A battery status assessment system for energy storage power stations

Abstract

The present invention relates to a battery state assessment system for an energy storage power station, comprising a characteristic acquisition unit, a state prediction unit, a health state prediction unit and a communication unit. The characteristic acquisition unit receives and processes passive and active data, and performs variable identification to obtain internal variables of the battery cell. Based on these variables, the state prediction unit dynamically adjusts the prediction model to update the internal state model of the battery. The health state prediction unit parses the internal state model to determine the health state (SOH) and charge level (SOC) of the battery. The communication unit transmits the prediction results to the user or related system in real time to achieve accurate monitoring and management of the battery.

Description

Technical Field

[0001] The invention relates to a battery status evaluation system, in particular to a battery status evaluation system for an energy storage power station. Background Art

[0002] With the continuous growth of global energy demand and the gradual enhancement of environmental protection awareness, the importance of energy storage technology in the energy field has become increasingly prominent. Energy storage systems are widely used in modern energy systems. Their main functions include balancing power supply and demand, improving grid stability, and coping with the volatility and uncertainty of renewable energy generation. They have become an indispensable part of the future development of smart grids and renewable energy. In particular, in the process of renewable energy such as solar energy and wind energy gradually replacing traditional fossil energy, energy storage power stations play a key role. By storing excess energy and releasing it during peak demand, they significantly improve the stability and optimization of energy supply.

[0003] Among the many existing energy storage technologies, battery energy storage technology has become one of the most widely used forms of energy storage due to its high energy conversion efficiency, rapid response capability, and easy expansion. The core function of the battery energy storage system is to effectively smooth the power load curve and reduce the peak load pressure of the power grid by storing electricity when the power demand is low and releasing electricity when the demand is peak. This not only helps to improve the operating efficiency of the power grid, but also can further improve the quality of electricity by providing functions such as frequency regulation and backup power supply. Therefore, the role of battery energy storage systems in modern energy systems is becoming increasingly important.

[0004] However, the long-term reliable operation of battery energy storage systems depends largely on the performance and health of the battery pack. As the number of battery charge and discharge cycles increases, the battery performance will inevitably decline, such as a decrease in capacity and an increase in internal resistance. These degradation phenomena will directly affect the operating efficiency of the battery, shorten its service life, and in severe cases cause safety issues such as overheating, leakage or explosion. Therefore, how to accurately evaluate the battery health status and then predict the remaining life of the battery has become a key issue to ensure the safe, stable and long-term operation of the battery energy storage system.

[0005] Traditional battery health status assessment methods mainly rely on external measurable battery parameters such as voltage, temperature, current, etc. Although these parameters can reflect the operating status of the battery to a certain extent, due to the high complexity and nonlinear characteristics of the chemical reactions inside the battery, it is difficult to fully and accurately reflect the true health status of the battery only through external parameters. For example, the battery voltage fluctuates due to the combined effects of multiple factors such as temperature changes and battery aging. Single voltage monitoring cannot reliably indicate the degree of degradation or potential failure risks inside the battery.

[0006] In recent years, with the development of electrochemical technology and materials science, researchers have gradually developed health assessment methods based on the internal state of batteries. These methods usually establish an equivalent circuit model of the battery and combine experimental data such as electrochemical impedance spectroscopy (EIS) to derive the internal physical and chemical parameters of the battery, such as electrolyte resistance, double layer capacitance, diffusion impedance, etc. These internal parameters can more accurately reflect the chemical reaction process inside the battery, thereby providing a more accurate assessment of the battery's health status. For example, by monitoring changes in electrolyte resistance, the degree of electrolyte degradation can be evaluated; by analyzing diffusion impedance, the degradation of electrode materials can be evaluated, etc.

[0007] Although the evaluation methods based on internal parameters have high accuracy and reliability in theory, they still face many challenges in practical applications. First, these methods usually rely on complex and expensive experimental instruments, such as electrochemical impedance spectrometers, scanning electron microscopes, etc., which makes them difficult to promote on a large scale in practical applications. In addition, these methods often require offline testing of batteries in laboratory environments, making it difficult to monitor the operating status of batteries in real time and continuously. The limitations of this offline test lead to a lag in battery health status monitoring, and it is impossible to detect potential degradation trends or hidden dangers in a timely manner.

[0008] As the scale of energy storage power stations continues to expand, the complexity of battery systems is also increasing. In large energy storage power stations, there are often thousands of battery cells, and there are significant performance differences and inconsistencies between these cells. In this case, the traditional single battery evaluation method is difficult to effectively cope with the complexity of the system level and cannot comprehensively and accurately evaluate the health status of the entire battery pack. This system-level complexity requires the development of a battery status evaluation method that can comprehensively consider multiple influencing factors and is suitable for large-scale energy storage systems, so as to achieve real-time monitoring and management of the health status of the entire battery pack.

[0009] Therefore, in view of the challenges in the above background technology, it is urgent to develop a new type of battery status assessment system. The system should be able to obtain multi-dimensional data of the battery in real time, comprehensively analyze the external and internal status of the battery, accurately assess the health of the battery, and predict its remaining life. This system can not only improve the safety and reliability of the battery energy storage system, but also provide a scientific basis for the optimized operation of the energy storage system, thereby maximizing economic benefits while ensuring energy supply. Summary of the invention

[0010] In order to solve the above problems in the prior art, the present invention proposes a battery status assessment system for an energy storage power station, wherein the battery status assessment system comprises a characteristic acquisition unit, a status prediction unit, a health status prediction unit and a communication unit;

[0011] The characteristic acquisition unit receives input data, the input data including passive data collected from passive measurements of the battery and active data collected from active measurements of the battery in response to a stimulus signal applied to the battery;

[0012] The characteristic acquisition unit performs variable identification based on shape characteristics based on input data related to the circuit equivalent model to acquire shape variables; performs variable identification based on improvement based on input data related to the circuit equivalent model to acquire improved variables;

[0013] The state prediction unit combines the shape variables and the improved variables through a determination algorithm to output updated internal variables of the battery cell, wherein the updated internal variables of the battery cell at least include extended impedance, electrolyte impedance and current transfer impedance;

[0014] The state prediction unit includes a prediction model, and the state prediction unit dynamically adjusts the parameters of the prediction model according to the battery operation state and environmental changes, and the state prediction unit updates the internal state model of the battery according to the adjusted prediction model and based on the updated internal variables of the battery cell;

[0015] The health state prediction unit is used to parse the internal state model to determine the current health state of the battery, including the health and charge level of the battery;

[0016] The communication unit is used to send one or more health prediction values ​​and charge level prediction values ​​to a user, a related computing system, or a combination of the two.

[0017] The passive data includes the open circuit voltage of the battery, the internal resistance in a static state and the temperature of the battery;

[0018] The active data is measurement data collected from the battery response after the characteristic acquisition unit applies an external stimulation signal, wherein the external stimulation signal includes a current pulse signal and an AC frequency sweep signal.

[0019] The characteristic acquisition unit performs variable identification related to the circuit equivalent model based on the acquired passive data and active data, and the variable identification includes:

[0020] Obtaining shape variables describing the health status of the battery through passive data, wherein the shape variables include the slope of the voltage recovery curve and the characteristic curve of the internal resistance changing with temperature;

[0021] Improved variables are extracted by combining active data with an electrochemical model or an equivalent circuit model, and the improved variables include diffusion impedance and charge transfer impedance.

[0022] The electrolyte impedance in the updated battery cell internal variable is measured by frequency domain analysis, and the determination algorithm calculates the impedance characteristics of the electrolyte by using the relationship between the current response and the frequency of the applied AC signal;

[0023] The current transfer impedance in the updated internal variables of the battery cell is obtained by fitting the volt-ampere characteristic curve;

[0024] The diffusion impedance in the updated internal variables of the battery cell is obtained by measuring the electrochemical impedance spectroscopy in the low-frequency range.

[0025] The prediction model is a dynamically adjustable model, and its mathematical expression is as follows:

[0026]

[0027] in,

[0028] is the terminal voltage of the battery at time t;

[0029] is the open circuit voltage;

[0030] is the instantaneous current;

[0031] is the series resistance;

[0032] represents the electrolyte impedance at frequency f;

[0033] is the current transfer impedance;

[0034] is the diffusion impedance.

[0035] The parameters of the dynamic adjustment of the prediction model are specifically:

[0036] The electrolyte impedance parameters are adjusted by impedance spectrum data; the current transfer impedance parameters are adjusted by voltammetric characteristic curve fitting; and the diffusion impedance parameters are adjusted by low-frequency impedance measurement data.

[0037] The dynamic adjustment of the parameters of the prediction model further includes temperature compensation, and the temperature compensation model is:

[0038]

[0039] in:

[0040] is the electrolyte impedance at temperature T;

[0041] The base temperature The electrolyte impedance under

[0042] is the activation energy;

[0043] k is the Boltzmann constant;

[0044] T is the current temperature, is the reference temperature.

[0045] The health status prediction unit analyzes the internal status model specifically including:

[0046] The degree of electrolyte degradation can be determined by the change in electrolyte impedance. The degree of electrode surface reaction degradation can be evaluated by the change in current transfer impedance. The diffusion process inside the battery can be analyzed by the change in diffusion impedance to predict the long-term decline trend of battery performance.

[0047] The health state prediction unit comprehensively evaluates the health state of the battery, and the health state evaluation formula of the battery is:

[0048]

[0049] in The health status based on the electrolyte impedance, Based on the health status of the current transfer impedance, Based on the health status of diffusion impedance, is the corresponding weight factor.

[0050] The health status prediction unit comprehensively evaluates the charging level of the battery, specifically including estimating the charging level by combining the open circuit voltage method and the coulomb counting method; dynamically compensating through the impedance parameters in the internal state model; and correcting the charging level in real time by Kalman filtering or adaptive filtering methods.

[0051] The battery status evaluation system of the present invention has the following beneficial effects:

[0052] By combining passive data and active data for variable identification and dynamically adjusting the prediction model parameters, the internal state of the battery can be more accurately reflected, improving the accuracy of battery health status assessment.

[0053] The system can obtain and transmit the battery's state of health (SOH) and charge level (SOC) in real time, allowing users to monitor the battery status in a timely manner, make maintenance and adjustment decisions quickly, and ensure the safe and stable operation of the energy storage power station.

[0054] By adaptively adjusting the prediction model parameters, the system can dynamically optimize the evaluation results according to the battery's operating status and environmental changes, adapt to various operating conditions, extend battery life, and improve the overall efficiency of the system.

[0055] Provides real-time, accurate health and charge level data to provide decision support for users and computing systems, optimize battery management and maintenance strategies, and reduce operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present application, but do not constitute an improper limitation of the present invention. In the drawings:

[0057] Figure 1 It is an overall architecture diagram of the battery status assessment system for energy storage power stations of the present invention. DETAILED DESCRIPTION

[0058] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments, wherein the illustrative embodiments and descriptions are only used to explain the present invention but are not intended to limit the present invention.

[0059] Example 1: Overall framework of battery status assessment system

[0060] like Figure 1 As shown, this embodiment provides a battery status assessment system for an energy storage power station. The system includes a characteristic acquisition unit 101, a status prediction unit 102, a health status prediction unit 103 and a communication unit 104, which are interconnected and communicated through a system bus to form a complete battery status assessment system.

[0061] The characteristic acquisition unit 101 is used to receive and process input data collected from the battery, and the input data includes passive data and active data. Passive data refers to measurement data obtained from the battery without applying external stimulation; while active data is response data collected after applying a specific stimulation signal to the battery. The characteristic acquisition unit further performs variable identification related to the circuit equivalent model based on these input data, and extracts shape variables and improved variables.

[0062] The state prediction unit 102 uses the shape variables and improved variables generated by the characteristic acquisition unit as inputs through a determination algorithm, calculates and outputs updated internal variables of the battery cell. The updated internal variables of the battery cell include but are not limited to diffusion impedance, electrolyte impedance and current transfer impedance. The state prediction unit contains a dynamically adjustable prediction model, which can adjust its parameters and calculation methods in real time according to changes in the operating state and environmental conditions of the battery. Through the updated internal variables of the battery cell, the prediction model can output an internal state model that reflects the true internal state of the battery.

[0063] The health state prediction unit 103 is used to parse the internal state model output from the state prediction unit. Through the analysis, the unit can determine the current health state of the battery, including the battery's state of health (SOH) and charge level (SOC). This information is crucial for evaluating the remaining life and performance of the battery.

[0064] The communication unit 104 is responsible for transmitting the health prediction value and charge level prediction value generated by the health prediction unit to the user or the relevant computing system, or a combination of the two. Through these data, the user can monitor the health status of the battery in real time, and make maintenance decisions or adjust the operating parameters of the battery accordingly to ensure the long-term stable operation of the energy storage power station.

[0065] Embodiment 2 Characteristic acquisition unit

[0066] The characteristic acquisition unit 101 is a key component for receiving and processing input data collected from the battery, and the input data includes passive data and active data.

[0067] 1. Acquisition and processing of passive data

[0068] Passive data refers to the measurement data obtained from the battery in its natural state without the action of external stimulation signals. This type of data usually includes but is not limited to the battery's open circuit voltage (OCV), internal resistance in a static state, battery temperature, etc.

[0069] The passive data is collected through the data acquisition module. This module consists of a high-precision voltage measurement circuit, a temperature sensor, and an internal resistance measurement device. The voltage measurement circuit is used to measure the open circuit voltage of the battery, the temperature sensor is used to obtain the temperature information of the battery surface, and the internal resistance measurement device is used to measure the internal resistance of the battery when no current passes through. The collected passive data is affected by environmental noise and measurement errors. The built-in filter of the feature acquisition unit is used to remove noise signals, such as a low-pass filter for eliminating high-frequency interference. The preprocessing module also includes a correction circuit for compensating for measurement deviations caused by temperature drift or sensor errors. After the passive data is filtered and preprocessed, the feature acquisition unit performs preliminary feature extraction operations on these data.

[0070] 2. Acquisition and processing of active data

[0071] Active data is the measurement data collected from the battery response after the characteristic acquisition unit applies an external stimulus signal (such as a current pulse, an AC signal). This type of data can reveal the dynamic characteristics of the battery, such as charge transfer impedance, diffusion impedance, etc.

[0072] The characteristic acquisition unit includes a signal generation module, which is used to generate a stimulus signal applied to the battery. The type of stimulus signal can be a constant current pulse signal, an AC sweep signal, etc. For example, to measure the electrochemical impedance spectroscopy (EIS) of the battery, the module generates an AC signal ranging from a few millihertz to a few kilohertz.

[0073] When a stimulus signal is applied to the battery, the battery will produce a corresponding voltage or current response. The high-precision acquisition circuit in the characteristic acquisition unit is used to capture these response signals. The acquisition accuracy should be high enough to ensure that the tiny changes inside the battery can be accurately captured.

[0074] For the collected active data, the characteristic acquisition unit first performs frequency domain analysis such as Fourier transform or wavelet transform to extract frequency response characteristics. These characteristics include electrochemical impedance, phase angle, etc., which are further used for variable identification.

[0075] The characteristic acquisition unit performs variable identification related to the circuit equivalent model based on the acquired passive data and active data. The variable identification process is as follows:

[0076] Shape variables are characteristic parameters obtained by analyzing the trends and patterns of passive data. These parameters include the slope of the voltage recovery curve, the characteristic curve of the internal resistance changing with temperature, etc. By statistically analyzing these variables, shape characteristic parameters that describe the health status of the battery can be obtained.

[0077] Improved variables are parameters extracted by deeper analysis of active data combined with electrochemical models or equivalent circuit models. These variables include diffusion impedance, charge transfer impedance, etc. The calculation of improved variables usually requires solving differential equations or performing nonlinear least squares fitting.

[0078] The characteristic acquisition unit inputs the extracted shape variables and improved variables into the circuit equivalent model, and fits the model parameters that best represent the current state of the battery through optimization methods such as least squares method and genetic algorithm. These parameters will be used for subsequent state prediction and health assessment.

[0079] Through the above detailed steps, the characteristic acquisition unit 101 can accurately acquire and process the passive and active data of the battery, extract representative shape variables and improvement variables, and lay the foundation for subsequent battery status evaluation and prediction.

[0080] Embodiment 3 State prediction unit

[0081] In the battery state evaluation system, the state prediction unit 102 is responsible for taking the shape variables and improvement variables generated by the characteristic acquisition unit as input, calculating and outputting updated internal variables of the battery cell through the determination algorithm. These internal variables include but are not limited to electrolyte impedance (Z ele )、Current transfer impedance(R ct ) and diffusion impedance (Z diff ). The state prediction unit also includes a dynamically adjustable prediction model, which can adjust its parameters in real time according to the changes in the battery's operating state and environmental conditions, thereby generating an internal state model that reflects the true internal state of the battery.

[0082] 1. Determination of the implementation method of the algorithm

[0083] The decision algorithm is the core part of the state prediction unit, which is used to process the input shape variables and improvement variables and calculate the updated internal variables of the battery cell.

[0084] The state prediction unit receives shape variables (such as the slope of the voltage recovery curve, internal resistance change characteristics, etc.) and improved variables (such as electrochemical impedance, diffusion impedance, etc.) generated by the characteristic acquisition unit. The input data is normalized, noise is removed, and outliers are detected to ensure the quality of the input data.

[0085] The battery equivalent circuit model is fitted through an optimization algorithm (such as the least squares method or genetic algorithm). Specifically, the judgment algorithm substitutes the shape variables and the improved variables into the model, and adjusts the model parameters so that the calculated internal variables of the battery cell can match the actual measured battery response data.

[0086] Further calculate the updated internal variables of the battery cell:

[0087] Electrolyte impedance (Z ele ): measured by frequency domain analysis. The decision algorithm uses the relationship between the current response and the frequency of the applied AC signal to calculate the impedance characteristics of the electrolyte.

[0088] Current transfer impedance (R ct ): Obtained by fitting the voltammetric characteristic curve and calculated based on the Butler-Volmer equation, it is used to describe the change in electrode reaction rate.

[0089] Diffusion impedance (Z diff ): Diffusion impedance mainly describes the diffusion process of charge or ions in the electrode material in the battery. It is usually obtained by measuring electrochemical impedance spectroscopy (EIS) in the low-frequency range. The size of diffusion impedance directly reflects the diffusion rate of the battery and the difficulty of charge conduction, and is one of the important indicators of the internal health of the battery.

[0090] The prediction model included in the state prediction unit is a dynamically adjustable model, and its mathematical expression is as follows:

[0091]

[0092] Model structure description:

[0093] is the terminal voltage of the battery at time t, which is the main output of the model and is used to reflect the real-time status of the battery.

[0094] It is the open circuit voltage, which indicates the voltage value of the battery under no-load conditions.

[0095] It is the instantaneous current, describing the current flow in the battery at present.

[0096] is the series resistance and represents the ohmic losses inside the battery.

[0097] It represents the electrolyte impedance at frequency f.

[0098] It is the current transfer impedance, which reflects the impedance of charge transfer from the electrolyte to the electrode.

[0099] It is the diffusion impedance, which describes the diffusion process of charges or ions in the electrode material.

[0100] In order to ensure the accuracy and adaptability of the prediction model, the state prediction unit implements a dynamic parameter adjustment mechanism. The specific steps are as follows:

[0101] The state prediction unit continuously monitors the battery's operating voltage, current, temperature and other data through a sensor network to obtain real-time operating status information.

[0102] Adaptive adjustment of key parameters, including

[0103] Electrolyte impedance Adjustment: Based on the real-time measured impedance spectrum data, the least squares method is used to adjust the electrolyte impedance parameters in the model to ensure that the model can reflect the current conductivity of the electrolyte.

[0104] Current transfer impedance Adjustment: Through the nonlinear least squares fitting of the real-time voltammetric characteristic curve, the current transfer impedance is dynamically adjusted to reflect the changes in the electrode surface reaction.

[0105] Diffusion impedance Adjustment: By analyzing the characteristics of the diffusion process and combining the impedance measurement data in the low-frequency range, the diffusion impedance parameters are adjusted so that they can accurately reflect the diffusion process inside the battery.

[0106] In addition, environmental conditions (such as temperature and humidity) have a significant impact on battery performance, especially when the temperature is high or low, the battery's internal resistance, open circuit voltage and other characteristics will change significantly. In order to adapt the prediction model to different environmental conditions, the dynamically adjustable model introduces an environmental factor compensation mechanism. The electrolyte impedance and current transfer impedance parameters in the model are adjusted to adapt to the impact of temperature changes on battery performance.

[0107] The influence of temperature on electrolyte impedance and current transfer impedance is particularly significant. The temperature compensation model is corrected based on the Arrhenius Equation:

[0108]

[0109] in:

[0110] is the electrolyte impedance at temperature T;

[0111] The base temperature The electrolyte impedance under

[0112] is the activation energy;

[0113] k is the Boltzmann constant;

[0114] T is the current temperature, is the reference temperature.

[0115] By integrating the updated internal variables of the battery cell calculated by the judgment algorithm, the prediction model can output an internal state model that reflects the real internal state of the battery. This model can not only predict the terminal voltage of the battery, but also provide key indicators of battery health status, performance degradation trend and remaining life. The output of the internal state model provides reliable data support for battery health management and performance optimization.

[0116] In summary, the state prediction unit accurately calculates and updates the internal variables of the battery cell through the combination of the judgment algorithm and the dynamically adjustable prediction model, and generates an internal state model that reflects the real internal state of the battery. The design and implementation of this unit ensures real-time monitoring and prediction of the battery state, providing a strong guarantee for the safe and stable operation of the battery.

[0117] Embodiment 4 Health status prediction unit

[0118] The health state prediction unit 103 determines the current health state of the battery (including SOH and SOC) by parsing the internal state model output from the state prediction unit 102. This parsing process relies on in-depth analysis of the key cell internal variables in the prediction model to evaluate the overall health and charging state of the battery.

[0119] The internal state model is calculated by the state prediction unit based on the internal variables of the battery cell, which reflects the actual physical and chemical state inside the battery. The model includes the following key parameters:

[0120] Electrolyte impedance: reflects the conductivity and internal resistance of the electrolyte.

[0121] Current transfer impedance: reflects the resistance to charge transfer on the electrode surface and is directly related to the electrode reaction rate.

[0122] Diffusion impedance: reflects the diffusion process of charge or ions in the electrode material and is an indicator of the internal health of the battery.

[0123] The health status prediction unit analyzes these internal variables of the battery cells to determine the SOH and SOC of the battery. The specific analysis process is as follows:

[0124] Analysis of electrolyte impedance

[0125] Deterioration identification: The health status prediction unit analyzes Z ele The degree of electrolyte degradation can be judged by the change trend of the electrolyte. Generally, as the battery is used for a longer time, the impedance of the electrolyte will gradually increase, indicating that the conductivity of the electrolyte is reduced, which is caused by the decomposition of the electrolyte or the change of the electrolyte concentration.

[0126] SOH Impact: Z ele The increase of Z is usually accompanied by the attenuation of battery capacity. ele The values ​​are compared with the initial baseline values ​​to calculate the degree of electrolyte degradation of the battery and incorporate it into the comprehensive assessment of SOH.

[0127] Current transfer impedance (R ct ) analysis:

[0128] Electrode reaction performance evaluation: R ct Reflects the activity of the electrode surface reaction. As the battery ages, a passivation layer will form on the electrode surface or other degradation phenomena will occur, resulting in R ct This increase will reduce the charging and discharging efficiency of the battery and lead to an increase in polarization.

[0129] SOH calculation contribution: The health status prediction unit compares the current R ct Compared with the initial state of the battery R ct, to evaluate the degree of electrode degradation. ct An increase usually means a significant deterioration in the battery's state of health and therefore carries a significant weight in the SOH calculation.

[0130] Diffusion impedance (Z diff ) analysis:

[0131] Diffusion process analysis: diffusion impedance Z diff It is a key parameter in low-frequency electrochemical impedance spectroscopy, reflecting the difficulty of charge or ion diffusion in the electrode material. As the battery is used, the electrode material will undergo structural changes, resulting in an increase in diffusion impedance.

[0132] Long-term prediction of health status: The health status prediction unit monitors Z diff The change of Z can predict the long-term decline trend of battery performance. diff This usually means that the battery performs poorly at high current density, which also has an impact on the accurate estimation of SOC.

[0133] Detailed analysis of the state of health (SOH):

[0134] Comprehensive SOH assessment: Health status prediction unit combined with Z ele , R ct and Z diff The analysis results are used to comprehensively evaluate the health status of the battery. The specific formula is:

[0135]

[0136] in Based on the electrolyte impedance, Based on the current transfer impedance, Based on diffusion impedance, is the corresponding weight factor, which is usually determined by calibration of experimental data.

[0137] Detailed analysis of the charge level (SOC):

[0138] Real-time SOC estimation: SOC estimation combines the advantages of the open circuit voltage method and the coulomb counting method. Impedance parameters such as Z in the internal state model ele and Z diff Dynamic compensation is provided during SOC estimation. For example, when Z diff When a large diffusion impedance is shown, the integration factor in the coulomb counting method needs to be adjusted to compensate for the battery's power loss under actual operating conditions.

[0139] SOC correction algorithm: Through Kalman filtering or other adaptive filtering methods, the health status prediction unit corrects the SOC in real time to reduce the deviation caused by measurement error or model uncertainty.

[0140] After completing the analysis of SOH and SOC, the health status prediction unit outputs the results to the battery management system or user interface for further battery management and maintenance decisions. These analysis results can help users understand the health status and charging status of the battery in real time, make timely maintenance or replacement decisions, and ensure the stable operation of the battery system.

[0141] Through the above detailed parsing process, the health status prediction unit can effectively extract the current health status information of the battery from the internal state model, providing solid data support for battery status monitoring and life management.

[0142] Embodiment 5 Communication unit

[0143] The communication unit 104 is responsible for transmitting the predicted state of health (SOH) and the predicted state of charge (SOC) generated by the health state prediction unit to the user, the relevant computing system, or a combination of the two. Through the transmission of these data, the user can monitor the health status of the battery in real time, so as to make timely maintenance decisions or adjust the operating parameters of the battery. This real-time data transmission is essential to ensure the long-term stable operation of the energy storage power station.

[0144] The communication unit plays a vital role in the battery status assessment system. It is not only a bridge for information transmission, but also has the task of accurately and timely transmitting the key data generated by the health status prediction unit to the user end or other systems. Through this unit, users and management systems can obtain detailed information about the battery's operating status, such as the current health and charge level. This data is crucial for real-time monitoring and management of the battery, and directly affects the battery's service life and safety.

[0145] With the help of SOH and SOC data transmitted by the communication unit, users can observe the changes in battery status in real time through the monitoring platform. SOC data can help users understand the current charging status of the battery, thereby optimizing the charging and discharging strategy to avoid overcharging or over-discharging. SOH data provides the health status of the battery. Users can formulate preventive maintenance plans or replace aging batteries in time according to the changing trend of health status to avoid system failure or performance degradation caused by battery degradation.

[0146] The communication unit can also transmit this data to the relevant computing system for more complex analysis and processing. For example, the computing system can automatically adjust the parameter settings of the battery management system (BMS) based on real-time SOH and SOC data, optimize the battery's charge and discharge curve, and improve the overall efficiency of the system. At the same time, this data can also be used for big data analysis or machine learning model training, thereby continuously improving the accuracy and prediction ability of battery status assessment.

[0147] Through efficient data transmission of the communication unit, the energy storage power station can operate in a dynamic, data-driven environment. This not only improves the system's response speed and management efficiency, but also greatly enhances the system's stability and reliability. Timely and accurate acquisition and processing of battery health and charging status data enables users to make fact-based decisions, thereby extending battery life, reducing operating costs, and ensuring the safe operation of energy storage power stations under various working conditions.

[0148] In summary, the communication unit 104 not only provides key decision support for users and computing systems by transmitting the battery's SOH and SOC data in real time, but also effectively ensures the long-term stable operation of the energy storage power station. It plays an indispensable role in the entire battery status assessment system.

[0149] The above description is only a preferred embodiment of the present invention, so all equivalent changes or modifications made according to the structure, characteristics and principles described in the scope of the patent application of the present invention are included in the scope of the patent application of the present invention.

Claims

1. A battery status assessment system for energy storage power stations, Features: The battery state assessment system comprises a characteristic acquisition unit (101), a state prediction unit (102), a health state prediction unit (103) and a communication unit (104); The characteristic acquisition unit (101) receives input data, the input data comprising passive data collected from passive measurements of the battery and active data collected from active measurements of the battery in response to a stimulus signal applied to the battery; The characteristic acquisition unit (101) performs variable identification based on shape characteristics based on input data related to the circuit equivalent model to obtain shape variables; performs variable identification based on improvement based on input data related to the circuit equivalent model to obtain improved variables; The state prediction unit (102) combines the shape variables and the improved variables through a determination algorithm to output updated internal variables of the battery cell, wherein the updated internal variables of the battery cell at least include extended impedance, electrolyte impedance and current transfer impedance; The state prediction unit (102) comprises a prediction model, the state prediction unit (102) dynamically adjusts the parameters of the prediction model according to the battery operation state and environmental changes, and the state prediction unit (102) updates the internal state model of the battery according to the adjusted prediction model and based on the updated internal variables of the battery cell; The health state prediction unit (103) is used to analyze the internal state model to determine the current health state of the battery, including the health and charging level of the battery; The communication unit (104) is used to send one or more health prediction values ​​and charge level prediction values ​​to a user, a related computing system, or a combination of the two; The prediction model is a dynamically adjustable model, and its mathematical expression is as follows: in, Is the battery in time The terminal voltage at is the open circuit voltage; is the instantaneous current; is the series resistance; Indicates frequency The electrolyte impedance under is the current transfer impedance; is the diffusion impedance; The parameters of the dynamic adjustment of the prediction model are specifically: Adjusting electrolyte impedance parameters through impedance spectrum data; Adjust the current transfer impedance parameters by fitting the volt-ampere characteristic curve; Adjust diffusion impedance parameters through low-frequency impedance measurement data; The dynamic adjustment of the parameters of the prediction model further includes temperature compensation, and the temperature compensation model is: in: For temperature The electrolyte impedance under The base temperature The electrolyte impedance under is the activation energy; is the Boltzmann constant; is the current temperature, is the reference temperature.

2. A battery status assessment system for an energy storage power station as claimed in claim 1, Features: The passive data includes the open circuit voltage of the battery, the internal resistance in a static state and the temperature of the battery; The active data is measurement data collected from the battery response after the characteristic acquisition unit (101) applies an external stimulation signal, wherein the external stimulation signal includes a current pulse signal and an AC frequency sweep signal.

3. A battery status assessment system for an energy storage power station as claimed in claim 1, Features: The characteristic acquisition unit (101) performs variable identification related to the circuit equivalent model based on the acquired passive data and active data, and the variable identification includes: Obtaining shape variables describing the health status of the battery through passive data, wherein the shape variables include the slope of the voltage recovery curve and the characteristic curve of the internal resistance changing with temperature; Improved variables are extracted by combining active data with an electrochemical model or an equivalent circuit model, and the improved variables include diffusion impedance and charge transfer impedance.

4. A battery status assessment system for an energy storage power station as claimed in claim 1, Features: The electrolyte impedance in the updated battery cell internal variable is measured by frequency domain analysis, and the determination algorithm calculates the impedance characteristics of the electrolyte by using the relationship between the current response and the frequency of the applied AC signal; The current transfer impedance in the updated internal variables of the battery cell is obtained by fitting the volt-ampere characteristic curve; The diffusion impedance in the updated internal variables of the battery cell is obtained by measuring the electrochemical impedance spectroscopy in the low-frequency range.

5. A battery status assessment system for an energy storage power station as claimed in claim 1, Features: The health status prediction unit (103) analyzes the internal status model and specifically includes: The degree of electrolyte degradation is determined by the change in electrolyte impedance; The degradation degree of the electrode surface reaction is evaluated by the change of current transfer impedance; The diffusion process inside the battery is analyzed by the change of diffusion impedance, and the long-term decline trend of battery performance is predicted.

6. A battery status assessment system for an energy storage power station as claimed in claim 1, Features: The health state prediction unit (103) comprehensively evaluates the health state of the battery. The battery health state evaluation formula for: in The health status based on the electrolyte impedance, Based on the health status of the current transfer impedance, Based on the health status of diffusion impedance, , , is the corresponding weight factor.

7. A battery status assessment system for an energy storage power station as claimed in claim 1, Features: The health status prediction unit (103) comprehensively evaluates the charging level of the battery, specifically including estimating the charging level by combining an open circuit voltage method and a coulomb counting method; performing dynamic compensation through impedance parameters in an internal state model; and correcting the charging level in real time through a Kalman filter or an adaptive filter method.

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