Electrochemical energy storage lithium iron phosphate battery dynamic internal resistance intelligent monitoring method and system

Abstract

The invention provides an electrochemical energy storage lithium iron phosphate battery dynamic internal resistance intelligent monitoring method, system and device and a storage medium, and the method comprises the steps: employing a forgetting factor recursive least square method to recognize Thevenin battery model parameters, and obtaining the battery model parameters changing in real time; battery square wave current node data analysis is utilized to calculate an average value of internal resistance parameters to obtain battery internal resistance; and accumulating and storing the battery internal resistance data of each cycle, thereby deeply analyzing the dynamic change trend of the battery internal resistance. The health condition and the performance change of the battery can be accurately evaluated, and the service life and the safety of the battery are improved.

Description

Technical Field

[0001] The present invention relates to the field of electrochemical energy storage, and more specifically, to a method, system, device and storage medium for intelligently monitoring the dynamic internal resistance of an electrochemical energy storage lithium iron phosphate battery. Background Art

[0002] Among the existing electrochemical energy storage technologies, lithium iron phosphate batteries are widely used in electric vehicles, energy storage power stations and other fields due to their long life, high safety and good charge and discharge performance. However, during use, the battery will be affected by many factors, such as temperature, number of charge and discharge cycles, etc., which will cause the internal resistance of the battery to change. The change in internal resistance directly affects the battery's charge and discharge efficiency, power output and life. Therefore, accurately monitoring the battery's internal resistance is crucial to evaluating the battery status, predicting the battery life and ensuring the safe operation of the battery.

[0003] Traditional battery internal resistance monitoring methods mostly rely on offline laboratory tests, which are not only time-consuming and labor-intensive, but also unable to achieve real-time monitoring. With the development of technology, some online monitoring methods have been proposed, but these methods often rely on complex hardware equipment or require a lot of computing resources, limiting their widespread deployment in practical applications. In addition, due to the dynamic change characteristics of battery internal resistance, traditional monitoring methods are difficult to accurately capture the real-time changes of internal resistance, resulting in inaccurate battery status assessment.

[0004] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: the existing monitoring methods cannot effectively realize real-time and dynamic monitoring of the internal resistance of lithium iron phosphate batteries, and lack efficient and accurate algorithm support in data processing and analysis, resulting in the inability to timely and accurately evaluate the health status of the battery and predict the battery life. Summary of the invention

[0005] The present invention provides a method, system, device and storage medium for intelligently monitoring the dynamic internal resistance of an electrochemical energy storage lithium iron phosphate battery.

[0006] In a first aspect of the present invention, a method for intelligently monitoring the dynamic internal resistance of an electrochemical energy storage lithium iron phosphate battery is provided, comprising:

[0007] The forgetting factor recursive least squares method is used to identify the parameters of the Thevenin battery model and obtain the battery model parameters that change in real time.

[0008] Utilize the battery square wave current node data analysis to calculate the average value of the internal resistance parameter to obtain the battery internal resistance;

[0009] The battery internal resistance data of each cycle is accumulated and stored, and the dynamic change trend of the battery internal resistance is deeply analyzed based on this.

[0010] Furthermore, the parameter identification step includes:

[0011] receiving an input signal from a battery;

[0012] Real-time identification of battery model internal resistance parameters;

[0013] Output the identified internal resistance parameters of the battery model.

[0014] Furthermore, the dynamic data analysis step includes:

[0015] Receiving the identified internal resistance parameters of the battery model;

[0016] Analyze the data after the battery square wave current node, calculate the average value of the internal resistance parameter as the battery internal resistance, and accumulate and store the battery internal resistance data of each cycle;

[0017] Output the estimated dynamic internal resistance value and dynamic change trend.

[0018] Furthermore, the forgetting factor recursive least squares method step includes:

[0019] Set the forgetting factor;

[0020] Update the battery model parameters according to the forgetting factor and the input signal.

[0021] Furthermore, the dynamic data algorithm steps include:

[0022] Preprocess the input battery model parameter data, eliminate noise and outliers, and extract the parameter data after the square wave current;

[0023] The pre-processed parameter data is averaged to obtain the best data to represent the battery internal resistance;

[0024] Accumulate and store the internal resistance data of each battery in each charge and discharge cycle;

[0025] The variance and standard deviation of the accumulated stored data are calculated to analyze the dynamic change trend of the battery internal resistance.

[0026] Furthermore, a Thevenin lithium-ion battery model is established, and the electrical characteristic equation of the model in the time domain is:

[0027]

[0028] Among them, Uoc is the open circuit voltage of the battery, R is the internal resistance of the battery; Rh and Ch are the resistance and capacitance of the first RC loop; Rz and Cz are the resistance and capacitance of the second RC loop, U is the terminal voltage of the battery, I L is the input current.

[0029] Furthermore, the dynamic data algorithm steps include:

[0030] Step 1: receiving the battery model parameters from the parameter identification calculation module, and performing preprocessing to eliminate noise and abnormal values, and extracting the parameter data after the square wave current node;

[0031] Step 2: Further analyze the parameter data processed by the dynamic data interception unit and obtain the best data by calculating the average value;

[0032] Step 3: Accumulate and store the internal resistance data of each battery in each charge and discharge cycle;

[0033] Step 4: Utilize the data in the dynamic data accumulation storage unit to analyze the changing trend of the battery internal resistance as the number of cycles increases.

[0034] In a second aspect of the present invention, a dynamic internal resistance intelligent monitoring system for an electrochemical energy storage lithium iron phosphate battery is provided, comprising:

[0035] An identification module is used to identify the parameters of the Thevenin battery model using a forgetting factor recursive least squares method to obtain battery model parameters that change in real time;

[0036] The battery internal resistance calculation module is used to analyze the battery square wave current node data and calculate the average value of the internal resistance parameter to obtain the battery internal resistance;

[0037] The analysis module is used to accumulate and store the battery internal resistance data of each cycle, and analyze the dynamic change trend of the battery internal resistance based on it.

[0038] In a third aspect of the present invention, an electronic device is provided, comprising: at least one processor, a memory and an input-output unit; wherein the memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute any one of the methods described in the first aspect.

[0039] In a fourth aspect of the present invention, a computer-readable storage medium is provided, which includes instructions, and when the instructions are executed on a computer, the computer executes any one of the methods in the first aspect.

[0040] The above-mentioned embodiments of the present invention have at least the following beneficial effects: the real-time identification of the Thevenin battery model parameters by the forgetting factor recursive least squares method can accurately capture the real-time changes of the lithium iron phosphate battery parameters, thereby obtaining more accurate battery model parameters. This method can more accurately reflect the actual working state of the battery by updating the battery model parameters in real time, and provide reliable data support for the health status assessment of the battery. At the same time, by analyzing the data after the square wave current node and calculating the average value of the internal resistance parameter, the dynamic change of the battery internal resistance can be effectively obtained, and then the battery internal resistance data of each cycle can be accumulated and stored, providing a data basis for in-depth analysis of the dynamic change trend of the battery internal resistance.

[0041] By building a Thevenin lithium-ion battery model and combining it with the dynamic data algorithm steps, the internal resistance of the battery can be deeply analyzed, including the calculation of variance and standard deviation, so as to fully understand the changing trend of the internal resistance of the battery with the number of charge and discharge cycles. This method can not only improve the accuracy of battery status monitoring, but also provide a scientific decision-making basis for battery maintenance and replacement, thereby extending the service life of the battery, reducing maintenance costs, and improving the overall safety of battery use. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0043] Figure 1 A schematic diagram of a process for intelligently monitoring the dynamic internal resistance of a lithium iron phosphate battery for electrochemical energy storage provided by an embodiment of the present invention;

[0044] Figure 2 A flow chart of dynamic data analysis provided by an embodiment of the present invention;

[0045] Figure 3 It is a schematic diagram of the second-order RC equivalent circuit model of a lithium-ion battery;

[0046] Figure 4 A schematic diagram of an experimental example provided for an embodiment of the present invention;

[0047] Figure 5 A schematic diagram of an experimental example provided for an embodiment of the present invention;

[0048] Figure 6 A schematic diagram of an experimental example provided for an embodiment of the present invention;

[0049] Figure 7 A schematic diagram of an experimental example provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0051] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, apparatus, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0052] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0053] Reference below Figure 1 , Figure 1 A schematic diagram of a flow chart of a method for intelligently monitoring the dynamic internal resistance of an electrochemical energy storage lithium iron phosphate battery provided by an embodiment of the present invention. Figure 1 As shown, a method 100 for intelligently monitoring dynamic internal resistance of an electrochemical energy storage lithium iron phosphate battery includes:

[0054] The forgetting factor recursive least squares method is used to identify the parameters of the Thevenin battery model and obtain the battery model parameters that change in real time.

[0055] Utilize the battery square wave current node data analysis to calculate the average value of the internal resistance parameter to obtain the battery internal resistance;

[0056] The battery internal resistance data of each cycle is accumulated and stored, and the dynamic change trend of the battery internal resistance is deeply analyzed based on this.

[0057] It should be noted that the core of this method is to use the forgetting factor recursive least squares method to identify the parameters of the Thevenin battery model to obtain real-time changes in the battery model parameters. Here, the Thevenin battery model refers to an equivalent circuit model used to describe the behavior of the battery in the circuit, which includes parameters such as open circuit voltage and internal resistance. The forgetting factor recursive least squares method is an algorithm that balances the weights of historical data and the latest data by setting a forgetting factor, thereby achieving dynamic updates of battery model parameters.

[0058] Specifically, the method first receives input signals of the battery, which may include electrical characteristics of the battery such as voltage and current, and then processes these signals using a forgetting factor recursive least squares method to identify the internal resistance parameters of the battery model.

[0059] More specifically, in this process, the setting of the forgetting factor is very critical, which determines the degree of reliance of the algorithm on historical data. Usually, the value of the forgetting factor is between 0 and 1. A smaller forgetting factor means that the algorithm attaches more importance to the latest data, while a larger forgetting factor means that the algorithm attaches more importance to historical data.

[0060] Preferably, in order to improve the accuracy of parameter identification, the input signal can be preprocessed to eliminate noise and outliers. In addition, the performance of the algorithm can be optimized by adjusting the value of the forgetting factor to better adapt to changes in battery parameters. For example, a larger forgetting factor can be set at the beginning of battery use to quickly learn the initial state of the battery, and then the forgetting factor can be gradually reduced over time to more sensitively capture changes in battery parameters.

[0061] Furthermore, other factors such as battery temperature and charge and discharge history can be considered to further improve the accuracy and robustness of the model.

[0062] In some embodiments, the parameter identification step includes:

[0063] receiving an input signal from a battery;

[0064] Real-time identification of battery model internal resistance parameters;

[0065] Output the identified internal resistance parameters of the battery model.

[0066] It should be noted that this implementation describes in detail the parameter identification step, which is a very critical part of the entire monitoring method. In this step, receiving the input signal of the battery means that the system needs to obtain electrical parameters such as voltage and current from the battery, which are the basis for identifying the battery model parameters. Real-time identification of the internal resistance parameters of the battery model involves using a specific algorithm to process these input signals to determine key parameters such as the internal resistance of the battery.

[0067] Specifically, the parameter identification step begins with receiving input signals of the battery under different working conditions. These signals may include but are not limited to the open circuit voltage, charge and discharge current, ambient temperature, etc. of the battery. These signals are collected by sensors and then transmitted to the processor. Next, the forgetting factor recursive least squares method is used to process these signals in real time to identify the internal resistance parameters of the battery model. In this process, it is necessary to set a suitable forgetting factor, which will affect the algorithm's weighting of new and old data, thereby affecting the accuracy and response speed of parameter identification.

[0068] Preferably, in order to improve the accuracy of identification, an adaptive mechanism can be introduced into the algorithm so that the forgetting factor can be dynamically adjusted according to the change of the battery state. For example, when the battery state changes significantly, the value of the forgetting factor can be increased to adapt to the new state more quickly.

[0069] Furthermore, the accuracy of parameter identification can be further improved by introducing machine learning techniques, such as neural networks. These techniques can better capture the complex relationship between battery parameters, thereby providing more accurate battery model parameter estimation. In practical applications, it is also possible to consider combining these identified parameters with other battery health indicators to achieve more comprehensive battery status monitoring.

[0070] like Figure 2 As shown, in some embodiments, the dynamic data analysis step includes:

[0071] Receiving the identified internal resistance parameters of the battery model;

[0072] Analyze the data after the battery square wave current node, calculate the average value of the internal resistance parameter as the battery internal resistance, and accumulate and store the battery internal resistance data of each cycle;

[0073] Output the estimated dynamic internal resistance value and dynamic change trend.

[0074] It should be noted that this implementation focuses on the dynamic data analysis step, which calculates and analyzes the change in battery internal resistance by receiving and processing the internal resistance parameters of the battery model. The battery square wave current nodes here refer to the points where the current suddenly changes during the battery charging and discharging process. These nodes can be used to analyze the internal resistance of the battery. The cumulative storage of the battery internal resistance data for each cycle involves saving the internal resistance data obtained in each charge and discharge cycle for long-term trend analysis.

[0075] Specifically, the dynamic data analysis step first receives the battery model internal resistance parameters from the parameter identification step. These parameters include the battery's open circuit voltage, internal resistance, state of charge, etc. Then, by analyzing the data after the battery square wave current node, the average value of the internal resistance parameters is calculated to estimate the battery's internal resistance.

[0076] More specifically, in this process, the data needs to be preprocessed to eliminate noise and outliers to ensure the accuracy of the calculation. The accumulated stored data can include the internal resistance value at the beginning and end of each charge and discharge cycle, as well as any significant changes during the cycle.

[0077] Preferably, in order to improve the accuracy and efficiency of data analysis, advanced data processing techniques can be used, such as filtering algorithms to reduce the impact of noise, or statistical methods to identify and process outliers. In addition, an automated trigger mechanism can be set up, and when the change in battery internal resistance exceeds a preset threshold, the system will automatically record and analyze this change, so as to promptly discover potential problems with battery performance.

[0078] Furthermore, when storing data, a database management system may be used to facilitate querying, updating, and maintaining the data.

[0079] Furthermore, the accumulated stored data can be regularly statistically analyzed, such as calculating the rate of change of the internal resistance, trend lines, etc., so as to gain a deeper understanding of the long-term trend of battery performance.

[0080] In some embodiments, the forgetting factor recursive least squares method step includes:

[0081] Set the forgetting factor;

[0082] Update the battery model parameters according to the forgetting factor and the input signal.

[0083] It should be noted that this implementation method describes in detail the specific steps of the forgetting factor recursive least squares method, which is a key technology for updating battery model parameters. The forgetting factor is an important parameter that determines the degree of importance the algorithm places on historical data when updating parameters. By setting an appropriate forgetting factor, the weights of new and old data can be balanced, thereby more accurately reflecting the real-time changes in battery model parameters.

[0084] Specifically, the forgetting factor recursive least squares step first involves setting a forgetting factor, which is usually a value between 0 and 1. The choice of the forgetting factor depends on how fast the battery model parameters change and the system's requirements for real-time performance. For example, if the battery parameters change quickly, a smaller forgetting factor can be selected to adapt to these changes more quickly. Next, based on the forgetting factor and the input signal, the battery model parameters are updated. This usually involves a mathematical formula that combines the current input signal and the previous parameter estimates, as well as the forgetting factor, to calculate the new parameter estimates.

[0085] Preferably, in order to improve the accuracy and robustness of parameter updates, an adaptive forgetting factor method can be used. This means that the forgetting factor is not fixed, but dynamically adjusted according to changes in the battery state. For example, when a significant change in the battery state is detected, the forgetting factor can be temporarily reduced to adapt to the new state more quickly.

[0086] Furthermore, other algorithms, such as Kalman filter, can be introduced to further improve the accuracy of parameter estimation. In practical applications, it is also possible to consider combining the forgetting factor recursive least squares method with other battery status monitoring technologies to achieve more comprehensive battery health management.

[0087] More specifically, a feedback mechanism can be set up to adjust the forgetting factor according to the difference between the actual performance and the predicted performance of the battery, thereby achieving finer parameter control.

[0088] In some embodiments, the dynamic data algorithm steps include:

[0089] Preprocess the input battery model parameter data, eliminate noise and outliers, and extract the parameter data after the square wave current;

[0090] The pre-processed parameter data is averaged to obtain the best data to represent the battery internal resistance;

[0091] Accumulate and store the internal resistance data of each battery in each charge and discharge cycle;

[0092] The variance and standard deviation of the accumulated stored data are calculated to analyze the dynamic change trend of the battery internal resistance.

[0093] It should be noted that this embodiment describes the dynamic data algorithm steps, which involve preprocessing, analysis and storage of battery model parameter data. Preprocessing refers to a series of operations performed on data before data analysis, with the purpose of improving data quality and eliminating noise and outliers. Square wave current refers to a specific waveform of a current signal that changes suddenly at a specific time point and is used to analyze the internal resistance of a battery.

[0094] Specifically, the dynamic data algorithm steps first include preprocessing the input battery model parameter data, which usually involves removing noise from the data and identifying and processing outliers. After preprocessing, the algorithm will intercept the parameter data after the square wave current, because the current change after the square wave current can more accurately reflect the internal resistance characteristics of the battery. Then, the best data is obtained by calculating the average value of these data to represent the internal resistance of the battery. In addition, the algorithm will accumulate and store the internal resistance data of each battery in each charge and discharge cycle to provide a basis for subsequent data analysis.

[0095] Preferably, the preprocessing step can use a variety of techniques, such as a low-pass filter to reduce high-frequency noise, or use statistical methods to identify and remove outliers. When intercepting the parameter data after the square wave current, a specific time window or current threshold can be set to determine the interception point. When calculating the average value, the arithmetic mean, weighted average or other more complex statistical methods can be selected to obtain a more accurate internal resistance estimate. The accumulated stored data can include information such as timestamp, internal resistance value, charge and discharge status, etc., to facilitate subsequent detailed analysis.

[0096] More specifically, a database or data warehouse can be used to store this data to support large-scale data processing and query operations. In addition, data compression technology can be introduced to reduce the demand for storage space while ensuring the accessibility and integrity of the data.

[0097] In some embodiments, a Thevenin lithium-ion battery model is established, and the electrical characteristic equation of the model in the time domain is:

[0098]

[0099] Among them, Uoc is the open circuit voltage of the battery, R is the internal resistance of the battery; Rh and Ch are the resistance and capacitance of the first RC loop; Rz and Cz are the resistance and capacitance of the second RC loop, U is the terminal voltage of the battery, I L is the input current. The second-order RC equivalent circuit model is as follows Figure 3 shown.

[0100] Furthermore, the above formula can be expressed in the frequency domain as follows:

[0101]

[0102] Furthermore, the above formula can be written into the transfer function expression as follows:

[0103]

[0104] Furthermore, the above formula is rearranged as follows:

[0105]

[0106] Further, let τ1 = R h C h ,τ2=R z C z ,

[0107] Further simplifying, we get:

[0108]

[0109] Among them, m1-m5 are coefficients, as shown below:

[0110]

[0111] Furthermore, define a, b, c, d, and e as follows:

[0112]

[0113] Further simplifying, we get:

[0114]

[0115] Furthermore, the battery internal resistance parameter R is summarized as:

[0116]

[0117] Specifically, the recursive least squares method uses the square criterion of discrete functions as its theoretical basis to obtain the identified parameters. Its basic principle is shown in the following formula: is the observation data matrix, and θ is the parameter matrix.

[0118]

[0119] The specific definition is:

[0120] Furthermore, the system introduces the forgetting factor λ as an adjustment coefficient in the observation data matrix and the system output vector. Specifically, the covariance matrix P(k), the gain matrix K(k), the error term e(k) and the output variable y are all affected by the forgetting factor. Whenever new observation data arrives, the system processes the data through an exponential weighting strategy to adjust the influence of new and old data, and then calculates the weighted average to update the identification parameters. This method ensures that as the observation data accumulates, the system can quickly adapt to input changes and effectively improve the response speed and accuracy of the identification parameters.

[0121]

[0122] Furthermore, when the online identification parameter error is large, the convergence speed of the online identification can be accelerated by optimizing the forgetting factor b1, thereby reducing the error.

[0123] Preferably, in order to more accurately simulate the behavior of the battery, the parameters in the model can be calibrated to match the characteristics of a specific battery or battery pack. For example, the open circuit voltage and internal resistance can be measured experimentally, while the state of charge SOC can be estimated from the charge and discharge history of the battery. The input current can be set according to the operating mode of the battery, while the parameters of the electrochemical polarization and concentration polarization effects may need to be determined through more complex electrochemical tests.

[0124] More specifically, experimental data can be used to verify and adjust the model to ensure the accuracy of the model prediction. In addition, the impact of temperature, aging and other environmental factors on battery parameters can also be considered, and the applicability and robustness of the model can be further improved by introducing these factors.

[0125] In some embodiments, the dynamic data algorithm steps include:

[0126] Step 1: receiving the battery model parameters from the parameter identification calculation module, and performing preprocessing to eliminate noise and abnormal values, and extracting the parameter data after the square wave current node;

[0127] Step 2: Further analyze the parameter data processed by the dynamic data interception unit and obtain the best data by calculating the average value;

[0128] Step 3: Accumulate and store the internal resistance data of each battery in each charge and discharge cycle;

[0129] Step 4: Utilize the data in the dynamic data accumulation storage unit to analyze the changing trend of the battery internal resistance as the number of cycles increases.

[0130] It should be noted that this implementation describes in detail the dynamic data algorithm steps, which are intended to process the battery model parameter data through a series of operations and analyze the changing trend of the battery internal resistance. In this step, the parameter identification calculation module refers to the system or algorithm used to calculate the battery model parameters, and preprocessing refers to a series of operations performed on the data before data analysis to improve the data quality.

[0131] Specifically, the first step of the implementation method is to receive the battery model parameters from the parameter identification calculation module and preprocess them to eliminate noise and outliers. This may involve using filters to smooth the data, or using statistical methods to identify and remove outliers. Intercepting the parameter data after the square wave current node means that the system will focus on analyzing the data after the current changes, because these data can better reflect the actual internal resistance state of the battery.

[0132] Preferably, the preprocessing step may include using a digital filter, such as a Kalman filter, to reduce the effects of measurement noise. In addition, an automated anomaly detection algorithm, such as a statistically based Z-score method, may be used to identify and process outliers. When intercepting parameter data after the square wave current node, a specific time window or current threshold may be set to determine the interception point to ensure that only data related to the battery internal resistance is analyzed.

[0133] More specifically, machine learning techniques, such as support vector machines (SVM) or neural networks, can be used to automatically identify and classify patterns in the data, thereby improving the accuracy of data capture. In addition, it is also possible to consider introducing adaptive algorithms to dynamically adjust the parameters of data capture to adapt to changes in battery status.

[0134] The above-mentioned embodiments of the present invention have the following beneficial effects: the intelligent monitoring method of dynamic internal resistance of electrochemical energy storage lithium iron phosphate battery described in the present invention can provide a real-time and accurate monitoring means, and can track the changes of battery internal resistance in real time by using the forgetting factor recursive least squares method to identify the battery model parameters, so as to timely capture the subtle changes of battery performance. This method can effectively evaluate the health of the battery, predict the life of the battery, provide a scientific basis for the maintenance and safety management of the battery, and thus improve the efficiency and safety of the battery.

[0135] In addition, this method can deeply analyze the dynamic change trend of the battery internal resistance by accumulating and storing the battery internal resistance data of each charge and discharge cycle and calculating the variance and standard deviation. This in-depth data analysis capability can reveal potential problems in the battery aging process and provide important reference for the optimization design and improvement of the battery. With this method, the battery system can be managed and maintained more effectively, reducing unexpected downtime and safety accidents caused by battery failure, thereby reducing operating costs and improving system reliability.

[0136] In some embodiments, a dynamic internal resistance intelligent monitoring system of an electrochemical energy storage lithium iron phosphate battery includes:

[0137] An identification module is used to identify the parameters of the Thevenin battery model using a forgetting factor recursive least squares method to obtain battery model parameters that change in real time;

[0138] The battery internal resistance calculation module is used to analyze the battery square wave current node data and calculate the average value of the internal resistance parameter to obtain the battery internal resistance;

[0139] The analysis module is used to accumulate and store the battery internal resistance data of each cycle, and analyze the dynamic change trend of the battery internal resistance based on it.

[0140] It is understandable that the modules recorded in the electrochemical energy storage lithium iron phosphate battery dynamic internal resistance intelligent monitoring system are the same as those in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the intelligent monitoring method for dynamic internal resistance of lithium iron phosphate batteries for electrochemical energy storage are also applicable to the intelligent monitoring system for dynamic internal resistance of lithium iron phosphate batteries for electrochemical energy storage and the modules contained therein, and will not be described in detail here.

[0141] In order to verify the accuracy and effectiveness of the intelligent monitoring method of dynamic internal resistance of electrochemical energy storage lithium iron phosphate battery of the present invention, we adopted a specific experimental setting. In the experiment, we selected the energy storage system iESS-CAB-W215H intelligent energy storage air-cooled integrated cabinet provided by Hongzheng Energy Storage, which consists of 240 lithium iron phosphate batteries with a rated capacity of 280Ah produced by Ruipu Lanjun Energy Co., Ltd. By simulating the actual energy storage operation environment, we conducted 100 complete charge and discharge cycle tests on these batteries.

[0142] In these tests, our intelligent monitoring method demonstrated significant superiority. By using the forgetting factor recursive least squares method to identify the battery model parameters in real time, and analyzing the battery square wave current node data through the dynamic data analysis and calculation module, our method can accurately calculate the average value of the battery internal resistance and accumulate and store the battery internal resistance data for each cycle. This cumulative storage mechanism not only helps to track the changes in battery internal resistance over a long period of time, but also provides strong data support for the evaluation of battery health status.

[0143] like Figure 4-7 As shown in the test results, our intelligent monitoring method shows higher accuracy than the existing technology in estimating the internal resistance of the battery. Compared with the AC impedance method, the DC internal resistance method, and the open circuit voltage method, the present invention not only surpasses these traditional methods in the accuracy of a single measurement, but also can conduct in-depth analysis of the changing trend of the battery internal resistance as the number of cycles increases through a dynamic data trend analysis unit. This trend analysis capability adds additional value to our method, enabling it to not only provide static internal resistance measurements, but also predict the long-term performance and life of the battery.

[0144] These test results not only verify the effectiveness of the technical solution of the present invention, but also demonstrate its potential in practical applications. Through these experiments, we further confirm the practicality of the present invention in predicting the battery cycle life of energy storage battery management systems and electric vehicle systems.

[0145] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention to form a technical solution.

Claims

1. An intelligent monitoring method for dynamic internal resistance of an electrochemical energy storage lithium iron phosphate battery, characterized in that: The following steps are involved: The forgetting factor recursive least squares method is used to identify the parameters of the Thevenin battery model and obtain the battery model parameters that change in real time. Utilize the battery square wave current node data analysis to calculate the average value of the internal resistance parameter to obtain the battery internal resistance; The battery internal resistance data of each cycle is accumulated and stored, and the dynamic change trend of the battery internal resistance is deeply analyzed based on this.

2. The method according to claim 1, characterized in that The parameter identification step comprises: receiving an input signal from a battery; Real-time identification of battery model internal resistance parameters; Output the identified internal resistance parameters of the battery model.

3. The method according to claim 1, characterized in that The dynamic data analysis step comprises: Receiving the identified internal resistance parameters of the battery model; Analyze the data after the battery square wave current node, calculate the average value of the internal resistance parameter as the battery internal resistance, and accumulate and store the battery internal resistance data of each cycle; Output the estimated dynamic internal resistance value and dynamic change trend.

4. The method according to claim 1, characterized in that: The forgetting factor recursive least squares method step includes: Set the forgetting factor; Update the battery model parameters according to the forgetting factor and the input signal.

5. The method according to claim 1, characterized in that The dynamic data algorithm steps include: Preprocess the input battery model parameter data, eliminate noise and outliers, and extract the parameter data after the square wave current; The pre-processed parameter data is averaged to obtain the best data to represent the battery internal resistance; Accumulate and store the internal resistance data of each battery in each charge and discharge cycle; The variance and standard deviation of the accumulated stored data are calculated to analyze the dynamic change trend of the battery internal resistance.

6. The method according to claim 1, characterized in that The Thevenin lithium-ion battery model is established, and the electrical characteristic equation of the model in the time domain is: Among them, Uoc is the open circuit voltage of the battery, R is the internal resistance of the battery; Rh and Ch are the resistance and capacitance of the first RC loop; Rz and Cz are the resistance and capacitance of the second RC loop, U is the terminal voltage of the battery, I L is the input current.

7. The method according to claim 1, characterized in that The dynamic data algorithm steps include: Step 1: receiving the battery model parameters from the parameter identification calculation module, and performing preprocessing to eliminate noise and abnormal values, and extracting the parameter data after the square wave current node; Step 2: Further analyze the parameter data processed by the dynamic data interception unit and obtain the best data by calculating the average value; Step 3: Accumulate and store the internal resistance data of each battery in each charge and discharge cycle; Step 4: Utilize the data in the dynamic data accumulation storage unit to analyze the changing trend of the battery internal resistance as the number of cycles increases.

8. An intelligent monitoring system for dynamic internal resistance of an electrochemical energy storage lithium iron phosphate battery, characterized in that: include: An identification module is used to identify the parameters of the Thevenin battery model using a forgetting factor recursive least squares method to obtain battery model parameters that change in real time; The battery internal resistance calculation module is used to analyze the battery square wave current node data and calculate the average value of the internal resistance parameter to obtain the battery internal resistance; The analysis module is used to accumulate and store the battery internal resistance data of each cycle, and analyze the dynamic change trend of the battery internal resistance based on it.

9. An electronic device, characterized in that: It comprises a processor and a memory coupled to the processor, wherein the memory stores program instructions executable by the processor; when the processor executes the program instructions stored in the memory, the method as claimed in any one of claims 1 to 7 is implemented.

10. A storage medium, characterized in that: The storage medium stores program instructions, and when the program instructions are executed by a processor, the method according to any one of claims 1 to 7 can be implemented.

Images