CN111584952B - Method and system for online evaluation of electrochemical cells of energy storage power station - Google Patents
Method and system for online evaluation of electrochemical cells of energy storage power station Download PDFInfo
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/4285—Testing apparatus
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
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- H01M2220/00—Batteries for particular applications
- H01M2220/10—Batteries in stationary systems, e.g. emergency power source in plant
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Abstract
The invention relates to a method and a system for on-line evaluation of an electrochemical battery of an energy storage power station.A big data processing subsystem processes and stores data of battery state monitoring acquired in real time; then, performing data analysis based on real-time data and historical data of the battery; and finally, evaluating the battery running state from the short time scale and the long time scale. Massive real-time data such as current, voltage and temperature of a battery stack, a battery module and a battery monomer are analyzed in a short time scale, the consistency of the battery and the charge capacity of the whole energy storage power station are evaluated by analyzing characteristic fingerprint data of the temperature, internal resistance, fault and the like of batteries with different characteristics, and data are provided for the energy storage power station to participate in power grid optimization control and operation management; under a long time scale, battery capacity decline and battery service life conditions are evaluated in battery full-life cycle historical data under different working conditions based on big data analysis, and references are provided for daily operation and maintenance management and battery gradient utilization of batteries.
Description
Technical Field
The invention relates to the field of electrochemical battery charging and discharging, in particular to a method and a system for online evaluation of an electrochemical battery of an energy storage power station.
Background
The energy storage technology adds a storage link in 'electric energy production, transmission, distribution and consumption' of an electric power system, so that the original almost 'rigid' system becomes 'flexible'. The high-capacity battery energy storage has the characteristics of quickly absorbing energy and timely releasing the energy, can realize time migration of the energy, and solves the problem of insufficient power supply abundance of a system caused by output fluctuation and uncertainty of new energy power generation. With the technical breakthrough in the aspects of battery materials, manufacturing processes, system integration, operation maintenance and the like, the manufacturing and operation costs of energy storage are greatly reduced, and the development potential of the energy storage system is huge. The electrochemical energy storage becomes the first choice of the energy storage power station battery by the characteristics of high energy density, high charge and discharge rate and long service life, but the safety and reliability of the electrochemical energy storage battery are always the problems that the application of the electrochemical energy storage battery needs to pay attention to:
on one hand, the energy is large, the voltage is high, most of the electrolyte is organic inflammable substances, and improper application can cause the temperature rise, ignition and even explosion of the battery;
on the other hand, the overcharge and overdischarge of the electrochemical energy storage battery can cause the change of the characteristics of the internal materials of the battery, so that irreversible loss is caused, and the performance is reduced;
furthermore, because of the difference of the process, the internal resistances of the batteries are often inconsistent, and the performance of the single batteries in the battery pack is unbalanced along with the cycle of charging and discharging, so that the service life of the battery pack is shortened, and the performance of the battery pack is reduced.
In summary, an intelligent and efficient state monitoring and online evaluation system must be matched with the energy storage battery, a battery energy storage online evaluation system supporting energy storage health management and safety management is established, real-time running state perception and health state evaluation of dynamic working conditions are integrated, and effective safety and reliability management is performed on the battery.
Disclosure of Invention
The invention aims to provide a method and a system for online evaluation of electrochemical cells of an energy storage power station, which integrate big data analysis, visual maintenance and fine management of the energy storage power station, can realize data access, processing and analysis of the whole life cycle of each cell of the energy storage power station, and complete the functions of whole life cycle monitoring, state analysis, online evaluation and the like of the energy storage power station, a cell stack, a cell module and the cell.
To solve the above problems, a first aspect of the present invention provides a method for online evaluation of an electrochemical cell of an energy storage power station, comprising:
and collecting battery state monitoring data in real time.
And the model normalizes and processes the battery data, and stores the acquired battery data in a business mode.
Setting different time thresholds, and analyzing the battery data according to the different time thresholds.
The battery state is evaluated according to the 'characteristic fingerprint' of the batteries with different characteristics.
According to one embodiment of the invention, the model normalization processing the battery data comprises:
establishing various energy storage power station service models, classifying and dividing batteries into single batteries, battery modules and battery stacks, and standardizing the corresponding relation between the battery service models and different battery categories.
And converting the battery four-remote data acquired in real time into service data related to a battery service model, and storing the data according to the service model.
According to an embodiment of the present invention, the converting the battery data collected in real time into the service data related to the battery service model includes: the device template is built by using the collection point table of the battery collection device. And defining the data service information type in the template according to the information model and the code of the battery service data.
Instantiating the device according to the device template. And establishing a data table of the data of the battery equipment battery stack, the battery module and the battery monomer according to the equipment, the attribute and the equipment attribute value and the battery model.
According to one embodiment of the invention, the setting of different time thresholds, and the describing of the characteristic fingerprint of the battery running state through the multi-angle threshold so as to analyze the battery data by using the characteristic fingerprint comprise: and analyzing the battery data by adopting methods of statistical calculation, difference value analysis, relevance analysis, gray scale analysis and probability analysis.
According to one embodiment of the invention, the battery data comprises: battery voltage, battery internal resistance, battery capacity.
According to an embodiment of the present invention, the setting different time thresholds and analyzing the battery data for different time thresholds includes:
setting a first threshold time, analyzing the consistency of the battery, and comprising the following steps: and calculating the pressure difference and the internal resistance difference of the parallel cell stacks, and judging the state of charge of the power station related to the cell stacks to obtain a cell consistency evaluation result.
Setting a second threshold time, evaluating the service life of the battery and correcting the available battery capacity, wherein the method comprises the following steps: the method comprises the steps of analyzing the data of the battery full life cycle process of a battery stack, a battery module and a battery monomer by using a gray scale method and an incremental analysis method, establishing a battery capacity decline model based on the nominal data of the battery cycle life, extracting the characteristics of a charge-discharge curve, evaluating whether the battery life is ended or not according to the inconsistency of characteristic variables, evaluating the overall life of a battery pack and the battery stack and correcting the available battery capacity.
Another aspect of the invention provides a system for online evaluation of an energy storage power station electrochemical cell, comprising: and the acquisition unit is used for acquiring the battery data in real time and storing the acquired battery data.
And the processing unit is used for carrying out model normalization processing on the battery data.
And the analysis unit is used for setting different time thresholds and analyzing the battery data according to the different time thresholds. And the evaluation unit is used for evaluating the battery state according to different analysis results.
According to an embodiment of the invention, the processing unit comprises: and the model standardization unit is used for establishing various energy storage power station service models, classifying the batteries into single batteries, battery modules and battery stacks, and standardizing the corresponding relation between the battery service models and different battery categories.
And the data conversion unit is used for converting the battery data acquired in real time into service data related to the battery service model.
According to an embodiment of the present invention, the data conversion unit includes: and the template building module is used for building a device template according to the collection point table of the battery collection device.
And the code definition module is used for defining the data service information type in the template according to the information model of the battery service data and the code.
An instantiation module to instantiate a device according to the device template.
And the data corresponding module is used for establishing a data table according to the equipment, the attribute and the equipment attribute value of the data of the battery equipment battery stack, the battery module and the battery monomer and the battery model.
According to an embodiment of the invention, the analysis unit comprises:
and the first analysis unit is used for setting a first threshold time and analyzing the consistency of the batteries.
And the second analysis unit is used for setting a second threshold time, evaluating the service life of the battery and correcting the available battery capacity. The method comprises the following steps: the first analysis module is used for analyzing the battery full life cycle process data of the battery stack, the battery module and the battery monomer; the second analysis module is used for establishing a battery capacity decline model according to the nominal data of the battery cycle life; and the third analysis module is used for extracting the charging and discharging curve data according to the characteristics, evaluating whether the service life of the battery is ended, evaluating the whole service life of the battery pack and the battery stack and correcting the available battery capacity.
In summary, the present invention provides a method and system for online evaluation of electrochemical cells of an energy storage power station, the method comprising: firstly, a big data processing subsystem processes and stores battery data acquired in real time; then, performing data analysis based on real-time data and historical data of the battery; and finally, evaluating the battery running state from the short time scale and the long time scale. Massive real-time data such as current, voltage and temperature of a battery stack, a battery module and a battery monomer are analyzed in a short time scale, battery consistency and the charge capacity of the whole energy storage power station are evaluated by analyzing characteristic fingerprint data of batteries with different characteristics, temperature, internal resistance, faults and the like, and data are provided for the energy storage power station to participate in power grid optimization control and operation management; under a long time scale, battery capacity decline and battery service life conditions are evaluated in battery full-life cycle historical data under different working conditions based on big data analysis, and references are provided for daily operation and maintenance management and battery gradient utilization of batteries.
Drawings
FIG. 1 is a schematic structural diagram of an energy storage power station disclosed in an embodiment of the invention;
FIG. 2 is a functional diagram of an online battery evaluation system according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a battery service model building process disclosed in an embodiment of the present invention;
FIG. 4 is a flow chart of a method for online evaluation of electrochemical cells of an energy storage power station, according to an embodiment of the present disclosure;
FIG. 5 is a block diagram of a system for online evaluation of electrochemical cells of an energy storage power station, according to an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
The MW-level high-capacity energy storage power station is composed of thousands of single batteries, a plurality of battery monomers are connected in series to form a battery module, and then the battery modules are connected in parallel/in series to form a battery stack. The structure of the energy storage power station is shown in figure 1, wherein a single battery stack or a plurality of battery stacks are connected in parallel and are connected into a power grid after energy interaction is carried out through a PCS.
The electrochemical cell online evaluation system of the energy storage power station collects the running information and the state data of the energy storage power station, the cell stack, the cell module and the cell monomer from a cell BMS system, an energy storage PCS, a measurement and control device and the like, the number of the cell monomers is the largest, the collection amount is large and the cell monomers need to be stored, the total data scale of the MW-level large-capacity energy storage power station is hundreds of thousands to millions of orders, and the online evaluation function of the cell needs to be completed under the support of a large data processing subsystem and a large data analysis subsystem, as shown in figure 2.
The online evaluation subsystem of the electrochemical battery of the energy storage power station comprises the short-time scale (5-15 minutes) and the long-time scale (1 day) for evaluating the battery state of the energy storage power station. The battery state evaluation of different time scales provides data support for requirements of energy storage power station optimization control, operation management, battery echelon utilization and the like.
The method comprises the steps of analyzing massive real-time data such as current, voltage and temperature of a battery stack, a battery module and a battery monomer under a short time scale, evaluating the reliability and accuracy of the acquired data of the battery monomer, the battery module and the battery stack by methods such as transverse data comparison and associated data analysis, after preprocessing the acquired data, evaluating the consistency of the battery by analyzing characteristic fingerprint data of batteries with different characteristics, temperature, internal resistance, faults and the like, and providing data for the energy storage power station to participate in power grid optimization control and operation management. Under a long time scale, battery capacity decline and battery service life conditions are evaluated in battery full-life cycle historical data under different working conditions based on big data analysis, and references are provided for daily operation and maintenance management and battery gradient utilization of batteries.
The invention discloses an energy storage power station electrochemical cell online evaluation system, which comprises the following contents: firstly, a big data processing subsystem processes and stores battery data acquired in real time; then, performing data analysis based on real-time data and historical data of the battery; and finally, evaluating the battery running state from the short time scale and the long time scale.
The data processing flow of the big data processing subsystem is as follows:
firstly, establishing various energy storage power station service models through a configuration tool, dividing battery information into single batteries, battery modules and battery stacks, and establishing an association relation among the service equipment;
and converting the remote signaling and remote measuring data into service information data with the meaning of battery service. The implementation mode is as follows: firstly, establishing a device template by using a collection point table of a battery collection device, defining the data service information type in the template according to an information model and a code of battery service data, and instantiating the device according to the device template, wherein all four-remote data comprise attributes such as a point number, a device number, a service information type and the like; in order to further facilitate the upper layer application to call data, the big data processing subsystem models the four-remote data service, and establishes a data table according to the device, the attribute and the device attribute value of the data of the battery device battery stack, the battery module and the battery cell and according to the battery model, as shown in fig. 3. Taking a cell stack as an example:
| cell stack equipment number | Heap attribute 1 | Heap attribute 1 | …… | Heap attribute n |
| Serial number | Attribute value | Attribute value | Attribute value | Attribute value |
The big data analysis subsystem provides basic algorithm support for high-level application calling according to a data processing method used by the battery online evaluation. The method mainly comprises the following steps:
1) statistical calculation including maximum, minimum, average, sorting, etc.;
2) the extreme difference value analysis is used for analyzing the difference value of the two attribute values, and for the analysis of the safety of the battery, the voltage difference and the temperature difference are more harmful when being out of limit;
3) performing relevance analysis, wherein a business relevance relation exists between the two attribute values or the change trend of the business relevance relation has a relevance relation;
4) and gray scale analysis, namely judging the similarity relation with the value of the unknown attribute with the incidence relation by using the data change time period value of the known attribute.
5) And probability analysis, which analyzes the probability of the event according to the historical data.
The online evaluation of the battery state is divided into a short-time scale evaluation and a long-time scale evaluation. Firstly, according to the evaluation index under each state, calculating the evaluation parameter, and finally obtaining the evaluation conclusion.
The evaluation index of the short time scale mainly aims at the evaluation of the state of charge (SOC) and the consistency of the battery of the energy storage power station. For a large-scale energy storage power station, the large-scale energy storage power station is composed of a plurality of battery stacks, and the SOC value of the whole energy storage power station is reasonably estimated under the condition that the SOC values sent by the battery stacks are inconsistent. In order to avoid the jump of the SOC value during the charge-discharge change and reduce the influence of the short plate effect of the battery, a battery consistency quick estimation model is established based on the distribution characteristics and the correlation of the voltage and the internal resistance of the battery, and the comprehensive performance index of the battery consistency is calculated. The specific implementation method comprises the following steps: calculating the differential pressure and the internal resistance difference of the parallel battery stacks, judging the battery consistency according to a battery consistency quick estimation model, taking the battery stack mean value as the reference in the BMS equalization range of the battery consistency, taking the SOC of the battery stack with high voltage as the reference in the charging process and the SOC of the battery stack with low voltage as the reference in the discharging process according to the charging and discharging state of a PCS (personal computer) outside the BMS equalization range, expanding the SOC values of all batteries under the PCS according to the capacity after the reference is determined, and then adding the SOC values of all the batteries under the PCS according to the capacity ratio to obtain the SOC of the whole energy storage power station.
The evaluation index of the long time scale is mainly an evaluation aiming at the service life of the battery and the capacity attenuation of the battery. The specific implementation method comprises the following steps: the method comprises the steps of analyzing the whole life cycle process data of a battery stack, a battery module and a battery monomer by using a gray scale method and an incremental analysis method according to the principle from whole to part, establishing a battery capacity decline model based on the battery cycle life nominal data, extracting characteristics of a charge-discharge curve, and identifying the battery monomer reaching the end-of-life state in a battery pack under the online condition according to the inconsistency of characteristic variables, so that the evaluation of the whole battery life of the energy storage power station and the correction of the available battery capacity are realized.
As shown in fig. 4, a method for online evaluation of an energy storage power station electrochemical cell, comprising: s101: and collecting battery state monitoring data in real time.
S102: and carrying out model normalization processing on the battery data. The method comprises the following steps: establishing various energy storage power station service models, classifying and dividing batteries into single batteries, battery modules and battery stacks, and standardizing the corresponding relation between the battery service models and different battery categories. And converting the battery data acquired in real time into service data related to the battery service model, and storing the service data.
Converting the battery data collected in real time into service data related to a battery service model, comprising: the device template is built by using the collection point table of the battery collection device. And defining the data service information type in the template according to the information model and the code of the battery service data.
Instantiating the device according to the device template. And establishing a data table of the data of the battery equipment battery stack, the battery module and the battery monomer according to the equipment, the attribute and the equipment attribute value and the battery model.
S103: setting different time thresholds, and analyzing the battery data according to the different time thresholds. The setting of different time thresholds, establishing "characteristic fingerprints" for batteries of different characteristics, and then analyzing the battery data by means of the different time thresholds, comprises: and analyzing the battery data by adopting methods of statistical calculation, difference value analysis, relevance analysis, gray scale analysis and probability analysis.
The battery data includes: battery voltage, battery internal resistance, battery capacity.
Setting different time thresholds, and analyzing the battery data according to the different time thresholds, wherein the steps comprise:
setting a first threshold time, analyzing the consistency of the battery, and comprising the following steps: and calculating the pressure difference and the internal resistance difference of the parallel cell stacks, and judging the state of charge of the power station related to the cell stacks to obtain a cell consistency evaluation result.
Setting a second threshold time, evaluating the service life of the battery and correcting the available battery capacity, wherein the method comprises the following steps: the method comprises the steps of analyzing the whole life cycle process data of a battery stack, a battery module and a single battery by using a gray scale method and an incremental analysis method according to the principle from whole to part, establishing a battery capacity decline model based on the battery cycle life nominal data, extracting characteristics of a charge-discharge curve, evaluating whether the battery life is ended or not according to the inconsistency of characteristic variables, evaluating the whole life of a battery pack and the battery stack and correcting the available battery capacity.
S104: the battery state is evaluated according to the 'characteristic fingerprint' of the batteries with different characteristics.
The invention relates to a method for on-line evaluation of an electrochemical battery of an energy storage power station, which comprises the following steps that firstly, a big data processing subsystem processes battery state monitoring data acquired in real time; then, performing data analysis and storage based on the real-time data and the historical data of the battery; and finally, evaluating the battery running state from the short time scale and the long time scale. Massive real-time data such as current, voltage and temperature of a battery stack, a battery module and a battery monomer are analyzed in a short time scale, the consistency of the battery is evaluated based on the analysis of characteristic fingerprint data of batteries with different characteristics, temperature, internal resistance, faults and the like, and the charge capacity of the whole energy storage power station provides data for the energy storage power station to participate in power grid optimization control and operation management; under a long time scale, battery capacity decline and battery service life conditions are evaluated in battery full-life cycle historical data under different working conditions based on big data analysis, and references are provided for daily operation and maintenance management and battery gradient utilization of batteries.
Another aspect of the invention provides a system 500 for online evaluation of electrochemical cells of an energy storage power station, as shown in fig. 5, comprising: and the acquisition unit 501 is used for acquiring the battery state monitoring data in real time.
And the processing unit 503 is configured to perform model normalization processing on the battery data, and perform business data storage.
An analyzing unit 505 is configured to set different time thresholds, and establish a "characteristic fingerprint" of the battery status to analyze the battery data for the different time thresholds.
An evaluation unit 507 for evaluating the battery status according to the "characteristic fingerprint" of the batteries with different characteristics.
According to an embodiment of the invention, the processing unit comprises: and the model standardization unit is used for establishing various energy storage power station service models, classifying the batteries into single batteries, battery modules and battery stacks, and standardizing the corresponding relation between the battery service models and different battery categories.
And the data conversion unit is used for converting the battery data acquired in real time into service data related to the battery service model.
According to an embodiment of the present invention, the data conversion unit includes: and the template building module is used for building a device template according to the collection point table of the battery collection device.
And the code definition module is used for defining the data service information type in the template according to the information model of the battery service data and the code.
An instantiation module to instantiate a device according to the device template.
And the data corresponding module is used for establishing a data table according to the equipment, the attribute and the equipment attribute value of the data of the battery equipment battery stack, the battery module and the battery monomer and the battery model.
According to an embodiment of the invention, the analysis unit comprises:
and the first analysis unit is used for setting a first threshold time and analyzing the consistency of the batteries.
And the second analysis unit is used for setting a second threshold time, evaluating the service life of the battery and correcting the available battery capacity. The method comprises the following steps: the first analysis module is used for analyzing the battery full life cycle process data of the battery stack, the battery module and the battery monomer; the second analysis module is used for establishing a battery capacity decline model according to the nominal data of the battery cycle life; and the third analysis module is used for extracting the charging and discharging curve data according to the characteristics, evaluating whether the service life of the battery is ended, evaluating the whole service life of the battery pack and the battery stack and correcting the available battery capacity.
The invention relates to a system for on-line evaluation of an electrochemical battery of an energy storage power station, which comprises a big data processing subsystem, a big data processing subsystem and a big data processing subsystem, wherein the big data processing subsystem is used for processing and storing battery data acquired in real time; then, performing data analysis based on real-time data and historical data of the battery; and finally, evaluating the battery running state from the short time scale and the long time scale. Massive real-time data such as current, voltage and temperature of a battery stack, a battery module and a battery monomer are analyzed in a short time scale, the consistency of the battery is evaluated based on the analysis of characteristic fingerprint data of batteries with different characteristics, temperature, internal resistance, faults and the like, and the charge capacity of the whole energy storage power station provides data for the energy storage power station to participate in power grid optimization control and operation management; under a long time scale, battery capacity decline and battery service life conditions are evaluated in battery full-life cycle historical data under different working conditions based on big data analysis, and references are provided for daily operation and maintenance management and battery gradient utilization of batteries.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.
Claims (8)
1. The method for the online evaluation of the electrochemical cells of the energy storage power station is characterized in that the energy storage power station is formed by connecting a plurality of single cells in series to form a cell module, then connecting a plurality of cell modules in parallel/in series to form a cell stack, connecting one or a plurality of cell stacks in parallel to perform energy interaction through a PCS (process control system) and then connecting the cell stacks into the energy storage power station, and the method comprises the following steps:
collecting battery state monitoring data in real time;
the model normalizes and processes the battery data, and stores the acquired battery data in a business mode;
setting different time thresholds, and analyzing the battery data according to the different time thresholds, wherein the steps comprise:
setting a first threshold time, calculating the pressure difference and the internal resistance difference of the parallel cell stacks, judging the consistency of the cells according to a cell consistency quick estimation model, and judging the state of charge (SOC) of a power station related to the cell stacks to obtain a cell consistency evaluation result; the battery consistency is based on the battery stack mean value in the BMS balance range, the SOC of the battery stack with high voltage is used as the reference in the charging process and the SOC of the battery stack with low voltage is used as the reference in the discharging process according to the charging and discharging state of the PCS outside the BMS balance range, and the SOC values of all the batteries under the PCS are expanded according to the capacity after the reference is determined; adding the SOC values of the batteries under all PCS according to the capacity ratio to obtain the SOC of the whole energy storage power station;
setting a second threshold time, and analyzing the battery full life cycle process data of the battery stack, the battery module and the single battery; establishing a battery capacity decline model based on the battery cycle life nominal data; extracting charging and discharging curve data according to the characteristics, evaluating whether the service life of the battery is ended, evaluating the overall service life of the battery pack and the battery stack and correcting the available battery capacity;
the battery state is evaluated according to the 'characteristic fingerprint' of the batteries with different characteristics.
2. The method of claim 1, wherein the model normalizes the battery data, comprising:
establishing various energy storage power station battery service models, classifying and dividing batteries into single batteries, battery modules and battery stacks, and standardizing the corresponding relation between the battery service models and different battery categories;
and converting the battery four-remote data acquired in real time into service data related to a battery service model, and storing the data according to the service model.
3. The method of claim 2, wherein converting the real-time collected battery data into business data associated with a battery business model comprises:
establishing a device template by using a collection point table of the battery collection device;
defining the data service information type in the template according to the information model and the code of the battery service data;
instantiating a device according to a device template;
and establishing a data table of the data of the battery equipment battery stack, the battery module and the battery monomer according to the equipment, the attribute and the equipment attribute value and the battery model.
4. The method of claim 1, wherein the setting different time thresholds, the "feature fingerprint" of the battery operation status is depicted by a multi-angle threshold, so as to analyze the battery data by using the "feature fingerprint", comprises: and analyzing the battery data by adopting methods of statistical calculation, difference value analysis, relevance analysis, gray scale analysis and probability analysis.
5. The method of claim 1, wherein the battery data comprises: battery voltage, battery internal resistance, battery capacity.
6. A system for online evaluation of electrochemical cells of an energy storage power station, the energy storage power station is formed by connecting a plurality of battery monomers in series into a battery module, then connecting a plurality of battery modules in parallel/in series into a battery stack, connecting a single or a plurality of battery stacks in parallel and performing energy interaction through a PCS (process control system) and then accessing the energy storage power station, and the system is characterized by comprising:
the acquisition unit is used for acquiring the battery data in real time and storing the acquired battery data;
the processing unit is used for carrying out model normalization processing on the battery data;
the analysis unit comprises a first analysis unit and a second analysis unit, wherein the first analysis unit is used for setting a first threshold time and analyzing the consistency of the batteries, and comprises the steps of judging the consistency of the batteries according to a quick battery consistency estimation model and judging the state of charge (SOC) of a power station related to the battery stack to obtain a battery consistency evaluation result; the battery consistency is based on the battery stack mean value in the BMS balance range, the SOC of the battery stack with high voltage is used as the reference in the charging process and the SOC of the battery stack with low voltage is used as the reference in the discharging process according to the charging and discharging state of the PCS outside the BMS balance range, and the SOC values of all the batteries under the PCS are expanded according to the capacity after the reference is determined; adding the SOC values of the batteries under all PCS according to the capacity ratio to obtain the SOC of the whole energy storage power station;
the second analysis unit is used for setting a second threshold time, evaluating the service life of the battery and correcting the available battery capacity, and comprises: the first analysis module is used for analyzing the battery full life cycle process data of the battery stack, the battery module and the battery monomer; the second analysis module is used for establishing a battery capacity decline model according to the nominal data of the battery cycle life; the third analysis module is used for extracting charging and discharging curve data according to characteristics, evaluating whether the service life of the battery is ended or not, evaluating the whole service life of the battery pack and the battery stack and correcting the available battery capacity;
and the evaluation unit is used for evaluating the battery state according to different analysis results.
7. The system of claim 6, wherein the processing unit comprises:
the model standardization unit is used for establishing various energy storage power station battery service models, classifying the batteries into single batteries, battery modules and battery stacks, and standardizing the corresponding relation between the battery service models and different battery categories;
and the data conversion unit is used for converting the battery data acquired in real time into service data related to the battery service model.
8. The system of claim 7, wherein the data conversion unit comprises:
the template building module is used for building a device template according to the collection point table of the battery collection device;
the coding definition module is used for defining the data service information type in the template according to the information model of the battery service data and the coding;
an instantiation module to instantiate a device according to a device template;
and the data corresponding module is used for establishing a data table according to the equipment, the attribute and the equipment attribute value of the data of the battery equipment battery stack, the battery module and the battery monomer and the battery model.
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