessively high temperature, the running stability of the battery is reduced, and the running danger of the battery is increased. The battery temperature changes are affected by various factors such as the number of charge and discharge cycles, the external ambient temperature during battery operation, the state of charge of the battery, the state of health of the battery, etc.
And on the premise of controlling other variables to be certain, measuring a temperature curve in the discharging process of the battery, and drawing a curve chart of the running temperature and the voltage of the battery. As can be seen from fig. 4, the rising of the battery temperature and voltage at the discharge voltage of 3.0V-3.4V is strongly correlated with the number of battery cycles.
In order to verify the validity of the health evaluation standard provided by the invention, the SOH, the battery cycle number and the battery health state evaluation factor data are quantized and normalized:
wherein R is a normalization result, D is data to be normalized, D max And D min The maximum value and the minimum value of the data to be normalized are respectively, and the data acquisition period is 15s, so that the step change phenomenon exists in the constant-amplitude discharge time curve in the graph.
As shown in fig. 5, SOH 503 generally decreases with the increase of the cycle number, and in the process of decreasing SOH, SOH is obviously positively correlated with the constant-amplitude discharge time 501, and SOH is generally negatively correlated with the constant-amplitude discharge temperature rise (including the internal dc resistance) 502, which proves that the constant-amplitude discharge time, the internal dc resistance and the constant-amplitude discharge temperature rise can characterize the SOH of the battery to a certain extent.
In order to describe the relationship between the constant-amplitude discharge time, the direct-current internal resistance and the constant-amplitude discharge temperature rise and the SOH more objectively and intuitively, correlation analysis is carried out on every two of the constant-amplitude discharge time, the direct-current internal resistance and the constant-amplitude discharge temperature rise respectively, and the correlation analysis is carried out on every two of the constant-amplitude discharge temperature rise respectively, and is shown in the following table.
| Index (I) | No. 1 battery | No. 2 battery | No. 3 battery | No. 4 battery |
| Constant amplitude discharge time | 0.9928 | 0.9436 | 0.9694 | 0.9686 |
| Internal resistance of DC | -0.9762 | -0.9512 | -0.9514 | -0.9596 |
| Constant amplitude discharge heating | -0.9789 | -0.9826 | -0.9331 | -0.9682 |
From the above table, the absolute values of the correlations between the constant-amplitude discharge time, the direct-current internal resistance, and the constant-amplitude discharge temperature rise and the SOH are all greater than 0.95, respectively, and the correlations are high, wherein the constant-amplitude discharge time is positively correlated with the SOH, and the direct-current internal resistance, the constant-amplitude discharge temperature rise and the SOH are negatively correlated.
In the embodiment, raw data of a plurality of parameters are collected for retired batteries in an energy storage power station; selecting candidate feature data from the original data by taking the original data with a specified distance as a reference; screening target feature data from the candidate feature data by taking adjacent candidate feature data as a reference; filtering out partial invalid target characteristic data; if filtering is finished, constructing battery characteristic data with unchanged scale and unchanged rotation according to the target characteristic data; and generating an index representing the health state for the energy storage power station battery according to the battery characteristic data. According to the embodiment, the battery characteristic data is built through coarse screening, fine screening, effectiveness screening and scale/rotation invariance, the characteristic is reduced from high dimension to low dimension, information redundancy is reduced, the battery characteristic data can comprehensively represent the characteristic of the battery, the independence of the characteristic is improved, and the index highly related to the evaluation of health is fitted, so that the accuracy of the evaluation of the health state is improved.
Example two
Fig. 6 is a schematic structural diagram of a health detection device of an energy storage power station battery according to a second embodiment of the present invention. As shown in fig. 6, the apparatus includes:
the original data acquisition module 601 is configured to acquire original data of a plurality of parameters for retired batteries in the energy storage power station;
a candidate feature data screening module 602, configured to screen candidate feature data from the original data with reference to the original data that are separated by a specified distance;
a target feature data screening module 603, configured to screen target feature data from the candidate feature data with reference to adjacent candidate feature data;
a target feature data filtering module 604, configured to filter out the target feature data that is partially invalid;
the battery characteristic data generating module 605 is configured to construct battery characteristic data with unchanged scale and unchanged rotation according to the target characteristic data if filtering is completed;
the health index generation module 606 is configured to generate an index representing a health state for the energy storage power station battery according to the battery characteristic data.
In one embodiment of the present invention, the candidate feature data filtering module 602 includes:
the boundary generation module is used for generating a circular boundary by taking the current original data as a circle center and a specified distance as a diameter;
The boundary value judging module is used for judging whether a plurality of other original data which are continuous in sequence and have a value larger than or equal to the current original data exist on the boundary; if yes, executing a candidate feature data determining module;
and the candidate feature data determining module is used for determining the current original data as candidate feature data.
In one embodiment of the present invention, the target feature data filtering module 603 includes:
the decision tree loading module is used for loading a decision tree constructed based on a greedy algorithm;
and the target feature data decision module is used for inputting a plurality of other candidate feature data adjacent to the current candidate feature data into the decision tree so as to decide whether the current candidate feature data is the target feature data or not.
In one embodiment of the present invention, the target feature data filtering module 604 includes:
the non-maximum value suppression module is used for performing non-maximum value suppression on the target characteristic data;
the adjacent characteristic data screening module is used for screening a plurality of other target characteristic data adjacent to the current target characteristic data if the non-maximum suppression is completed;
The sensitivity calculation module is used for taking absolute values of differences between the current target characteristic data and a plurality of other target characteristic data and summing the absolute values to serve as the sensitivity of the current target characteristic data;
and the sensitivity filtering module is used for filtering the current target characteristic data if the sensitivity is smaller than a preset threshold value.
In one embodiment of the present invention, the battery characteristic data generating module 605 includes:
the scaling parameter query module is used for querying a preset scaling factor and the number of layers;
the scaling factor calculation module is used for calculating the product between the scaling factor and each layer number to obtain a scaling factor;
the feature scaling module is used for calculating the ratio between the target feature data and each scaling coefficient to obtain the target feature data after scaling;
and the priority matrix generation module is used for writing the target characteristic data after scaling into the priority matrix of each layer to be used as battery characteristic data with unchanged scale.
In one embodiment of the present invention, the battery characteristic data generating module 605 further includes:
the neighborhood construction module is used for constructing a circular neighborhood by taking the scaled target characteristic data as a circle center in the priority matrix;
A centroid locating module for locating a centroid in the neighborhood;
and a main direction generating module, configured to generate a main direction of the target feature data after scaling in the neighborhood as rotation-invariant battery feature data, where the main direction is a direction pointing from the target feature data after scaling to the centroid.
In one embodiment of the present invention, the health indicator generation module 606 includes:
and the health factor calculation module is used for generating constant-amplitude discharge time, direct-current internal resistance and constant-amplitude discharge temperature rise for the energy storage power station battery according to the battery characteristic data, and taking the constant-amplitude discharge time, the direct-current internal resistance and the constant-amplitude discharge temperature rise as indexes for representing the health state.
The health detection device for the energy storage power station battery provided by the embodiment of the invention can execute the health detection method for the energy storage power station battery provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the health detection method for the energy storage power station battery.
Example III
Fig. 7 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 7, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the method of health detection of the energy storage power station battery.
In some embodiments, the method of health detection of the energy storage power station battery may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the energy storage power station battery health detection method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the method of health detection of the energy storage power station battery by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
Example IV
Embodiments of the present invention also provide a computer program product comprising a computer program which, when executed by a processor, implements a method for health detection of an energy storage power station battery as provided by any of the embodiments of the present invention.
Computer program product in the implementation, the computer program code for carrying out operations of the present invention may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. The method for detecting the health of the battery of the energy storage power station is characterized by comprising the following steps of:
collecting original data of a plurality of parameters for retired batteries in an energy storage power station;
selecting candidate feature data from the original data by taking the original data with a specified distance as a reference;
screening target feature data from the candidate feature data by taking the adjacent candidate feature data as a reference;
Filtering out the target characteristic data which are partially invalid;
if filtering is finished, constructing battery characteristic data with unchanged scale and unchanged rotation according to the target characteristic data;
and generating an index representing the health state for the energy storage power station battery according to the battery characteristic data.
2. The method of claim 1, wherein the screening candidate feature data from the raw data with reference to the raw data at a specified distance comprises:
generating a circular boundary by taking the current original data as a circle center and a specified distance as a diameter;
judging whether a plurality of other original data which are continuous in sequence and have a value larger than or equal to the current original data exist on the boundary;
if yes, determining the current original data as candidate feature data.
3. The method of claim 1, wherein the screening out target feature data from the candidate feature data with reference to adjacent candidate feature data comprises:
loading a decision tree constructed based on a greedy algorithm;
and inputting a plurality of other candidate feature data adjacent to the current candidate feature data into the decision tree to decide whether the current candidate feature data is target feature data or not.
4. The method of claim 1, wherein said filtering out partially invalid target feature data comprises:
performing non-maximum suppression on the target feature data;
if the non-maximum suppression is completed, screening out a plurality of other target feature data adjacent to the current target feature data;
taking absolute values of differences between the current target feature data and a plurality of other target feature data and summing the absolute values to serve as the sensitivity of the current target feature data;
and if the sensitivity is smaller than a preset threshold value, filtering the current target characteristic data.
5. The method of claim 1, wherein constructing scale-invariant and/or rotation-invariant battery feature data from the target feature data comprises:
inquiring a preset scaling factor and the number of layers;
calculating the product of the scaling factor and each layer number to obtain a scaling coefficient;
calculating the ratio between the target feature data and each scaling coefficient to obtain the target feature data after scaling;
and writing the target characteristic data after scaling into a priority matrix of each layer to serve as battery characteristic data with unchanged scale.
6. The method of claim 5, wherein constructing scale-invariant and rotation-invariant battery feature data from the target feature data further comprises:
in the priority matrix, constructing a circular neighborhood by taking the scaled target characteristic data as a circle center;
locating a centroid in the neighborhood;
generating a main direction of the target feature data after scaling in the neighborhood as rotation-invariant battery feature data, wherein the main direction is a direction pointing from the target feature data after scaling to the centroid.
7. The method of any one of claims 1-6, wherein generating an indicator for the energy storage power station battery that characterizes a state of health as a function of the battery characteristic data comprises:
and generating constant-amplitude discharge time, direct current internal resistance and constant-amplitude discharge temperature rise for the energy storage power station battery according to the battery characteristic data, and taking the constant-amplitude discharge time, the direct current internal resistance and the constant-amplitude discharge temperature rise as indexes for representing the health state.
8. A health detection device of an energy storage power station battery, comprising:
the original data acquisition module is used for acquiring original data of a plurality of parameters for the retired battery in the energy storage power station;
The candidate feature data screening module is used for screening candidate feature data from the original data by taking the original data with a specified distance as a reference;
the target feature data screening module is used for screening target feature data from the candidate feature data by taking the adjacent candidate feature data as a reference;
the target characteristic data filtering module is used for filtering out the partially invalid target characteristic data;
the battery characteristic data generation module is used for constructing battery characteristic data with unchanged scale and unchanged rotation according to the target characteristic data if filtering is completed;
and the health index generation module is used for generating indexes representing the health states of the energy storage power station batteries according to the battery characteristic data.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of health detection of an energy storage power station battery of any one of claims 1-7.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program for causing a processor to execute the method for health detection of an energy storage power station battery according to any one of claims 1-7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117872168A (en) * | 2024-03-12 | 2024-04-12 | 苏州市洛肯电子科技有限公司 | Intelligent detection method and system for embedded RFID new energy battery |
| CN117949831A (en) * | 2024-03-27 | 2024-04-30 | 牡丹江师范学院 | Adjustable physical similarity simulation experiment platform |
| CN117949831B (en) * | 2024-03-27 | 2024-05-31 | 牡丹江师范学院 | Adjustable physical similarity simulation experiment platform |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117872168A (en) * | 2024-03-12 | 2024-04-12 | 苏州市洛肯电子科技有限公司 | Intelligent detection method and system for embedded RFID new energy battery |
| CN117949831A (en) * | 2024-03-27 | 2024-04-30 | 牡丹江师范学院 | Adjustable physical similarity simulation experiment platform |
| CN117949831B (en) * | 2024-03-27 | 2024-05-31 | 牡丹江师范学院 | Adjustable physical similarity simulation experiment platform |
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