CN114139706A - Evaluation system of energy storage resource - Google Patents
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Abstract
The application discloses evaluation system of energy storage resource. Wherein, this system includes: the acquisition module is used for acquiring log information of a preset type of energy storage system and carrying out format standardization processing on the log information to obtain target log information; a storage module, configured to store target log information, where the target log information at least includes: presetting operating state data of the type energy storage system; and the quantitative analysis module is used for receiving the target log information from the storage module and determining the potential utilization level of the resources in the preset type of energy storage system according to the target log information, wherein the potential utilization level is used for indicating the available probability of the resources. The battery energy storage system utilization potential evaluation method and device solve the technical problems that due to the fact that most of related technicians adopt battery pack integrated monitoring, a large amount of manpower and material resources are wasted due to the fact that the utilization potential of the battery energy storage system is evaluated through regular manual detection, and the utilization potential of the full life cycle of the battery cannot be accurately measured.
Description
Technical Field
The application relates to the field of battery detection, in particular to an evaluation system of energy storage resources.
Background
The battery energy storage is an important branch of the development of the current energy storage technology, the battery energy storage application technology is developing towards the directions of distribution, movement, isomerization, intellectualization and informatization, and the quantitative evaluation of the utilization potential of the battery energy storage system plays an important role in the multi-scene safe production work of the mobile/heterogeneous battery energy storage system. The traditional battery energy storage system utilization potential evaluation mostly adopts a mode of battery pack integrated monitoring and periodic manual inspection at present, although a large amount of manpower and material resources are invested, the traditional battery energy storage system all belongs to sampling investigation of battery operation time sequence, and the utilization potential of the full life cycle of the battery cannot be accurately measured/quantified.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the application provides an evaluation system of energy storage resources to at least solve because the integrated monitoring of group battery is adopted to most among the relevant technical staff, regularly artifical detection is carried out and is appraised the extravagant a large amount of manpower and materials that cause to battery energy storage system utilization potential, can't accurately measure the technical problem of battery full life cycle's utilization potential.
According to an aspect of an embodiment of the present application, there is provided an energy storage resource evaluation system, including: the acquisition module is used for acquiring log information of a preset type of energy storage system and carrying out format standardization processing on the log information to obtain target log information; a storage module, configured to store target log information, where the target log information at least includes: presetting operating state data of the type energy storage system; and the quantitative analysis module is used for receiving the target log information from the storage module and determining the potential utilization level of the resources in the preset type of energy storage system according to the target log information, wherein the potential utilization level is used for indicating the available probability of the resources.
Optionally, the quantitative analysis module comprises: and the knowledge base is used for providing standard state data of the energy storage system and potential utilization levels corresponding to the standard state data.
Optionally, the quantitative analysis module further comprises: and the machine learning module is used for classifying the running state data based on a clustering algorithm and updating the knowledge base based on a classification result.
Optionally, the quantitative analysis module further comprises: and the real-time streaming computing module is used for comparing the running state data with the standard state data stored in the knowledge base under the offline and/or online conditions, and determining the potential utilization level of the resources in the energy storage system according to the comparison result.
Optionally, the collecting module is used for collecting log information of the energy storage system of the preset type by integrating the flash into the kafka.
Optionally, the system further comprises: and the view module is used for receiving the utilization level of the potential resources in the preset type energy storage system from the quantitative analysis module and displaying the utilization level.
According to another aspect of the embodiments of the present application, there is also provided an evaluation method for energy storage resources, where the evaluation method is implemented based on any one of evaluation systems for energy storage resources, and the evaluation method includes: collecting log information of a preset type energy storage system, and carrying out format standardization processing on the log information to obtain target log information; wherein the preset types include: a mobile type energy storage system and/or a heterogeneous type energy storage system; extracting running state data of a preset type of energy storage system from the target log information; and comparing the operating state data with the state data stored in the knowledge base, and determining the potential utilization level of the resources in the energy storage system according to the comparison result, wherein the potential utilization level is used for indicating the usable probability of the resources.
Optionally, after determining the potential utilization level of the resource in the energy storage system according to the comparison result, the method further includes: the potential utilization levels are sent to a view module for presentation.
According to another aspect of the embodiments of the present application, a non-volatile storage medium is further provided, where the non-volatile storage medium includes a stored program, and when the program runs, a device in which the non-volatile storage medium is located is controlled to execute any one of the energy storage resource evaluation methods.
According to another aspect of the embodiments of the present application, there is also provided a processor, where the processor is configured to execute a program, where the program executes any one of the methods for evaluating energy storage resources when running.
In the embodiment of the application, a method of quantitatively analyzing target log information is adopted, and the acquisition module is used for acquiring the log information of the energy storage system of a preset type and carrying out format standardization processing on the log information to obtain the target log information; a storage module, configured to store target log information, where the target log information at least includes: presetting operating state data of the type energy storage system; the quantitative analysis module is used for receiving the target log information from the storage module and determining the potential utilization level of the resources in the energy storage system of the preset type according to the target log information, wherein the potential utilization level is used for indicating the available probability of the resources, so that the technical effect of automatically and accurately determining the potential utilization level of the resources in the energy storage system is achieved, and the technical problems that a large amount of manpower and material resources are wasted and the utilization potential of the battery energy storage system cannot be accurately measured due to the fact that most of related technicians adopt battery pack integrated monitoring and regular manual detection to evaluate the utilization potential of the battery energy storage system are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic structural diagram of an alternative energy storage resource evaluation system according to an embodiment of the present application;
fig. 2 is a flowchart of an alternative Spark big data platform-based mobile/heterogeneous battery energy storage system utilization potential evaluation method according to an embodiment of the present application;
fig. 3 is an architecture diagram illustrating potential evaluation of a mobile/heterogeneous battery energy storage system based on a Spark big data platform according to an embodiment of the present application;
fig. 4 is a schematic flow chart of an alternative energy storage resource evaluation method according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or 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.
To facilitate better understanding of embodiments related to the present application by those skilled in the art, technical terms or partial terms that may be referred to in the embodiments of the present application are explained as follows:
SOC, State of Charge State of the battery;
SOE, State of Energy State of Energy battery;
SOP State of Power Battery;
SOH State of Health Battery Health status;
open Circuit Voltage;
RDD: a resource Distributed Dataset elastic Distributed Dataset;
API, Application Programming Interface;
HDFS, Hadoop Distributed File Systems Distributed File system;
the SVM is a Support Vector Machine;
KNN: k-nearest Neighbors;
k-means K-mean algorithm.
In accordance with an embodiment of the present application, there is provided an energy storage resource evaluation system embodiment, it is noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
Fig. 1 is an evaluation system for an energy storage resource according to an embodiment of the present application, where the evaluation system for an energy storage resource includes:
the acquisition module 40 is used for acquiring log information of a preset type of energy storage system and performing format standardization processing on the log information to obtain target log information;
a storage module 42, configured to store target log information, where the target log information at least includes: presetting operating state data of the type energy storage system;
and the quantitative analysis module 44 is configured to receive the target log information from the storage module, and determine a potential utilization level of the resource in the preset type of energy storage system according to the target log information, where the potential utilization level is used to indicate a usable probability of the resource.
In the evaluation system of the energy storage resource, an acquisition module 40 is used for acquiring log information of an energy storage system of a preset type and performing format standardization processing on the log information to obtain target log information; a storage module 42, configured to store target log information, where the target log information at least includes: presetting operating state data of the type energy storage system; the quantitative analysis module 44 is configured to receive the target log information from the storage module, and determine a potential utilization level of resources in the preset type energy storage system according to the target log information, where the potential utilization level is used to indicate a usable probability of the resources, so that a technical effect of automatically and accurately determining the potential utilization level of the resources in the energy storage system is achieved, and further a technical problem that a large amount of manpower and material resources are wasted and the utilization potential of the battery energy storage system cannot be accurately measured due to the fact that most of related technicians adopt battery pack integrated monitoring and periodic manual detection to evaluate the utilization potential of the battery energy storage system is solved.
In some embodiments of the present application, the quantitative analysis module comprises: and the knowledge base is used for providing standard state data of the energy storage system and potential utilization levels corresponding to the standard state data.
In some embodiments of the present application, the quantitative analysis module further comprises: and the machine learning module is used for classifying the running state data based on a clustering algorithm and updating the knowledge base based on a classification result.
In some optional embodiments of the present application, the quantitative analysis module further comprises: and the real-time streaming computing module is used for comparing the running state data with the standard state data stored in the knowledge base under the offline and/or online conditions, and determining the potential utilization level of the resources in the energy storage system according to the comparison result.
It should be noted that the acquisition module realizes acquisition of log information of the energy storage system of the preset type by integrating the flash into the kafka.
In some embodiments of the present application, the system further comprises: and the view module is used for receiving the utilization level of the potential resources in the preset type energy storage system from the quantitative analysis module and displaying the utilization level.
Fig. 2 is a flowchart of a method for evaluating utilization potential of a mobile/heterogeneous battery energy storage system based on a Spark big data platform according to the present application, as shown in fig. 2:
1. the method comprises the following steps: and collecting logs according to real-time information, and standardizing the format of the logs. The collected logs are sent to an HDFS (Hadoop distributed File System) for storage on one hand, and are sent to a Spark big data platform on the other hand, and transactions and actions operations are executed and stored as RDD (remote data device).
2. Step two: the collected data were analyzed using Spark Streaming and Spark MLlib. And data mining and machine learning are carried out on the energy storage resources through historical data and online real-time data of a Spark big data platform. And analyzing the logs in the HDFS in real time, combining the logs with the knowledge base to generate new knowledge items which are not included in the knowledge base, and expanding the knowledge base. Spark MLlib provides rich machine learning algorithms, performs aggregation classification on energy storage resources, generates a plurality of clusters, and compares/updates a knowledge base.
3. Step three: polling, and determining the utilization potential level. And determining the potential utilization level of the energy storage resource by comparing and judging the log information acquired in real time with the stored information in the knowledge base.
4. Step four: and sending the result of the third step to a view module for displaying.
An optional embodiment of the application provides a Spark big data platform-based mobile/heterogeneous battery energy storage system utilization potential quantitative evaluation method. The technical task is realized in the following mode, and the battery energy storage data acquisition, processing, analysis and early warning big data platform based on Spark Core and Spark SQL is designed, and comprises an acquisition module, a data analysis module and a view module, wherein the architecture diagram of the platform is shown in figure 3. The specific implementation mode is as follows:
1. and the acquisition module is used for querying the compiler through the Catalyst, and converting the SQL/Data Frame/Dataset Data into HDFS/RDD finally through a series of conversions. The method comprises the steps of preprocessing device logs of various external energy storage resources, transmitting the device logs to a data storage module in real time, wherein the preprocessing comprises log filtering, merging and format standardization, and the log specification is unified.
2. The collection module integrates flash into kafka (a distributed message system with the kafka developed by Linkedln and open source) to realize log collection, log preprocessing and log real-time transmission, can collect syslog, monitoring folder logs, TCP/UDP interface logs and the like, can be well docked with Spark Streaming, and realizes real-time transmission of preprocessed log information to the data storage module.
3. And the data storage module is used for taking the collected running state data (such as OCV, output current, output voltage, battery capacity and the like) of the battery energy storage system, environmental data (such as temperature, humidity, load power and the like), battery information (battery materials, production date, cycle times and the like) as effective original data through data filters and maps and returning the effective original data to be unmodified RDD.
4. A quantitative analysis module comprising Spark Streaming and Spark MLlib modules: the Spark Streaming module comprises alarm generation, off-line analysis and on-line analysis, and updates a knowledge base in real time according to an analysis result; the Spark MLlib module comprises artificial intelligence-based classification, regression and clustering algorithms and is used for battery recombination analysis of battery energy storage, and analysis results are returned and a knowledge base is updated.
The knowledge base is a core database for quantitative evaluation of the utilization potential of the battery system, saves multi-scene benchmarking data of the operation of the heterogeneous battery energy storage system, comprises a single influence factor battery evaluation and a multi-scene associated quantitative analysis method, and has the functions of but not limited to:
1. according to the partial content of the single log as analysis data, judging a certain characteristic in the quantification of the utilization potential of the battery energy storage system, such as at least one utilization potential value in SOC, SOE, SOP, SOH, capacity, battery internal resistance and the like;
2. according to the frequency of the occurrence of the special log content in unit time, the utilization potential of the battery energy storage system is judged to be recalibrated and modified, and the correct battery utilization potential is continuously updated in a quantized mode;
3. determining a safe operation boundary according to the operation of the same battery energy storage resources distributed in different devices, and updating the safe operation boundary in a knowledge base;
4. and (3) carrying out potential quantitative characteristic analysis on the utilization potential of the battery energy storage system under multiple influence factors (temperature, load and working condition) with multiple correlation degrees.
Compared with the prior art, the mobile/heterogeneous battery energy storage system utilization potential quantitative evaluation system based on the Spark big data platform has the following outstanding beneficial effects:
spark is based on the high-performance streaming computing and processing speed of Spark, the rich ecology of multiple data sources (MySQL, Hbase and Kafka) and multiple API interfaces (queue, ORC, CSV and Json), and the capability of fully combining big data and AI, so that the value in the data of a mobile/heterogeneous battery energy storage system can be fully mined, and the utilization potential of the battery energy storage system can be quantified in a real-time and online manner.
2. The distributed architecture of the large data platform is easy to expand and reduce, can deal with the change of the distributed energy storage scale to achieve effective utilization of resources, and also overcomes the defect that the prior art is difficult to process massive logs.
3. The energy storage resource utilization potential quantitative evaluation method can accurately remove the misinformation of information and provide a detailed quantitative evaluation result for guiding the optimal configuration and the combined optimal operation of the distributed/mobile/heterogeneous energy storage system.
4. The big data technology is used for data mining and machine learning, massive historical log information can be effectively utilized, and the knowledge base can be automatically expanded by combining the existing knowledge base with the association analysis.
5. And the Flume is integrated into the kafka, so that log collection of various energy storage devices can be realized, the logs are transmitted to the Spark big data platform HDFS/RDD/MySQL through kafak for storage, and data display, query and the like can be performed.
6. The utilization potential of the battery energy storage system refers to states of the battery such as SOC, SOE, SOP, SOH, capacity and internal resistance of the battery. The application provides a method for quantitatively evaluating the utilization potential of a battery energy storage system with real-time analysis and multi-factor correlation.
Fig. 4 is a method for evaluating energy storage resources according to an embodiment of the present application, where the method is implemented based on an evaluation system for any one of the energy storage resources, and as shown in fig. 4, the method includes:
step S102, collecting log information of an energy storage system of a preset type, and carrying out format standardization processing on the log information to obtain target log information; wherein the preset types include: a mobile type energy storage system and/or a heterogeneous type energy storage system;
step S104, extracting the running state data of the preset type energy storage system from the target log information;
and S106, comparing the running state data with the state data stored in the knowledge base, and determining the potential utilization level of the resources in the energy storage system according to the comparison result, wherein the potential utilization level is used for indicating the usable probability of the resources.
In the method for evaluating the energy storage resources, firstly, log information of an energy storage system of a preset type can be collected, and format standardization processing is carried out on the log information to obtain target log information; wherein the preset types include: a mobile type energy storage system and/or a heterogeneous type energy storage system; then, extracting the running state data of the preset type energy storage system from the target log information; and finally, comparing the running state data with the state data stored in the knowledge base, and determining the potential utilization level of the resources in the energy storage system according to the comparison result, wherein the potential utilization level is used for indicating the available probability of the resources, so that the technical effect of automatically and accurately determining the potential utilization level of the resources in the energy storage system is realized, and the technical problems that a large amount of manpower and material resources are wasted and the utilization potential of the battery energy storage system cannot be accurately measured due to the fact that most of related technicians adopt battery pack integrated monitoring and regular manual detection to evaluate the utilization potential of the battery energy storage system are solved.
Optionally, after determining the potential utilization level of the resource in the energy storage system according to the comparison result, the method further includes: the potential utilization levels are sent to a view module for presentation.
According to another aspect of the embodiments of the present application, a non-volatile storage medium is further provided, where the non-volatile storage medium includes a stored program, and when the program runs, a device in which the non-volatile storage medium is located is controlled to execute any one of the energy storage resource evaluation methods.
Specifically, the storage medium is used for storing program instructions for executing the following functions, and the following functions are realized:
collecting log information of a preset type energy storage system, and carrying out format standardization processing on the log information to obtain target log information; wherein the preset types include: a mobile type energy storage system and/or a heterogeneous type energy storage system; extracting running state data of a preset type of energy storage system from the target log information; and comparing the operating state data with the state data stored in the knowledge base, and determining the potential utilization level of the resources in the energy storage system according to the comparison result, wherein the potential utilization level is used for indicating the usable probability of the resources.
According to another aspect of the embodiments of the present application, there is also provided a processor, where the processor is configured to execute a program, where the program executes any one of the methods for evaluating energy storage resources when running.
Specifically, the processor is configured to call a program instruction in the memory, and implement the following functions:
collecting log information of a preset type energy storage system, and carrying out format standardization processing on the log information to obtain target log information; wherein the preset types include: a mobile type energy storage system and/or a heterogeneous type energy storage system; extracting running state data of a preset type of energy storage system from the target log information; and comparing the operating state data with the state data stored in the knowledge base, and determining the potential utilization level of the resources in the energy storage system according to the comparison result, wherein the potential utilization level is used for indicating the usable probability of the resources.
In the embodiment of the application, a method of quantitatively analyzing target log information is adopted, and the acquisition module is used for acquiring the log information of the energy storage system of a preset type and carrying out format standardization processing on the log information to obtain the target log information; a storage module, configured to store target log information, where the target log information at least includes: presetting operating state data of the type energy storage system; the quantitative analysis module is used for receiving the target log information from the storage module and determining the potential utilization level of the resources in the energy storage system of the preset type according to the target log information, wherein the potential utilization level is used for indicating the available probability of the resources, so that the technical effect of automatically and accurately determining the potential utilization level of the resources in the energy storage system is achieved, and the technical problems that a large amount of manpower and material resources are wasted and the utilization potential of the battery energy storage system cannot be accurately measured due to the fact that most of related technicians adopt battery pack integrated monitoring and regular manual detection to evaluate the utilization potential of the battery energy storage system are solved.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (10)
1. An energy storage resource evaluation system, comprising:
the acquisition module is used for acquiring log information of a preset type of energy storage system and carrying out format standardization processing on the log information to obtain target log information;
a storage module, configured to store the target log information, where the target log information at least includes: running state data of the preset type energy storage system;
and the quantitative analysis module is used for receiving the target log information from the storage module and determining a potential utilization level of the resources in the preset type of energy storage system according to the target log information, wherein the potential utilization level is used for indicating the available probability of the resources.
2. The system of claim 1, wherein the quantitative analysis module comprises: and the knowledge base is used for providing standard state data of the energy storage system and potential utilization levels corresponding to the standard state data.
3. The system of claim 2, wherein the quantitative analysis module further comprises: and the machine learning module is used for classifying the running state data based on a clustering algorithm and updating the knowledge base based on a classification result.
4. The system of claim 2, wherein the quantitative analysis module further comprises: and the real-time streaming computing module is used for comparing the running state data with the standard state data stored in the knowledge base under the offline and/or online conditions, and determining the potential utilization level of resources in the energy storage system according to the comparison result.
5. The system of claim 1, wherein the collection module collects log information for a predetermined type of energy storage system by integrating flash into kafka.
6. The system of claim 1, further comprising:
and the view module is used for receiving the utilization level of the potential resources in the preset type of energy storage system from the quantitative analysis module and displaying the utilization level.
7. An energy storage resource evaluation method implemented based on the energy storage resource evaluation system of any one of claims 1 to 6, comprising:
collecting log information of a preset type energy storage system, and carrying out format standardization processing on the log information to obtain target log information; wherein the preset types include: a mobile type energy storage system and/or a heterogeneous type energy storage system;
extracting the running state data of the preset type energy storage system from the target log information;
and comparing the running state data with state data stored in a knowledge base, and determining a potential utilization level of resources in the energy storage system according to a comparison result, wherein the potential utilization level is used for indicating the usable probability of the resources.
8. The method of claim 7, wherein after determining the potential utilization level of the resources in the energy storage system based on the comparison, the method further comprises: and sending the potential utilization level to a view module for displaying.
9. A non-volatile storage medium, characterized in that the non-volatile storage medium includes a stored program, and when the program runs, the apparatus where the non-volatile storage medium is located is controlled to execute the energy storage resource evaluation method according to any one of claims 7 to 8.
10. A processor, configured to execute a program, wherein the program executes the method for evaluating an energy storage resource according to any one of claims 7 to 8.
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| CN114579659A (en) * | 2022-03-07 | 2022-06-03 | 山东云储新能源科技有限公司 | System and method for estimating and sorting utilization potential of power battery by gradient utilization |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114579659A (en) * | 2022-03-07 | 2022-06-03 | 山东云储新能源科技有限公司 | System and method for estimating and sorting utilization potential of power battery by gradient utilization |
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