System and method for dynamically calibrating SOH of power battery of energy storage system based on big data

Abstract

The invention discloses an SOH system and method for dynamically calibrating a power battery of an energy storage system based on big data. The SOH system comprises a data acquisition module, a data storage module, a data analysis module and an SOH calibration module. Charge and discharge historical data of the power battery of the energy storage system are extracted, and through analysis of a big data algorithm, an SOC change trend table in the whole charge and discharge period from charging to the top end (full) and from discharging to the tail end (emptying) of the battery is obtained, so that an SOC minimum jump coefficient is obtained, and an SOC calibration coefficient is calculated based on the coefficient. And calculating the real SOH according to the calibration coefficient and the current SOH set value of the battery, and dynamically calibrating the power battery of the energy storage system.

Description

Technical Field

[0001] The present invention belongs to the field of energy storage system management, and specifically relates to a system and method for dynamically calibrating a power battery SOH of an energy storage system based on big data. Background Art

[0002] After long-term operation, the power battery of the energy storage system will decay and age with the increase of charge and discharge times and the influence of environmental factors such as temperature, causing the actual SOH value of the battery to deviate from the nominal SOH value (100%) when the battery leaves the factory. In addition, the existing SOC statistical methods, whether it is the cumulative power integration method, the voltage method, the Kalman filter method, etc., are not yet mature, and cannot fully and truly reflect the SOC of the current energy storage system battery. In addition, the cumulative power integration method is mostly used to calculate SOC on the market, which leads to SOC data jumps at the top of the charge and the end of the discharge when the energy storage system is charged and discharged at high power, thereby affecting the user's control of the capacity and charge and discharge capabilities of the entire energy storage system. Therefore, it is necessary to adjust the SOH value in time after the battery decays to ensure that the SOC is more accurate and the changes are more stable, so as to achieve safer, more stable and controllable operation of the energy storage system. The SOH estimation in the market is based on the regular full charge and discharge of the battery, and the BMS calculates the SOH value, but it is difficult for many customers to do this regularly in actual applications or it greatly increases the customer's maintenance costs.

[0003] In view of the above problems, the present invention proposes a method for dynamically calibrating the SOH of a power battery of an energy storage system based on big data. Summary of the invention

[0004] The technical problem to be solved by the present invention is to address the deficiencies of the above-mentioned prior art and to provide a system and method for dynamically calibrating the SOH of the power battery of the energy storage system based on big data, which makes full use of the historical storage data of the energy storage system's charge and discharge for feature analysis. It does not require additional data sources, and does not require complicated system installation and configuration. The cost is controllable, and the system is safe and reliable.

[0005] The present invention discloses a power battery SOH system for dynamically calibrating an energy storage system based on big data, comprising:

[0006] A data acquisition module is used to collect real-time data on the charging and discharging of the power battery of the energy storage system and transmit the data to the data storage module;

[0007] A data storage module, used to store real-time data of power battery charging and discharging of the energy storage system and historical data of power battery charging and discharging;

[0008] The data analysis module is used to extract the power battery charge and discharge history data stored in the data storage module, and obtain the SOC change trend table of the battery during the entire charge and discharge cycle from charging to the top to discharging to the end through big data algorithm analysis, and extract the SOC minimum jump coefficient from the SOC change trend table, and output the extracted SOC minimum jump coefficient to the SOH calibration module;

[0009] The SOH calibration module calculates the SOC calibration coefficient based on the acquired SOC minimum jump coefficient, calculates the real SOH according to the SOC calibration coefficient and the current battery SOH setting value, calibrates the real SOH, and outputs the calibrated SOH to the energy storage system.

[0010] The present invention also discloses a method for dynamically calibrating the SOH of a power battery of an energy storage system based on big data, comprising the following steps:

[0011] Step 1: The data acquisition module connects to the energy storage system and collects real-time data of the power battery charging and discharging of the energy storage system, and transmits the data to the data storage module;

[0012] Step 2: The data storage module stores the real-time data of the power battery charge and discharge of the energy storage system and the historical data of the power battery charge and discharge;

[0013] Step 3: The data analysis module extracts the power battery charge and discharge history data stored in the data storage module, and obtains the SOC change trend table of the battery during the entire charge and discharge cycle from charging to the top to discharging to the end through big data algorithm analysis, and extracts the SOC minimum jump coefficient from the SOC change trend table, and outputs the extracted SOC minimum jump coefficient to the SOH calibration module;

[0014] Step 4: The SOH calibration module calculates the SOC calibration coefficient based on the obtained SOC minimum jump coefficient, calculates the real SOH according to the SOC calibration coefficient and the current battery SOH setting value, calibrates the real SOH, and outputs the calibrated SOH to the energy storage system.

[0015] In one embodiment, in step 3, the power battery charge and discharge history data stored in the data storage module is extracted, specifically as follows:

[0016] Use appropriate interfaces to connect to a data storage module that stores historical data on power battery charge and discharge;

[0017] Write query statements or call corresponding APIs to extract required charging and discharging history data. The data should contain key information such as timestamp, charging / discharging status, current, voltage, SOC, etc.

[0018] Remove invalid, abnormal or duplicate data records to ensure data accuracy and consistency;

[0019] Convert data into a format suitable for analysis by big data algorithms, such as time series data.

[0020] In one embodiment, in step 3, the big data algorithm analysis is as follows:

[0021] Use time series analysis technology to analyze the charging and discharging history data of the power battery and identify the change trend of SOC;

[0022] Extract key features from time series data, including the rate of change and fluctuation of SOC. If the data volume is large and complex, use clustering or classification algorithms to further analyze the change pattern of SOC.

[0023] In one embodiment, in step 3, a SOC variation trend table of the battery during the entire charge and discharge cycle from charging to the top end to discharging to the end end is obtained, which is as follows:

[0024] According to the analysis results, a table containing timestamp and SOC value information is constructed to show the changing trend of SOC during the entire charge and discharge cycle, and a chart is used to show the changing trend of SOC.

[0025] In one embodiment, in step 3, the SOC minimum jump coefficient is extracted from the SOC change trend table, as follows:

[0026] For adjacent data points in the SOC change trend table, their SOC differences are calculated and then divided by the adjacent time interval to obtain the jump coefficient;

[0027] Traverse all jump coefficients and find the minimum value, which is the minimum SOC jump coefficient.

[0028] In one embodiment, step three also includes result verification and optimization, as follows:

[0029] By comparing actual data with algorithm analysis results, the accuracy of the SOC change trend table and the SOC minimum jump coefficient is verified;

[0030] Based on the verification results, the big data algorithm is tuned to improve the accuracy and efficiency of the analysis.

[0031] In one embodiment, in step 4, the SOC calibration coefficient is calculated based on the obtained SOC minimum jump coefficient, as follows:

[0032] The SOC calibration coefficient is calculated using the SOC minimum jump coefficient based on the specific characteristics of the battery and experimental data. The calculation formula is: KSOC=f(CSOC), where KSOC is the SOC calibration coefficient, CSOC is the SOC minimum jump coefficient, and f is the functional relationship between the SOC calibration coefficient and the minimum jump coefficient.

[0033] In one embodiment, in step 4, the actual SOH is calculated based on the SOC calibration coefficient and the current battery SOH setting value, as follows:

[0034] The real SOH is calculated by the formula SOHreal=g(KSOC,SOHset), where SOHreal is the real SOH, g is the functional relationship among the SOC calibration coefficient, the current battery SOH setting value and the real SOH, KSOC is the SOC calibration coefficient, and SOHset is the current battery SOH setting value.

[0035] In one embodiment, in step 4, the real SOH is calibrated as follows:

[0036] The average value of the current measurement value and the last calibration value is the final SOH value.

[0037] The beneficial effects of the present invention are:

[0038] 1. The present invention makes full use of the historical storage data of the energy storage system for charge and discharge to perform feature analysis, without the need for additional data sources, complicated system installation and configuration, controllable costs, and a safe and reliable system;

[0039] 2. The present invention can dynamically adjust the SOH of the power battery of the energy storage system without manual intervention, has a high degree of intelligence, and adopts a variety of error correction algorithms to ensure that the SOH value is accurate and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0041] The present invention will be further described in detail below with reference to specific embodiments.

[0042] The present invention discloses a power battery SOH system for dynamically calibrating an energy storage system based on big data, comprising:

[0043] A data acquisition module is used to collect real-time data on the charging and discharging of the power battery of the energy storage system and transmit the data to the data storage module;

[0044] A data storage module, used to store real-time data of power battery charging and discharging of the energy storage system and historical data of power battery charging and discharging;

[0045] The data analysis module is used to extract the power battery charge and discharge history data stored in the data storage module, and obtain the SOC change trend table of the battery during the entire charge and discharge cycle from charging to the top to discharging to the end through big data algorithm analysis, and extract the SOC minimum jump coefficient from the SOC change trend table, and output the extracted SOC minimum jump coefficient to the SOH calibration module;

[0046] The SOH calibration module calculates the SOC calibration coefficient based on the acquired SOC minimum jump coefficient, calculates the real SOH according to the SOC calibration coefficient and the current battery SOH setting value, calibrates the real SOH, and outputs the calibrated SOH to the energy storage system.

[0047] In this system, the specific functions of each functional module are as follows:

[0048] Data acquisition module: This module is used to collect real-time charging and discharging data of the power battery of the energy storage system, including but not limited to key parameters such as current, voltage, temperature, etc. The collected data will be transmitted to the data storage module in real time.

[0049] Data storage module: This module is used to store real-time data transmitted by the data acquisition module, as well as the charging and discharging history data of the power battery. The data storage module should have efficient data management capabilities to ensure the integrity and security of the data.

[0050] Data analysis module: This module extracts the charge and discharge history data of the power battery from the data storage module, and uses the big data algorithm to analyze it, and obtains the SOC change trend table of the battery during the entire charge and discharge cycle from charging to the top and discharging to the end. On this basis, the SOC minimum jump coefficient is further extracted, which reflects the stability of the SOC change of the battery during the charge and discharge process.

[0051] SOH calibration module: This module receives the SOC minimum jump coefficient output by the data analysis module and calculates the SOC calibration coefficient based on the coefficient. Then, the real SOH is calculated based on the SOC calibration coefficient and the current battery SOH setting value. Finally, the real SOH is calibrated and the calibrated SOH is output to the energy storage system for further decision-making and optimization of the system.

[0052] The above system has the following advantages:

[0053] 1. Real-time collection and storage of power battery charging and discharging data, providing a rich data source for subsequent data analysis.

[0054] 2. The SOC change trend table and SOC minimum jump coefficient are obtained through big data algorithm analysis, which improves the accuracy of battery performance status assessment.

[0055] 3. SOH calibration based on the minimum SOC jump coefficient enables accurate assessment of battery health status, which helps to extend battery life and improve the overall performance of the energy storage system.

[0056] The present invention also discloses a method for dynamically calibrating the SOH of a power battery of an energy storage system based on big data, comprising the following steps:

[0057] Step 1: The data acquisition module connects to the energy storage system and collects real-time data of the power battery charging and discharging of the energy storage system, and transmits the data to the data storage module;

[0058] Step 2: The data storage module stores the real-time data of the power battery charge and discharge of the energy storage system and the historical data of the power battery charge and discharge;

[0059] Step 3: The data analysis module extracts the power battery charge and discharge history data stored in the data storage module, and obtains the SOC change trend table of the battery during the entire charge and discharge cycle from charging to the top to discharging to the end through big data algorithm analysis, and extracts the SOC minimum jump coefficient from the SOC change trend table, and outputs the extracted SOC minimum jump coefficient to the SOH calibration module;

[0060] Step 4: The SOH calibration module calculates the SOC calibration coefficient based on the obtained SOC minimum jump coefficient, calculates the real SOH according to the SOC calibration coefficient and the current battery SOH setting value, calibrates the real SOH, and outputs the calibrated SOH to the energy storage system.

[0061] In this embodiment, in step 3, the power battery charge and discharge history data stored in the data storage module is extracted, as follows:

[0062] Use appropriate interfaces to connect to a data storage module that stores historical data on power battery charge and discharge;

[0063] Write query statements or call corresponding APIs to extract required charging and discharging history data. The data should contain key information such as timestamp, charging / discharging status, current, voltage, SOC, etc.

[0064] Remove invalid, abnormal or duplicate data records to ensure data accuracy and consistency;

[0065] Convert data into a format suitable for analysis by big data algorithms, such as time series data.

[0066] In this embodiment, in step 3, the big data algorithm analysis is as follows:

[0067] Use time series analysis technology to analyze the charging and discharging history data of the power battery and identify the change trend of SOC;

[0068] Extract key features from time series data, including the rate of change and fluctuation of SOC. If the data volume is large and complex, use clustering or classification algorithms to further analyze the change pattern of SOC.

[0069] In this embodiment, in step 3, a SOC variation trend table of the battery during the entire charge and discharge cycle from charging to the top end to discharging to the end end is obtained, which is as follows:

[0070] According to the analysis results, a table containing timestamp and SOC value information is constructed to show the changing trend of SOC during the entire charge and discharge cycle, and a chart is used to show the changing trend of SOC.

[0071] In this embodiment, in step 3, the SOC minimum jump coefficient is extracted from the SOC change trend table, as follows:

[0072] For adjacent data points in the SOC change trend table, their SOC differences are calculated and then divided by the adjacent time interval to obtain the jump coefficient;

[0073] Traverse all jump coefficients and find the minimum value, which is the minimum SOC jump coefficient.

[0074] In this embodiment, step three also includes result verification and optimization, which are as follows:

[0075] By comparing actual data with algorithm analysis results, the accuracy of the SOC change trend table and the SOC minimum jump coefficient is verified;

[0076] Based on the verification results, the big data algorithm is tuned to improve the accuracy and efficiency of the analysis.

[0077] In this embodiment, in step 4, the SOC calibration coefficient is calculated based on the obtained SOC minimum jump coefficient, which is as follows:

[0078] The SOC calibration coefficient is calculated using the SOC minimum jump coefficient based on the specific characteristics of the battery and experimental data. The calculation formula is: KSOC=f(CSOC), where KSOC is the SOC calibration coefficient, CSOC is the SOC minimum jump coefficient, and f is the functional relationship between the SOC calibration coefficient and the minimum jump coefficient.

[0079] In this embodiment, in step 4, the real SOH is calculated based on the SOC calibration coefficient and the current battery SOH setting value, as follows:

[0080] The real SOH is calculated by the formula SOHreal=g(KSOC,SOHset), where SOHreal is the real SOH, g is the functional relationship among the SOC calibration coefficient, the current battery SOH setting value and the real SOH, KSOC is the SOC calibration coefficient, and SOHset is the current battery SOH setting value.

[0081] In this embodiment, in step 4, the real SOH is calibrated as follows:

[0082] The average value of the current measurement value and the last calibration value is the final SOH value.

[0083] In the above method, each step is as follows:

[0084] Step 1: Data Collection

[0085] The data acquisition module of the present invention is connected to the energy storage system by wired or wireless means, and collects key data of the power battery during the charging and discharging process in real time, including but not limited to current, voltage, temperature, etc. These data reflect the working status of the power battery and are the basis for subsequent analysis and calibration. Once the data acquisition is completed, the module will immediately transfer the data to the data storage module for storage.

[0086] Step 2: Data storage

[0087] The data storage module is responsible for receiving and storing real-time data from the data acquisition module, as well as the previously stored power battery charge and discharge history data. In order to ensure the integrity and security of the data, the data storage module adopts efficient data management strategies, such as data backup and encrypted storage. In this way, the data analysis module can easily access the data when needed.

[0088] Step 3: Data Analysis

[0089] The data analysis module extracts the charge and discharge history data of the power battery from the data storage module and uses big data algorithms for in-depth analysis. These algorithms can identify the SOC (State of Charge) change trends during the entire charge and discharge cycle of the battery from charging to the top to discharging to the end. By analyzing these trends, the module can extract the SOC minimum jump coefficient, which reflects the stability of the SOC change of the battery during the charge and discharge process. Once the SOC minimum jump coefficient is extracted, the data analysis module will output it to the SOH calibration module for further processing.

[0090] Step 4: SOH calibration

[0091] The SOH calibration module receives the SOC minimum jump coefficient from the data analysis module and uses it to calculate the SOC calibration coefficient. This calibration coefficient is the key to adjusting the battery SOH setting value to reflect the true battery health status. Based on the SOC calibration coefficient and the current battery SOH setting value, the SOH calibration module can calculate the true SOH value and calibrate the value. The calibrated SOH value more accurately reflects the actual health status of the battery and provides strong support for the optimization decision of the energy storage system. Finally, the calibrated SOH value will be output to the energy storage system for further use by the system.

[0092] Through the above four steps, the present invention realizes an efficient and accurate method for collecting, storing, analyzing and calibrating the charge and discharge data of the power battery of the energy storage system. This method not only improves the accuracy of the battery performance status assessment, but also helps to extend the battery life and improve the overall performance of the energy storage system.

[0093] In the above method, in KSOC=f(CSOC), f represents a function that maps CSOC (a physical quantity or parameter) to KSOC (another physical quantity or result).

[0094] In step 3, when processing big data, ensure the scalability and performance of the algorithm. Data cleaning and preprocessing are key steps to ensure the accuracy of the analysis results. Visualization tools can help better understand the data and algorithm analysis results. In practical applications, the algorithm may need to be customized and optimized according to the specific type and characteristics of the power battery.

[0095] In the above method, SOHreal = g(KSOC, SOHset), g is a function that calculates the real SOH value SOHreal based on the SOC calibration coefficient KSOC and the current battery SOH set value SOHset. g represents the mapping relationship from KSOC and SOHset to SOHreal. This relationship can be based on various factors such as the physical properties, chemical properties, historical data, and experimental measurements of the battery.

[0096] The specific form of the g function may be determined in the following ways:

[0097] 1. Derivation based on physical models: If we have a deep understanding of the internal mechanism of the battery, we can derive the form of the g function based on the laws of physics and chemical principles. This usually requires modeling and analysis of factors such as the battery's charging and discharging process, internal resistance, and capacity decay.

[0098] 2. Experimental data fitting: By experimentally measuring the SOH of the battery under different conditions and collecting the corresponding KSOC and SOHset values, we can use mathematical methods (such as regression analysis, machine learning algorithms, etc.) to fit a function g that best describes the data points.

[0099] 3. Empirical formula: If the form of the g function is very complex or difficult to express with a mathematical expression, you can consider using a table lookup method. That is, pre-calculate and store a series of SOHreal values ​​corresponding to KSOC and SOHset values, and obtain the results by looking up the table.

[0100] The above-mentioned embodiments only express the specific implementation of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A power battery SOH system for dynamic calibration of energy storage system based on big data, characterized in that: include: A data acquisition module is used to collect real-time data on the charging and discharging of the power battery of the energy storage system and transmit the data to the data storage module; A data storage module, used to store real-time data of power battery charging and discharging of the energy storage system and historical data of power battery charging and discharging; The data analysis module is used to extract the power battery charge and discharge history data stored in the data storage module, and obtain the SOC change trend table of the battery during the entire charge and discharge cycle from charging to the top to discharging to the end through big data algorithm analysis, and extract the SOC minimum jump coefficient from the SOC change trend table, and output the extracted SOC minimum jump coefficient to the SOH calibration module; The SOH calibration module calculates the SOC calibration coefficient based on the acquired SOC minimum jump coefficient, calculates the real SOH according to the SOC calibration coefficient and the current battery SOH setting value, calibrates the real SOH, and outputs the calibrated SOH to the energy storage system.

2. A method for dynamically calibrating the SOH of a power battery in an energy storage system based on big data, characterized in that: The following steps are involved: Step 1: The data acquisition module connects to the energy storage system and collects real-time data of the power battery charging and discharging of the energy storage system, and transmits the data to the data storage module; Step 2: The data storage module stores the real-time data of the power battery charge and discharge of the energy storage system and the historical data of the power battery charge and discharge; Step 3: The data analysis module extracts the power battery charge and discharge history data stored in the data storage module, and obtains the SOC change trend table of the battery during the entire charge and discharge cycle from charging to the top to discharging to the end through big data algorithm analysis, and extracts the SOC minimum jump coefficient from the SOC change trend table, and outputs the extracted SOC minimum jump coefficient to the SOH calibration module; Step 4: The SOH calibration module calculates the SOC calibration coefficient based on the obtained SOC minimum jump coefficient, calculates the real SOH according to the SOC calibration coefficient and the current battery SOH setting value, calibrates the real SOH, and outputs the calibrated SOH to the energy storage system.

3. A method for dynamically calibrating the SOH of a power battery of an energy storage system based on big data according to claim 2, characterized in that: In step 3, the power battery charge and discharge history data stored in the data storage module is extracted, as follows: Use appropriate interfaces to connect to a data storage module that stores historical data on power battery charge and discharge; Write query statements or call corresponding APIs to extract required charging and discharging history data. The data should contain key information such as timestamp, charging / discharging status, current, voltage, SOC, etc. Remove invalid, abnormal or duplicate data records to ensure data accuracy and consistency; Convert data into a format suitable for analysis by big data algorithms, such as time series data.

4. According to claim 2, a method for dynamically calibrating the SOH of a power battery of an energy storage system based on big data is characterized in that: In step three, big data algorithm analysis is as follows: Use time series analysis technology to analyze the charging and discharging history data of the power battery and identify the change trend of SOC; Extract key features from time series data, including the rate of change and fluctuation of SOC. If the data volume is large and complex, use clustering or classification algorithms to further analyze the change pattern of SOC.

5. The method for dynamically calibrating the SOH of a power battery of an energy storage system based on big data according to claim 2 is characterized in that: In step 3, the SOC variation trend table of the battery during the entire charge and discharge cycle from charging to the top to discharging to the end is obtained, as follows: According to the analysis results, a table containing timestamp and SOC value information is constructed to show the changing trend of SOC during the entire charge and discharge cycle, and a chart is used to show the changing trend of SOC.

6. A method for dynamically calibrating the SOH of a power battery of an energy storage system based on big data according to claim 2, characterized in that: In step 3, the SOC minimum jump coefficient is extracted from the SOC change trend table, as follows: For adjacent data points in the SOC change trend table, their SOC differences are calculated and then divided by the adjacent time interval to obtain the jump coefficient; Traverse all jump coefficients and find the minimum value, which is the minimum SOC jump coefficient.

7. A method for dynamically calibrating the SOH of a power battery of an energy storage system based on big data according to claim 2, characterized in that: Step three also includes result verification and optimization, as follows: By comparing actual data with algorithm analysis results, the accuracy of the SOC change trend table and the SOC minimum jump coefficient is verified; Based on the verification results, the big data algorithm is tuned to improve the accuracy and efficiency of the analysis.

8. The method for dynamically calibrating the SOH of a power battery of an energy storage system based on big data according to claim 2 is characterized in that: In step 4, the SOC calibration coefficient is calculated based on the obtained SOC minimum jump coefficient, as follows: The SOC calibration coefficient is calculated using the SOC minimum jump coefficient based on the specific characteristics of the battery and experimental data. The calculation formula is: KSOC=f(CSOC), where KSOC is the SOC calibration coefficient, CSOC is the SOC minimum jump coefficient, and f is the functional relationship between the SOC calibration coefficient and the minimum jump coefficient.

9. The method for dynamically calibrating the SOH of a power battery of an energy storage system based on big data according to claim 2, characterized in that: In step 4, the actual SOH is calculated based on the SOC calibration coefficient and the current battery SOH setting value, as follows: The real SOH is calculated by the formula SOHreal=g(KSOC,SOHset), where SOHreal is the real SOH, g is the functional relationship among the SOC calibration coefficient, the current battery SOH setting value and the real SOH, KSOC is the SOC calibration coefficient, and SOHset is the current battery SOH setting value.

10. The method for dynamically calibrating the SOH of a power battery of an energy storage system based on big data according to claim 2, characterized in that: In step 4, the real SOH is calibrated as follows: The average value of the current measurement value and the last calibration value is the final SOH value.

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