Energy storage battery performance monitoring method and system based on big data model

Abstract

The invention discloses an energy storage battery performance monitoring method and system based on a big data model. The method comprises the following steps: collecting real-time data of various operating parameters of an energy storage battery; analyzing the change trend of the battery performance, and calculating the trend factor of the historical data of the energy storage battery to identify the long-term change trend of the battery performance; modeling the real-time data and the trend factor by using a big data model based on a neural network to generate a performance degradation prediction factor for quantitatively evaluating the degradation speed and state of the battery; and predicting the battery performance according to the performance degradation prediction factor, and generating a corresponding performance evaluation score to realize performance monitoring of the energy storage battery. According to the embodiment of the invention, various operation parameters can be collected in real time, the trend factors of historical data are combined, modeling and prediction are carried out by utilizing an advanced machine learning technology, and an innovative solution is provided for efficient management and maintenance of the energy storage battery.

Description

Technical Field

[0001] The present invention belongs to the field of energy storage technology, and in particular to a method and system for monitoring energy storage battery performance based on a big data model. Background Art

[0002] With the rapid development of renewable energy, energy storage batteries are increasingly used in power systems, smart grids, electric vehicles and other fields. The performance of energy storage batteries directly affects the efficiency, service life and safety of energy storage. Therefore, it is particularly important to effectively monitor and manage the performance of energy storage batteries. Traditional energy storage battery performance monitoring methods mainly rely on manual inspection and simple parameter recording, which makes it difficult to achieve real-time monitoring of battery status. These methods may cause data lag or omissions, and thus fail to reflect the true status of the battery in a timely manner. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for monitoring the performance of energy storage batteries based on a big data model to address the deficiencies in the prior art. The method and system can collect a variety of operating parameters in real time, combine trend factors of historical data, and use advanced machine learning technology for modeling and prediction, thereby providing an innovative solution for the efficient management and maintenance of energy storage batteries.

[0004] An embodiment of the present application provides a method for monitoring the performance of an energy storage battery based on a big data model, the method comprising: Collecting real-time data of various operating parameters of the energy storage battery, wherein the operating parameters include: voltage, current, temperature, internal resistance and charge and discharge status; Analyze the changing trend of battery performance and calculate the trend factor of the historical data of energy storage batteries to identify the long-term changing trend of battery performance; Modeling the real-time data and the trend factor using a neural network-based big data model to generate a performance degradation prediction factor for quantitatively evaluating the degradation speed and state of the battery; The battery performance is predicted based on the performance degradation prediction factor, and a corresponding performance evaluation score is generated to achieve performance monitoring of the energy storage battery.

[0005] Optionally, analyzing the changing trend of battery performance and calculating the trend factor of historical data of the energy storage battery includes: Determine the size of the analysis time window, and extract historical data of voltage, current and internal resistance covering the analysis time window from a database; Calculating the average power output by the battery within the analysis time window, and determining an internal resistance correction value for correcting the power loss caused by the internal resistance; The trend factor of the historical data of the energy storage battery is calculated according to the average power and the internal resistance correction value.

[0006] Optionally, the using of a big data model based on a neural network to model the real-time data and the trend factor to generate a performance degradation prediction factor includes: A big data model for generating performance degradation prediction factors is trained using an adaptive recursive neural network, wherein the model generates performance degradation prediction factors by learning weight parameters corresponding to a preset first nonlinear characteristic function and a second nonlinear characteristic function, and the first nonlinear characteristic function is used to comprehensively evaluate the overall impact of battery voltage, current, temperature and trend factors on battery performance, and the second nonlinear characteristic function is used to evaluate the impact of charge and discharge state and internal resistance on battery degradation.

[0007] Optionally, predicting the battery performance according to the performance degradation prediction factor and generating a corresponding performance evaluation score includes: Calculate the temperature average value and temperature correction factor in the real-time data within the acquisition time period; A performance evaluation score for predicting future battery performance is calculated based on the performance degradation prediction factor, the temperature average value, and the temperature correction factor.

[0008] Another embodiment of the present application provides a storage battery performance monitoring system based on a big data model, the system comprising: A collection module, used to collect real-time data of various operating parameters of the energy storage battery, wherein the operating parameters include: voltage, current, temperature, internal resistance and charge and discharge status; An analysis module is used to analyze the changing trend of battery performance and calculate the trend factor of the historical data of the energy storage battery to identify the long-term changing trend of battery performance; A generation module, for modeling the real-time data and the trend factor using a big data model based on a neural network to generate a performance degradation prediction factor for quantitatively evaluating the degradation speed and state of the battery; The monitoring module is used to predict the battery performance according to the performance degradation prediction factor and generate a corresponding performance evaluation score to realize the performance monitoring of the energy storage battery.

[0009] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0010] Yet another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods described above.

[0011] Compared with the prior art, the present invention provides a method for monitoring the performance of energy storage batteries based on a big data model, which collects real-time data of various operating parameters of the energy storage battery; analyzes the changing trend of the battery performance, and calculates the trend factor of the historical data of the energy storage battery to identify the long-term changing trend of the battery performance; uses a big data model based on a neural network to model the real-time data and the trend factor to generate a performance degradation prediction factor for quantitatively evaluating the degradation rate and state of the battery; predicts the battery performance according to the performance degradation prediction factor, and generates a corresponding performance evaluation score to achieve performance monitoring of the energy storage battery, so as to provide an innovative solution for efficient management and maintenance of energy storage batteries by collecting various operating parameters in real time and combining the trend factor of historical data with advanced machine learning technology for modeling and prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A hardware structure block diagram of a computer terminal for a method for monitoring energy storage battery performance based on a big data model provided by an embodiment of the present invention; Figure 2 A schematic diagram of a flow chart of a method for monitoring the performance of an energy storage battery based on a big data model provided by an embodiment of the present invention; Figure 3 A schematic structural diagram of an energy storage battery performance monitoring system based on a big data model provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0013] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0014] The embodiment of the present invention first provides a method for monitoring the performance of an energy storage battery based on a big data model. The method can be applied to electronic devices such as computer terminals, specifically ordinary computers, etc.

[0015] The following describes it in detail by taking running on a computer terminal as an example. Figure 1 The hardware structure block diagram of a computer terminal of a method for monitoring the performance of an energy storage battery based on a big data model provided by an embodiment of the present invention. Figure 1 As shown, the computer terminal may include one or more ( Figure 1Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0016] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / modules corresponding to the energy storage battery performance monitoring method based on the big data model in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implementing the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0017] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0018] See also Figure 2 , an embodiment of the present invention provides a method for monitoring the performance of an energy storage battery based on a big data model, which may include the following steps: S201, collecting real-time data of various operating parameters of the energy storage battery, wherein the operating parameters include: voltage, current, temperature, internal resistance, and charge and discharge state; In the energy storage battery performance monitoring method of the present invention, real-time data collection is one of the basic steps. This process involves the monitoring of multiple key operating parameters, mainly including the following aspects: Voltage (V): Voltage is an important indicator of the output capacity of energy storage batteries, usually measured in volts (V). The voltage level of a battery can reflect its remaining energy and state of charge. Monitoring voltage changes can help identify the battery's charge and discharge conditions, the risk of overcharge or overdischarge, and voltage imbalances between components. Therefore, voltage monitoring is critical to ensuring the safe and efficient operation of energy storage systems.

[0019] Current (I): Current is the rate of electricity flowing through the battery, measured in amperes (A). Changes in current directly affect the battery's charge and discharge process. Real-time current monitoring can effectively evaluate the battery's charging efficiency and discharge capacity, and can also reflect the battery's load conditions and dynamic changes in energy use. This is of great significance to improving battery utilization efficiency and extending battery life.

[0020] Temperature (T): Temperature is one of the environmental factors that affect battery performance and is usually expressed in degrees Celsius (°C). The operating temperature of energy storage batteries is closely related to their chemical reaction rate and internal resistance. Temperature monitoring can help identify battery thermal management issues, such as the adverse effects of overheating or low temperatures on battery performance. This is critical to ensuring battery safety and stability.

[0021] Internal resistance (R): Internal resistance is the resistance to the flow of current inside the battery, generally measured in milliohms (mΩ). The size of the internal resistance reflects the health and performance degradation of the battery. Monitoring changes in internal resistance helps evaluate the battery's charge and discharge efficiency and safety. High internal resistance may indicate battery aging or a risk of failure.

[0022] State of Charge (SOC): The state of charge (SOC) is the percentage of the battery's current charge relative to its rated capacity, usually expressed in %. SOC monitoring can provide real-time information on battery energy utilization, provide a basis for energy management and optimization control, and ensure that the energy storage system operates in the best working state.

[0023] In the present invention, the real-time data collection of various operating parameters of the energy storage battery can be implemented by the following specific schemes: Sensor deployment: Install a variety of sensors at key locations of energy storage batteries. These sensors may include: voltage sensor: used to monitor the output voltage of the battery in real time; current sensor: used to measure the charge and discharge current flowing through the battery; temperature sensor: monitor the operating temperature of the battery to ensure that it operates within a safe range; internal resistance sensor: used to regularly measure and evaluate the change in the internal resistance of the battery; SOC sensor: real-time calculation and feedback of the battery's charge and discharge status through the battery management system (BMS). Intelligent data acquisition modules such as embedded systems or PLCs (programmable logic controllers) can be used to aggregate and process the data collected by the above sensors. The real-time monitoring system can transmit data with the battery management system through the CAN bus or RS-485 communication protocol to achieve real-time data updates and remote monitoring. The collected real-time data is stored in a database for subsequent analysis. The data storage system needs to be efficient and secure to prevent data loss and damage. The collected raw data is preprocessed, including denoising, filtering and normalization, to improve the accuracy and reliability of the data.

[0024] Through the above implementation methods, real-time data of various operating parameters of energy storage batteries can be effectively collected, providing a solid data foundation for subsequent performance monitoring and analysis. This process not only improves the accuracy of energy storage battery monitoring, but also lays the foundation for battery status assessment and performance prediction, thereby providing support for safe and reliable energy management.

[0025] S202, analyzing the change trend of battery performance and calculating the trend factor of historical data of the energy storage battery to identify the long-term change trend of battery performance; In the performance monitoring of energy storage batteries, analyzing the changing trend of battery performance is a key step in evaluating the health status and life expectancy of batteries. By comparing real-time data with historical data, the performance fluctuations of batteries in different time periods and their influencing factors can be identified. This process involves monitoring and statistical analysis of multiple operating parameters such as voltage, current and internal resistance to generate a trend factor (TF). The trend factor not only reflects the changes in battery performance over time, but also reveals potential degradation patterns, helping users take maintenance measures in a timely manner to ensure the safe and efficient operation of the battery system.

[0026] The calculation and analysis of trend factors make the management of energy storage battery performance more scientific and systematic. By identifying the long-term trend of battery performance, managers can develop appropriate strategies based on data insights to optimize battery use and maintenance. In addition, trend factors also provide important basic data for future battery performance predictions, helping to determine whether the battery needs to be replaced or repaired, and ultimately improving the economy and reliability of the energy storage system.

[0027] Specifically, the size of the analysis time window may be determined, and historical data of voltage, current and internal resistance covering the analysis time window may be extracted from a database; The size of the analysis time window determines the scope and granularity of data analysis. Usually, it is necessary to set an appropriate time window according to the specific application scenario and battery usage cycle, such as day, week or month. A reasonable time window can ensure that the dynamic changes in battery performance are captured, while avoiding inaccurate analysis due to sparse data. In actual applications, a standard time window can be set, such as 7 days, and adjusted and optimized based on the actual collected data.

[0028] Extract the battery operation data within the specified time window from the database. These data include parameters such as voltage, current, internal resistance, etc., to ensure that they can truly reflect the working conditions of the battery. By extracting relevant data, subsequent trend calculations and performance analysis can be effectively performed, which provides the necessary data basis for identifying battery degradation. SQL queries can be used to extract relevant data within a specified time period from the historical data table and organize it into a structured data format for further analysis.

[0029] Calculating the average power output by the battery within the analysis time window, and determining an internal resistance correction value for correcting the power loss caused by the internal resistance; Average power is the average value of the power output by the battery in this time window, and the calculation formula is power = voltage × current. By calculating the average power, we can understand the energy output level of the battery in this time period. The calculation of average power helps to evaluate the working efficiency of the battery and can reflect the performance of the battery in different situations.

[0030] The internal resistance correction value is used to compensate for the power loss caused by internal resistance when calculating the total output power. Internal resistance causes energy loss, so correction is required when evaluating battery performance. By correcting the power loss caused by internal resistance, the actual performance of the battery can be more realistically reflected, improving the accuracy of the analysis.

[0031] The trend factor of the historical data of the energy storage battery is calculated according to the average power and the internal resistance correction value.

[0032] The trend factor (TF) is calculated by correcting the average power and reflects the operating efficiency and performance changes of the battery within a certain time window. The calculation of the trend factor helps to determine the long-term health status and performance degradation rate of the battery, and provides a historical basis for management and decision-making. For example, a trend factor can be: The trend factor is designed to quantify the comprehensive performance of the battery within a specific time window, so as to facilitate long-term monitoring and evaluation of the battery's operating efficiency and health status. By combining multiple core parameters such as voltage, current, and internal resistance, the trend factor provides a comprehensive quantitative indicator for changes in battery performance. Assuming that the TF calculation result is 0.85, it indicates that the battery's performance during this time period is relatively good.

[0033] To analyze the voltage of the ith data point in the time window, To analyze the current of the ith data point in the time window, To analyze the power output of the battery within the time window, is the average power, To analyze the resistance of the ith data point in the time window, is the internal resistance correction value used to correct the power loss caused by internal resistance, n is the number of data points in the analysis time window, is a correction coefficient related to the temperature of the ith data point in the analysis time window, which is used to compensate for the impact of temperature changes on internal resistance and power calculations to ensure the accuracy of data analysis. For example, a correction coefficient can be: This formula is designed to correct the internal resistance and battery performance through environmental influences under different temperature conditions. In actual applications, the internal resistance and performance of the battery tend to fluctuate with temperature changes. Therefore, this correction method can more accurately reflect the actual performance of the battery at a specific temperature. Through this formula, the ambient temperature factor can be included in the calculation of the trend factor, thereby improving the accuracy and reliability of battery performance monitoring.

[0034] in, Provides a baseline for the basic correction factor to characterize the basic performance of the battery under standard or reference conditions. It can be based on data or empirical values ​​provided by the battery manufacturer, for example, Can be set to 1.00, which generally represents base performance at reference temperature. It is the temperature sensitivity coefficient, which reflects the influence of temperature change on the correction factor. It can be determined based on historical data. It can quantify the influence of temperature change on battery performance. For example, if beta is 0.02, it means that the correction factor will increase by 0.02 for every 1°C increase. To analyze the temperature of the i-th data point in the time window, The first reference temperature is usually set to a fixed temperature value, which represents the standard test temperature of battery performance, usually the temperature in a laboratory environment. The commonly used first reference temperature can be set to 20°C.

[0035] S203, using a big data model based on a neural network to model the real-time data and the trend factor to generate a performance degradation prediction factor for quantitatively evaluating the degradation speed and state of the battery; In the method of the present invention, a big data model based on a neural network is used to model real-time data and trend factors, with the main purpose of generating performance degradation prediction factors. This process usually involves taking multiple real-time parameters of the battery (such as voltage, current, temperature, etc.) and calculated trend factors as inputs, and using an adaptive recurrent neural network (RNN) to train the model to learn the complex nonlinear relationship between these inputs and battery performance. In this way, the model can extract the potential pattern of battery performance degradation and generate a predictor that can be used to evaluate the battery degradation rate and state, thereby achieving a comprehensive assessment of the battery health status.

[0036] The significance of this step is that by introducing deep learning neural network technology, the process of energy storage battery performance monitoring is made more intelligent and accurate. Compared with traditional linear regression or simple statistical methods, neural networks can handle more complex input features and multi-dimensional data, thereby improving the accuracy of predicting battery performance degradation. This method can not only detect potential battery failures in a timely manner, but also provide a scientific basis for battery maintenance and management through the continuous update of performance evaluation factors, thereby improving the reliability and economy of energy storage systems.

[0037] Specifically, an adaptive recursive neural network can be used to train a big data model for generating performance degradation prediction factors, wherein the model generates performance degradation prediction factors by learning weight parameters corresponding to a preset first nonlinear characteristic function and a second nonlinear characteristic function, and the first nonlinear characteristic function is used to comprehensively evaluate the overall impact of battery voltage, current, temperature and trend factors on battery performance, and the second nonlinear characteristic function is used to evaluate the impact of charge and discharge state and internal resistance on battery degradation.

[0038] Exemplarily, a performance degradation prediction factor may be: , It is a performance degradation prediction factor that reflects the degree of battery degradation. The higher the value, the worse the battery performance. For example, if DF is 0.75, it means that the battery health status has deteriorated to a certain extent. is the first nonlinear characteristic function, is the second nonlinear characteristic function, , These two parameters are obtained through model training and reflect the relative importance of different input features to the degree of battery performance degradation. For example, beta_1 = 0.6, beta_2 = 0.4, indicating that voltage, current and temperature have a greater impact on battery degradation.

[0039] Exemplarily, a first nonlinear characteristic function may be: Wherein, a, b, and c are corresponding first adjustment parameters, a weighs the effect of the product of voltage and current on battery performance, for example, a = 0.1; b reflects the effect of temperature on battery performance, for example, b = 0.05; c is used to adjust the degree of influence of the trend factor on battery performance, for example, c = 0.2; is the average voltage value in the real-time data during the acquisition period, is the average current value in the real-time data during the acquisition period, It is the average temperature value in the real-time data during the acquisition period.

[0040] Exemplarily, a second nonlinear characteristic function may be: Wherein, d and e are the second adjustment parameters, d represents the positive influence of the charge and discharge state on the battery performance, for example, d = 0.3; e represents the negative influence of the internal resistance on the battery performance, for example, e = 0.4; is the average value of the charge and discharge status in the real-time data during the acquisition period, It is the average resistance value in the real-time data during the acquisition period.

[0041] Through the above design, the performance degradation prediction factor is integrated into the big data model, which can effectively utilize the ability of machine learning, allowing the model to automatically learn and optimize the weights of each parameter, and improve the accuracy and reliability of battery performance monitoring. This method combines the empirical knowledge of traditional battery monitoring with modern big data technology, and can better adapt to the complexity and variability of battery performance.

[0042] S204, predicting the battery performance according to the performance degradation prediction factor, and generating a corresponding performance evaluation score to achieve performance monitoring of the energy storage battery.

[0043] In the energy storage battery performance monitoring method, the core of the method is to predict the performance of the battery using the performance degradation prediction factor (DF). By analyzing the real-time data and trend factors, the generated performance evaluation score will reflect the health of the battery in the future. This process involves a comprehensive analysis of multiple operating parameters of the battery, including voltage, current, temperature, etc., to ensure the accuracy and real-time nature of the prediction.

[0044] The implementation of this step makes the performance monitoring of energy storage batteries more scientific and accurate. By generating corresponding performance evaluation scores, users can timely understand the health status of the battery, foresee possible failures or performance degradation, and take preventive measures. This not only helps to extend the service life of the battery, but also optimizes the battery operation strategy, improves the overall energy efficiency and reliability of the battery system, and reduces maintenance costs and risks of downstream applications.

[0045] Specifically, the temperature average value and the temperature correction factor in the real-time data within the acquisition time period can be calculated; In this step, the battery temperature data collected within a specific period of time must first be averaged to obtain the average temperature value. Subsequently, the temperature correction factor is calculated using the average value and other related parameters to correct the battery performance changes caused by temperature fluctuations. This correction factor will play a key role in subsequent performance evaluation.

[0046] The impact of temperature on battery performance is significant, especially under different environmental conditions of charge and discharge. By calculating the temperature average and temperature correction factor, it is possible to ensure that the temperature factor is taken into account during performance evaluation, thereby improving the accuracy and reliability of the prediction results. This provides a scientific basis for the battery management system and helps optimize the battery's use and maintenance strategy.

[0047] Exemplarily, a temperature correction factor may be: This formula provides a quantitative description of the effect of temperature on battery performance, which can be used to effectively correct the temperature factor in performance evaluation.

[0048] in, is the second reference temperature, which is used as the reference point for temperature correction, for example, 25°C. is the standard temperature difference, which affects the calculation accuracy of the temperature correction factor, for example, 10°C; k is the temperature influence coefficient, which adjusts the impact of temperature changes on battery performance, for example, k = 0.1.

[0049] A performance evaluation score for predicting future battery performance is calculated based on the performance degradation prediction factor, the temperature average value, and the temperature correction factor.

[0050] In this step, the performance degradation prediction factor and temperature correction factor calculated previously are combined to generate an evaluation score for future battery performance. This score reflects the expected performance of the battery under specific temperature conditions by subtracting the effect of the temperature correction factor from the performance degradation prediction factor.

[0051] The calculated performance evaluation score is an important indicator for battery performance monitoring and can provide users with an intuitive basis for judgment. By monitoring this score, changes in battery performance can be detected in a timely manner, and then the operation strategy can be adjusted and necessary maintenance measures can be taken. This process significantly improves the scientificity and effectiveness of battery management.

[0052] For example, a performance evaluation score may be: This formula is used to comprehensively consider the performance degradation prediction factor and the impact of temperature on battery performance. By introducing correction factors, the prediction results are closer to the actual situation.

[0053] It can be seen that real-time data of various operating parameters of the energy storage battery are collected; the changing trend of battery performance is analyzed, and the trend factor of the historical data of the energy storage battery is calculated to identify the long-term changing trend of the battery performance; the real-time data and the trend factor are modeled using a big data model based on a neural network to generate a performance degradation prediction factor for quantitatively evaluating the degradation rate and state of the battery; the battery performance is predicted based on the performance degradation prediction factor to generate a corresponding performance evaluation score to achieve performance monitoring of the energy storage battery, so that it can collect a variety of operating parameters in real time, and combine the trend factors of historical data, and use advanced machine learning technology for modeling and prediction, so as to provide an innovative solution for the efficient management and maintenance of energy storage batteries.

[0054] Another embodiment of the present invention provides a storage battery performance monitoring system based on a big data model, see Figure 3 , the system may include: The acquisition module 301 is used to collect real-time data of various operating parameters of the energy storage battery, wherein the operating parameters include: voltage, current, temperature, internal resistance and charge and discharge state; An analysis module 302 is used to analyze the changing trend of battery performance and calculate the trend factor of the historical data of the energy storage battery to identify the long-term changing trend of the battery performance; A generation module 303 is used to model the real-time data and the trend factor using a big data model based on a neural network to generate a performance degradation prediction factor for quantitatively evaluating the degradation speed and state of the battery; The monitoring module 304 is used to predict the battery performance according to the performance degradation prediction factor and generate a corresponding performance evaluation score to achieve performance monitoring of the energy storage battery.

[0055] It can be seen that real-time data of various operating parameters of the energy storage battery are collected; the changing trend of battery performance is analyzed, and the trend factor of the historical data of the energy storage battery is calculated to identify the long-term changing trend of the battery performance; the real-time data and the trend factor are modeled using a big data model based on a neural network to generate a performance degradation prediction factor for quantitatively evaluating the degradation rate and state of the battery; the battery performance is predicted based on the performance degradation prediction factor to generate a corresponding performance evaluation score to achieve performance monitoring of the energy storage battery, so that it can collect a variety of operating parameters in real time, and combine the trend factors of historical data, and use advanced machine learning technology for modeling and prediction, so as to provide an innovative solution for the efficient management and maintenance of energy storage batteries.

[0056] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0057] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps: S201, collecting real-time data of various operating parameters of the energy storage battery, wherein the operating parameters include: voltage, current, temperature, internal resistance, and charge and discharge state; S202, analyzing the change trend of battery performance and calculating the trend factor of historical data of the energy storage battery to identify the long-term change trend of battery performance; S203, using a big data model based on a neural network to model the real-time data and the trend factor to generate a performance degradation prediction factor for quantitatively evaluating the degradation speed and state of the battery; S204, predicting the battery performance according to the performance degradation prediction factor, and generating a corresponding performance evaluation score to achieve performance monitoring of the energy storage battery.

[0058] It can be seen that real-time data of various operating parameters of the energy storage battery are collected; the changing trend of battery performance is analyzed, and the trend factor of the historical data of the energy storage battery is calculated to identify the long-term changing trend of the battery performance; the real-time data and the trend factor are modeled using a big data model based on a neural network to generate a performance degradation prediction factor for quantitatively evaluating the degradation rate and state of the battery; the battery performance is predicted based on the performance degradation prediction factor to generate a corresponding performance evaluation score to achieve performance monitoring of the energy storage battery, so that it can collect a variety of operating parameters in real time, and combine the trend factors of historical data, and use advanced machine learning technology for modeling and prediction, so as to provide an innovative solution for the efficient management and maintenance of energy storage batteries.

[0059] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0060] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0061] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program: S201, collecting real-time data of various operating parameters of the energy storage battery, wherein the operating parameters include: voltage, current, temperature, internal resistance, and charge and discharge state; S202, analyzing the change trend of battery performance and calculating the trend factor of historical data of the energy storage battery to identify the long-term change trend of battery performance; S203, using a big data model based on a neural network to model the real-time data and the trend factor to generate a performance degradation prediction factor for quantitatively evaluating the degradation speed and state of the battery; S204, predicting the battery performance according to the performance degradation prediction factor, and generating a corresponding performance evaluation score to achieve performance monitoring of the energy storage battery.

[0062] It can be seen that real-time data of various operating parameters of the energy storage battery are collected; the changing trend of battery performance is analyzed, and the trend factor of the historical data of the energy storage battery is calculated to identify the long-term changing trend of the battery performance; the real-time data and the trend factor are modeled using a big data model based on a neural network to generate a performance degradation prediction factor for quantitatively evaluating the degradation rate and state of the battery; the battery performance is predicted based on the performance degradation prediction factor to generate a corresponding performance evaluation score to achieve performance monitoring of the energy storage battery, so that it can collect a variety of operating parameters in real time, and combine the trend factors of historical data, and use advanced machine learning technology for modeling and prediction, so as to provide an innovative solution for the efficient management and maintenance of energy storage batteries.

[0063] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. A method for monitoring energy storage battery performance based on a big data model, characterized in that: The method comprises: Collecting real-time data of various operating parameters of the energy storage battery, wherein the operating parameters include: voltage, current, temperature, internal resistance and charge and discharge status; Analyze the changing trend of battery performance and calculate the trend factor of the historical data of energy storage batteries to identify the long-term changing trend of battery performance; Modeling the real-time data and the trend factor using a neural network-based big data model to generate a performance degradation prediction factor for quantitatively evaluating the degradation speed and state of the battery; The battery performance is predicted based on the performance degradation prediction factor, and a corresponding performance evaluation score is generated to achieve performance monitoring of the energy storage battery.

2. The method according to claim 1, characterized in that The analysis of the changing trend of battery performance and calculation of the trend factor of the historical data of the energy storage battery include: Determine the size of the analysis time window, and extract historical data of voltage, current and internal resistance covering the analysis time window from a database; Calculating the average power output by the battery within the analysis time window, and determining an internal resistance correction value for correcting the power loss caused by the internal resistance; The trend factor of the historical data of the energy storage battery is calculated according to the average power and the internal resistance correction value.

3. The method according to claim 2, characterized in that The method of using a big data model based on a neural network to model the real-time data and the trend factor to generate a performance degradation prediction factor includes: A big data model for generating performance degradation prediction factors is trained using an adaptive recursive neural network, wherein the model generates performance degradation prediction factors by learning weight parameters corresponding to a preset first nonlinear characteristic function and a second nonlinear characteristic function, and the first nonlinear characteristic function is used to comprehensively evaluate the overall impact of battery voltage, current, temperature and trend factors on battery performance, and the second nonlinear characteristic function is used to evaluate the impact of charge and discharge state and internal resistance on battery degradation.

4. The method according to claim 3, characterized in that The predicting the battery performance according to the performance degradation prediction factor and generating a corresponding performance evaluation score includes: Calculate the temperature average value and temperature correction factor in the real-time data within the acquisition time period; A performance evaluation score for predicting future battery performance is calculated based on the performance degradation prediction factor, the temperature average value, and the temperature correction factor.

5. A storage battery performance monitoring system based on a big data model, characterized in that: The system comprises: A collection module, used to collect real-time data of various operating parameters of the energy storage battery, wherein the operating parameters include: voltage, current, temperature, internal resistance and charge and discharge status; An analysis module is used to analyze the changing trend of battery performance and calculate the trend factor of the historical data of the energy storage battery to identify the long-term changing trend of battery performance; A generation module, for modeling the real-time data and the trend factor using a big data model based on a neural network to generate a performance degradation prediction factor for quantitatively evaluating the degradation speed and state of the battery; The monitoring module is used to predict the battery performance according to the performance degradation prediction factor and generate a corresponding performance evaluation score to realize the performance monitoring of the energy storage battery.

6. The system according to claim 5, characterized in that The analysis module is specifically used for: Determine the size of the analysis time window, and extract historical data of voltage, current and internal resistance covering the analysis time window from a database; Calculating the average power output by the battery within the analysis time window, and determining an internal resistance correction value for correcting the power loss caused by the internal resistance; The trend factor of the historical data of the energy storage battery is calculated according to the average power and the internal resistance correction value.

7. The system according to claim 6, characterized in that The generation module is specifically used for: A big data model for generating performance degradation prediction factors is trained using an adaptive recursive neural network, wherein the model generates performance degradation prediction factors by learning weight parameters corresponding to a preset first nonlinear characteristic function and a second nonlinear characteristic function, and the first nonlinear characteristic function is used to comprehensively evaluate the overall impact of battery voltage, current, temperature and trend factors on battery performance, and the second nonlinear characteristic function is used to evaluate the impact of charge and discharge state and internal resistance on battery degradation.

8. The system according to claim 7, characterized in that The monitoring module is specifically used for: Calculate the temperature average value and temperature correction factor in the real-time data within the acquisition time period; A performance evaluation score for predicting future battery performance is calculated based on the performance degradation prediction factor, the temperature average value, and the temperature correction factor.

9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 4 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 4.

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