High-efficiency non-destructive lithium deposition detection method and system for batteries

Abstract

The present application relates to the field of new energy batteries, and discloses an efficient and non-destructive lithium deposition detection method and system for batteries. The detection method includes the following steps: S1. Real-time acquisition of battery working data, including internal resistance, temperature, signal strength, voltage and current of the battery, through multiple sensors; S2. De-noising and standardization of the acquired data, and time alignment of the data to ensure data consistency and accuracy; S3. Based on the influence of the battery lithium deposition process on the propagation of high-frequency signals, an attenuation model of high-frequency signals is established, which describes the relationship between the conductivity of the battery and the signal attenuation coefficient. The present invention adopts a battery lithium deposition detection technology based on a signal attenuation model and multi-dimensional variable coupling analysis, which achieves the effect of real-time detection of battery lithium deposition under the premise of high precision, effectively improves the accuracy and stability of the detection, and avoids misjudgment or missed judgment caused by a single data source in traditional solutions.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy batteries, and in particular to a high-efficiency non-destructive lithium deposition detection method and system for batteries. Background Art

[0002] Existing battery management systems rely mainly on basic battery parameters such as internal resistance, current and voltage to assess the battery's state. Typically, internal resistance sensors, temperature sensors, and current and voltage measuring devices are used for monitoring. These sensors provide basic data about the battery's operating status through simple measurements. Increases in internal resistance are usually related to battery aging, capacity decay, or lithium deposition, while temperature changes are closely related to heat accumulation during the battery's charge and discharge process. Fluctuations in current and voltage directly reflect changes in battery load. Traditional systems usually use this data combined with simple algorithms to assess the battery's health and make necessary adjustments. However, this traditional monitoring method has certain limitations, especially in complex working environments, where it is difficult to accurately identify the occurrence of lithium deposition.

[0003] Most existing battery monitoring technologies rely on the measurement of a single sensor and fail to fully consider the complexity of the battery status. Traditional solutions only infer the occurrence of lithium plating through single parameters such as internal resistance, temperature, or current and voltage. However, lithium plating not only affects internal resistance and temperature, but is also closely related to multi-dimensional parameters such as signal attenuation and conductivity. Existing technologies often fail to comprehensively consider the interactions between these variables, resulting in the inability to detect this phenomenon in the early stages of lithium plating, and are prone to misjudgment or missed judgments. In addition, the existing system lacks adaptability and real-time feedback mechanisms. Therefore, when faced with a complex battery working environment, it is impossible to dynamically adjust the alarm threshold, or accurately monitor and quickly respond to the battery status according to environmental changes. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a method and system for detecting lithium plating of batteries with high efficiency and non-destructiveness, which solves the problems of misjudgment and missed judgment caused by single sensor monitoring in the prior art, as well as monitoring failure or delayed response caused by the inability to adapt to the complex battery working environment.

[0005] To achieve the above objectives, the present invention is implemented by the following technical scheme: a highly efficient and non-destructive lithium deposition detection method for a battery comprises the following steps:

[0006] S1. First, use a variety of sensors to collect battery data in real time. Including internal resistance sensor, temperature sensor, and high-frequency signal sensor. The internal resistance sensor measures the internal resistance of the battery in real time, and the temperature sensor measures the temperature change when the battery is working. The high-frequency signal sensor is used to measure the attenuation of the signal propagating inside the battery;

[0007] These sensor data are collected and processed through sampling circuits and data acquisition modules. The acquisition frequency is high, ensuring that rapid changes in battery operation can be captured. The collected data include internal resistance, current, voltage, and signal strength parameters. These data provide a complete view of the current working status of the battery and can provide accurate basic data for subsequent analysis.

[0008] S2. The collected data often contains noise and interference signals. Therefore, the data must be preprocessed. First, remove low-frequency noise and filter out some useless interference signals. This step ensures the accuracy of subsequent data analysis. Then, standardize the data and convert the data collected by different sensors into the same standard unit to ensure data consistency and facilitate subsequent algorithm analysis;

[0009] The timing alignment of data is also very important because there are time differences in data collection from different sensors. Through the timing alignment process, the synchronization of data is ensured so that correct results can be obtained during the analysis process. Finally, the processed data will enter the model analysis module.

[0010] S3, signal attenuation model is the core part of this method. According to the characteristics of lithium deposition in the battery, the propagation of the signal in the battery will be affected by the change of battery conductivity. Lithium deposition will cause the conductivity of the local area of ​​the battery to decrease, and this change will increase the attenuation of the signal;

[0011] The mathematical model of signal attenuation is: in: Indicates at time The signal strength (unit: A), is the attenuation coefficient (unit: s -1 ), represents the decay rate of the signal, is time (unit: s); the establishment of the signal attenuation model needs to consider multiple factors, and the relationship between conductivity and the battery lithium deposition process needs to be further verified and optimized through experimental data. Depending on the type of battery, the model will be different, so the parameters of the model will be adjusted according to the actual situation during implementation.

[0012] S4. Lithium deposition in batteries is not only caused by changes in conductivity, but is also closely related to changes in the internal resistance and temperature of the battery. Therefore, it is necessary to build a multi-dimensional variable coupling analysis model. This model combines multiple factors such as internal resistance, temperature, and signal strength, and can more accurately identify lithium deposition phenomena;

[0013] The core idea of ​​this model is to infer the timing and location of lithium deposition through changes in the battery's internal resistance, temperature, and high-frequency signal attenuation. In the multi-dimensional variable coupling analysis, the battery's internal resistance and temperature are used as input variables, and the relationship with the signal strength is modeled to obtain a dynamic prediction of the lithium deposition phenomenon;

[0014] The increase in battery internal resistance is often related to the lithium deposition process, and the increase in temperature will also accelerate the lithium deposition process. By monitoring the internal resistance and temperature changes in real time and combining them with signal attenuation, the occurrence of lithium deposition can be more accurately inferred.

[0015] S5. In order to optimize the performance of the entire detection system, a particle swarm optimization algorithm is used. This algorithm balances detection accuracy, real-time performance and cost through multi-objective optimization. Specifically, the optimization objective function is: in: is the detection error, which indicates the probability of misjudgment and missed judgment during the battery lithium deposition detection process (error rate of detecting lithium deposition); The response delay refers to the time interval from signal acquisition to the system making a decision; The operating cost of the system includes hardware equipment cost, computing resource consumption, and algorithm complexity; , , is the weight in the objective function, indicating the relative importance of detection accuracy, response time, and cost;

[0016] By adjusting each particle in the particle swarm, the particle swarm optimization algorithm can find the best parameter combination to improve the overall performance of the system;

[0017] The particle swarm optimization algorithm finds the global optimal solution by simulating the foraging behavior of a flock of birds. Each particle represents a solution, and particles work together to search for the optimal solution. This process can effectively avoid falling into the local optimum and ensure that the system can maintain a high accuracy under different working conditions.

[0018] S6. Based on the optimized model, the real-time monitoring system will continuously analyze various parameters of the battery, such as internal resistance, temperature, and signal strength. When lithium deposition is detected, the system will immediately issue an alarm signal and provide corresponding maintenance suggestions to the battery management system. Maintenance suggestions may include stopping charging, discharging, deactivating or replacing battery operations;

[0019] Through the real-time feedback mechanism, the battery management system can take measures in the first time to avoid safety problems caused by lithium plating. In addition, real-time monitoring can also help detect the health of the battery and extend the battery life.

[0020] Preferably, the high-frequency signal attenuation model calculates the attenuation coefficient of the signal based on the change in battery conductivity, and uses the attenuation coefficient to describe the occurrence of lithium plating. Specifically, the conductivity inside the battery will change as lithium plating proceeds, and the conductivity of the local lithium plating area will decrease, resulting in an increase in the resistance of the area. The signal will experience stronger attenuation when passing through these areas. The attenuation coefficient can be used to quantify this signal attenuation phenomenon, which is closely related to the change in battery conductivity. The attenuation formula of the model is: in:

[0021] It is at the moment Signal attenuation coefficient (unit: s -1 ),

[0022] is the base attenuation coefficient of the battery under normal conditions,

[0023] It is the attenuation increment caused by the change of conductivity in the local lithium deposition area of ​​the battery. is the conductivity of the local area (unit: S / m);

[0024] This attenuation coefficient directly affects the signal strength of the battery, thus reflecting the occurrence of lithium deposition. When the conductivity in the battery decreases due to lithium deposition, the attenuation coefficient increases, resulting in a faster signal attenuation rate. By monitoring changes in signal attenuation, the system can detect the occurrence of lithium deposition in advance and take corresponding battery management measures accordingly.

[0025] Preferably, the multi-dimensional variable coupling analysis model combines the dynamic coupling of the battery's internal resistance, temperature and signal strength, aiming to predict the occurrence of lithium precipitation and its impact on the overall performance of the battery through the interaction of these key parameters. Specifically, the relationship between internal resistance, temperature and signal strength is described by a dynamic equation. The model takes into account the mutual influence of each variable and controls the input (battery charge and discharge current) to predict the occurrence of lithium precipitation and its impact on the overall performance of the battery. ) to solve. The internal resistance of the battery Affected by the lithium deposition phenomenon, the temperature The signal strength will change as the heat inside the battery accumulates. By solving these dynamic equations, we can get the prediction results of lithium plating.

[0026] Preferably, the multi-objective optimization algorithm adopts a particle swarm optimization algorithm, and performs a balanced optimization on detection accuracy, real-time performance and cost according to the weight in the objective function, wherein the objective function is: in, represents the detection error, Indicates detection delay, The operating cost of the system;

[0027] Target Aims to minimize detection errors. The existence of misjudgment and missed detection will directly affect the performance of the battery management system. Misjudgment means that the system incorrectly determines that the battery has lithium deposition, while missed detection means that the occurrence of lithium deposition is not detected in time. Therefore, optimizing detection errors is the key to improving battery safety and service life.

[0028] Preferably, the signal processing part is based on the nonlinear relationship between the high-frequency signal intensity and the battery conductivity, and uses this relationship to infer the severity of the lithium deposition phenomenon. Specifically, the lithium deposition phenomenon causes changes in the internal conductivity of the battery, which in turn affects the propagation characteristics of the high-frequency signal. When lithium deposition occurs in the battery, the conductivity of the local area will decrease, resulting in increased signal attenuation. Therefore, the increase in signal attenuation can be used as an indication of battery lithium deposition.

[0029] There is a nonlinear relationship between the change in signal intensity and conductivity. The decrease in conductivity inside the battery will lead to a signal attenuation coefficient. By monitoring the change in this attenuation coefficient, the system can infer the occurrence and development of lithium plating, thereby providing accurate early warning information for the battery management system.

[0030] Preferably, the step includes real-time detection of the working state of the battery, and judging whether lithium deposition occurs by monitoring the key factors of internal resistance, temperature change, signal attenuation and current. The internal resistance and temperature of the battery are important indicator parameters for the occurrence of lithium deposition. The increase of internal resistance and the increase of temperature usually indicate that an abnormal reaction has occurred inside the battery. At the same time, the change of signal attenuation and current fluctuation are also closely related to the lithium deposition phenomenon. When the internal resistance of the battery is ,temperature and signal attenuation coefficient When the set threshold is exceeded, the system can determine the occurrence of lithium precipitation through real-time calculation;

[0031] The system will comprehensively consider the battery's internal resistance and temperature change trends, combined with the calculation results of signal strength attenuation. If the changes in these parameters exceed the preset safety threshold, the system will trigger an alarm signal to remind the battery of lithium deposition. The alarm system is triggered based on the following conditions: in:

[0032] is the change in internal resistance, is the safety threshold of internal resistance,

[0033] is the temperature change, is the safety threshold of temperature,

[0034] is the signal attenuation coefficient, is the threshold value of the attenuation coefficient;

[0035] When the changes in these parameters exceed the safe range, the system will immediately send an alarm message to the battery management system and provide specific maintenance suggestions. Maintenance suggestions include immediately stopping charging, stopping discharging, deactivating the battery, or replacing the battery. Through this real-time monitoring and feedback mechanism, the system can take timely measures before lithium plating occurs, thereby effectively avoiding safety hazards caused by lithium plating.

[0036] A highly efficient and non-destructive lithium deposition detection system for a battery based on the above method comprises:

[0037] Multiple sensors are used to collect data on the battery's internal resistance, temperature, current, voltage, and signal strength in real time; these data provide basic input for subsequent analysis, ensuring that the system can fully understand the battery's operating status. The internal resistance sensor is used to detect the battery's internal resistance, the temperature sensor monitors the battery's operating temperature, the current and voltage sensors record the battery's charge and discharge conditions, and the signal strength sensor focuses on the attenuation of the battery's internal signal;

[0038] The data processing unit is used to denoise, standardize, and time-align the collected signal data; the denoising process removes external interference and sensor errors in the data, and the standardization process ensures the consistency of data from different sensors and the accuracy of subsequent processing and analysis. The time alignment process ensures the synchronization of data from different sensors in time and ensures the accuracy of the data;

[0039] The signal processing unit is used to process high-frequency signal data, calculate the signal attenuation caused by the change in conductivity, and infer the lithium deposition phenomenon according to the influence of the battery lithium deposition phenomenon on signal attenuation; the change in the signal attenuation coefficient is closely related to the change in battery conductivity. The signal processing unit infers the occurrence of the lithium deposition phenomenon based on this change and provides data support for subsequent analysis;

[0040] The model optimization unit is used to optimize parameters by using a multi-objective optimization algorithm through multi-dimensional variable coupling analysis, combined with data on battery internal resistance, temperature, and signal strength, so as to estimate the occurrence of lithium precipitation. This unit uses the optimization algorithm to ensure that the system can achieve the best balance between detection accuracy, real-time performance, and operating costs, so as to accurately estimate the occurrence of lithium precipitation and provide early warnings based on changing trends.

[0041] The control and decision-making unit is used to monitor the lithium deposition status of the battery in real time according to the optimized model, and provide alarm signals and battery management suggestions when lithium deposition occurs. When lithium deposition occurs, the system will trigger an alarm signal and provide battery management suggestions, such as limiting the battery charge and discharge rate, deactivating the battery, or performing maintenance operations. Through this feedback mechanism, the system can take timely measures before lithium deposition causes greater damage, effectively ensuring the safety of the battery and extending the battery life.

[0042] Preferably, the signal processing unit calculates the signal attenuation coefficient based on the nonlinear relationship between the change in conductivity and signal attenuation in the battery, thereby inferring the occurrence of lithium deposition. When the battery is working normally, the attenuation rate of the signal remains stable, but with the occurrence of lithium deposition, the local conductivity of the battery will decrease, resulting in accelerated attenuation of the signal during propagation. As the lithium deposition process progresses, the local conductivity of the battery decreases, resulting in an increase in the signal attenuation coefficient. By calculating this attenuation coefficient in real time, the system can accurately track the occurrence of lithium deposition inside the battery and infer the severity of lithium deposition based on the degree of change in the attenuation rate. The signal processing unit uses the attenuation coefficient as an important indicator, combined with other battery parameters, to infer the spatial distribution of lithium deposition and its impact on battery performance.

[0043] Preferably, the model optimization unit adopts a particle swarm optimization algorithm to adjust system parameters by means of multi-objective optimization to achieve the best balance between detection accuracy, real-time performance and system cost. The particle swarm optimization algorithm simulates group behavior, finds the optimal solution, and effectively balances multiple objectives. In the present invention, the optimization objectives of the PSO algorithm include detection error, response delay and system operating cost. Through the particle swarm optimization algorithm, the system can control the cost of the system while ensuring high accuracy and low latency, so that battery lithium deposition detection can operate efficiently in different application environments. During the optimization process, the algorithm continuously adjusts the position and speed of the particles to find the best combination of parameters, and finally optimizes the system performance, ensures the accuracy and response speed of detection, and reduces resource consumption.

[0044] Preferably, the control and decision unit determines whether lithium deposition occurs in the battery by analyzing the internal resistance, temperature and signal attenuation data of the battery in real time. ,temperature and signal attenuation coefficient When the set threshold is exceeded, the control and decision-making unit will immediately activate the alarm mechanism to indicate the lithium deposition problem in the battery. At this time, the system will not only send out an alarm signal, but also provide specific maintenance suggestions based on the current working status of the battery.

[0045] If the battery internal resistance Increase to exceed the safety threshold ,temperature Exceeding the preset range , or the signal attenuation factor If abnormal changes are shown, the system will determine the occurrence of lithium plating and send an alarm signal to the battery management system to notify the operator to take timely measures.

[0046] The present invention provides a highly efficient and non-destructive lithium deposition detection method and system for batteries. It has the following beneficial effects: 1. The present invention adopts a battery lithium deposition detection technology based on a signal attenuation model and a multi-dimensional variable coupling analysis, achieving the effect of real-time detection of battery lithium deposition under the premise of high precision. Compared with the detection scheme relying on a single sensor in the prior art, the present invention effectively improves the accuracy and stability of detection by comprehensively considering multiple variables such as the internal resistance, temperature and signal strength of the battery, and avoids misjudgment or missed judgment caused by a single data source in the traditional scheme.

[0047] 2. The present invention uses a particle swarm optimization algorithm for multi-objective optimization, achieving the best balance between accuracy, real-time performance and cost. Compared with the single optimization target algorithm commonly used in the prior art, the present invention can simultaneously optimize detection accuracy, reduce response delay and reduce system cost, greatly improving the overall performance of the system and making it more adaptable to different battery types and working environments.

[0048] 3. The present invention automatically triggers the early warning and response measures of the battery management system through real-time monitoring and feedback mechanism, so as to achieve the effect of taking safety measures in time when lithium plating occurs. Compared with the detection scheme in the prior art that cannot respond in real time, the present invention can automatically start safety measures in the first time through real-time monitoring of signal attenuation and battery status changes, thereby reducing the safety risks of the battery caused by lithium plating.

[0049] 4. The present invention provides an efficient system architecture that can non-destructively detect lithium deposition and optimize battery management, achieving the effect of effectively monitoring lithium deposition without interfering with the normal operation of the battery. Compared with the prior art solution that requires destructive testing of the battery, the present invention uses non-destructive testing technology, which greatly reduces the impact on the battery, while ensuring the normal operation and management of the battery and improving the battery life. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 One of the schematic flow charts of the method of the present invention;

[0051] Figure 2 This is the second schematic flow chart of the method of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] Please refer to the attached Figure 1 -Attached Figure 2 The embodiment of the present invention provides a highly efficient and non-destructive lithium deposition detection method for a battery, comprising the following steps:

[0054] S1: Data collection;

[0055] First, in order to ensure accurate nondestructive detection of battery lithium deposition, real-time monitoring of the battery working state is crucial. The data acquisition step is the basis of this method, covering the acquisition of multiple working state parameters of the battery through a variety of sensors. The data collected by these sensors provide necessary inputs for signal processing, model analysis and optimization algorithms in subsequent steps. In this embodiment, multiple sensors are used, including internal resistance sensors, temperature sensors, current and voltage sensors, and high-frequency signal sensors, through which the electrical parameters and physical parameters of the battery under different working conditions are collected.

[0056] In this embodiment, the internal resistance sensor is used to monitor the internal resistance value of the battery in real time. The change in internal resistance reflects the change in conductivity during the lithium deposition process of the battery. Lithium deposition will cause the local conductivity of the battery to decrease, thereby increasing the internal resistance. Therefore, the collection of internal resistance data is crucial for identifying the lithium deposition phenomenon. Generally, the increase in internal resistance can be obtained by methods such as AC impedance measurement or DC current source measurement. These data provide important inputs for subsequent signal attenuation analysis and multidimensional variable coupling analysis.

[0057] As an option, a temperature sensor is used to monitor the temperature changes of the battery. The increase in temperature is usually an important feature of the battery lithium deposition process, especially when the battery load is high, local overheating areas are prone to lithium deposition. Therefore, the temperature change data will be used to infer the occurrence and development of lithium deposition. By installing multiple temperature sensors, the temperature changes at different locations of the battery can be monitored to further improve the accuracy of the temperature data. Temperature data not only provides an important basis for model analysis, but also helps to monitor the battery status in real time and prevent safety problems caused by overheating.

[0058] Specifically, the high-frequency signal sensor is used to measure the attenuation of the high-frequency signal in the battery. During the charge and discharge process, changes in the conductivity in the battery will cause changes in the propagation speed and attenuation of the signal. The lithium plating process usually leads to a decrease in local conductivity, and this change can be monitored by the attenuation of the high-frequency signal. Signal attenuation is positively correlated with changes in the conductivity of the battery. Therefore, by monitoring the intensity changes of the high-frequency signal, an early warning signal of lithium plating can be obtained. This step is crucial for real-time detection of battery lithium plating, especially during battery operation, where changes in signal attenuation can provide a direct basis for timely detection of lithium plating.

[0059] In some embodiments, the data acquisition unit uses high-frequency sampling technology, and the sampling frequency is set to hundreds of hertz to ensure that the rapidly changing battery parameters can be accurately captured during the charging and discharging process. Through high-speed sampling and signal processing, the data acquisition unit can collect parameters such as internal resistance, temperature, voltage, current and signal strength within a time interval of milliseconds to ensure the accuracy and real-time nature of the data.

[0060] In one possible implementation, the data acquisition system transmits the collected data to the data processing unit in real time via wireless or wired means. During the data transmission process, a reliable data transmission protocol is used to ensure that the data will not be lost or interfered with during the transmission process. The transmission protocol ensures high accuracy and timeliness of the data, ensuring that the entire detection system can respond in the shortest possible time.

[0061] In this embodiment, the real-time and accuracy of data acquisition are the key to the performance of the entire system. The collected data contains information in multiple dimensions such as internal resistance, current, voltage, signal strength, temperature, etc., which reflects the current state of the battery. By fusing the data from different sensors, accurate data input can be provided for subsequent steps (establishment of signal attenuation model, calculation of multi-dimensional variable coupling analysis model, etc.). These data will be preliminarily processed by the data acquisition unit, including denoising, calibration and other operations, to ensure that the data has high accuracy in subsequent analysis.

[0062] Specifically, the internal resistance data collected , Temperature data and signal strength data The data will be recorded in the form of time series. The time series can be marked at the millisecond level to ensure that the subsequent real-time monitoring and data processing can be synchronized with the battery status. Time synchronization is especially important for the monitoring of high-frequency signals to avoid inconsistent data due to time delays.

[0063] In one possible embodiment, the internal resistance data It can be measured by the small signal AC method, using a constant current source to provide a weak current signal to the battery, measuring its voltage response and calculating the internal resistance. It can be obtained by the thermistor response of the temperature sensor. The high-frequency signal generator and receiver work together to ensure that the signal will not be distorted during propagation inside the battery.

[0064] In this embodiment, by ,temperature The comprehensive collection of current and voltage data and high-frequency signal attenuation data provides a full range of inputs for subsequent signal processing, analysis modeling and optimization algorithms. Accurate battery status data can provide reliable data support for subsequent model analysis, lithium plating prediction and real-time monitoring, ultimately achieving efficient detection of battery lithium plating.

[0065] In summary, the data collection step of this embodiment comprehensively and accurately monitors multiple parameters of the battery through the collaborative work of multiple sensors, ensuring the high timeliness and high precision of data collection. These real-time and accurate data collection provides a solid foundation for signal processing, analysis models, and optimization algorithms in subsequent steps, ensuring the effectiveness and accuracy of the entire lithium precipitation detection method.

[0066] S2: data preprocessing;

[0067] In the data acquisition step, the sensor obtains the working status data of the battery, but this data usually contains noise, errors or inconsistent parts. Therefore, in order to ensure the effectiveness of the data in subsequent analysis and processing, data preprocessing must be performed. The main purpose of the data preprocessing link is to improve the accuracy of the data, eliminate interference, and unify the data of different sensors in terms of dimensions, so as to provide reliable data input for subsequent analysis models and optimization algorithms. In this embodiment, data preprocessing includes denoising, standardization, and time series alignment steps.

[0068] First of all, denoising is the first step in data preprocessing. Data noise mainly comes from electrical noise when the battery is working, sensor errors, and environmental interference. These noises will affect the accuracy of the data, thereby affecting the calculation results of the subsequent model. Denoising usually uses filtering technology. Generally speaking, low-pass filters can effectively remove high-frequency noise, while high-pass filters can remove low-frequency interference. Common denoising methods include Kalman filtering, mean filtering, wavelet transform, etc. It is crucial to choose a suitable denoising method according to the actual situation.

[0069] In this embodiment, Kalman filtering is selected for signal denoising. Kalman filtering is an optimal estimation method that can minimize the estimation error and remove noise based on the weighted average of the measured value and the predicted value of the system state. The basic formula of Kalman filtering is as follows: in: is the estimated state value (battery internal resistance, temperature or signal strength), is the prior estimate, is the Kalman gain, which represents the weight of the filter. is the actual measured value (battery voltage or current), is the state transfer matrix, which is used to convert the state vector into the measurement value.

[0070] Kalman Gain

[0071] The calculation formula is as follows: in: is the prior error covariance,

[0072] is the covariance matrix of the measurement noise.

[0073] In this way, Kalman filtering can optimize signal estimation based on the known battery operating state and the statistical characteristics of the noise, thereby effectively removing noise.

[0074] As an option, for some special cases of signals or battery working conditions, wavelet transform can also be used for denoising. Wavelet transform can decompose the signal and effectively distinguish low-frequency and high-frequency components. By selecting a suitable threshold, the signal is denoised, thereby achieving more sophisticated signal processing.

[0075] In the next step, standardization unifies all collected multidimensional data into a common scale. Since the working state of the battery involves multiple sensors, the data collected by each sensor has different units and dimensions. The unit of current is ampere (A), the unit of voltage is volt (V), and the temperature is expressed in degrees Celsius (℃). Direct comparison of data in different units will lead to errors, so standardization is required.

[0076] In this embodiment, the step of standardization includes converting the data of each sensor to zero mean and unit variance. Specifically, the mean of each sensor data is first calculated.

[0077] and standard deviation, and then transform each data point as follows: in: For the Data points from sensors, and Respectively The mean and standard deviation of the sensor data, is the standardized data.

[0078] Through standardization, the data of all sensors will be converted to data with a mean of 0 and a variance of 1, so that different data sources can be fairly compared and processed. The standardized data is convenient for subsequent model analysis, signal processing and optimization algorithm calculation, avoiding deviations caused by different dimensions.

[0079] Specifically, in the process of standardization, the data of all sensors are represented at the same scale, which is very important for model training and data fusion. Different working states of the battery have different effects on each variable. Standardization can eliminate this difference and ensure that different characteristics of the battery have the same importance in subsequent analysis.

[0080] In another possible implementation, timing alignment is an important step in data preprocessing to ensure the temporal consistency of data from different sensors. Since multiple sensors for data collection work in parallel, there is a slight deviation in their collection time. If this time deviation is not processed, it will lead to errors in subsequent data analysis. Therefore, timing alignment is to ensure that all data points can be processed under the same time reference.

[0081] In the process of timing alignment, interpolation methods are usually used. Linear interpolation is one of the most commonly used methods. It can minimize the time difference by interpolating each data point, thereby ensuring data synchronization. The linear interpolation formula is as follows: in: It is at the moment The interpolation result is and They are respectively and The corresponding known data points are and is the time point in the original data.

[0082] Through timing alignment, it can be ensured that the data collected by multiple sensors are synchronized, providing consistent data for subsequent fusion calculations and model analysis.

[0083] In summary, data preprocessing optimizes the quality of raw data through denoising, standardization, and time alignment, making the data more suitable for subsequent model analysis. Denoising effectively eliminates interference components in the signal, standardization ensures the uniform scale of data from different sensors, and time alignment ensures the synchronization of data in time. These steps provide high-quality input for subsequent signal attenuation modeling, multi-dimensional variable coupling analysis, and optimization algorithms, ensuring the efficiency and accuracy of the entire battery lithium plating detection system.

[0084] S3: Establishment of signal attenuation model;

[0085] In the data preprocessing stage, the accuracy and consistency of the battery working data have been ensured through steps such as denoising, standardization, and timing alignment. On this basis, a signal attenuation model needs to be established next to reveal the impact of the lithium plating process on signal propagation. The signal attenuation model is constructed based on the impact of the battery lithium plating phenomenon on conductivity. Specifically, lithium plating will cause changes in the local conductivity of the battery, and this change will directly affect the propagation speed and attenuation of the signal inside the battery. Therefore, the degree of signal attenuation can reflect the occurrence of lithium plating, and thus provide data support for subsequent lithium plating detection.

[0086] In this embodiment, the signal attenuation model is established based on the effect of the change in the conductivity of the battery on the attenuation of the high-frequency signal. It is a quantitative indicator of the conductivity of battery materials. Changes in conductivity are usually accompanied by the occurrence of lithium deposition. When the battery is working normally, the signal propagation speed and attenuation coefficient However, when lithium plating occurs, the conductivity of the local area of ​​the battery will decrease, resulting in accelerated signal attenuation. Therefore, the signal strength It will show different degrees of attenuation over time.

[0087] Specifically, the signal attenuation can be described by the following equation: in: Indicates at time The signal strength (unit: A), is the attenuation coefficient (unit: s -1 ), represents the decay rate of the signal, is the time (unit: s).

[0088] The attenuation coefficient is closely related to the change in the conductivity of the battery. 0 is a fixed value. However, when lithium deposition begins, the local conductivity decreases and the attenuation coefficient Therefore, the attenuation coefficient It can be expressed as: in: It is the base attenuation coefficient when the battery has no lithium deposition. is the attenuation increment caused by lithium deposition, is the local conductivity of the battery (unit: S / m).

[0089] In some embodiments, the attenuation increment can be further refined to take into account the different effects of different stages of lithium precipitation on attenuation. For example, in the early stages of lithium precipitation, the decrease in conductivity is relatively slow, while in the later stages, the intensification of lithium precipitation causes a sharp decrease in conductivity, thereby accelerating the attenuation of the signal. Therefore, the attenuation increment can be dynamically adjusted according to the working state of the battery and the progress of lithium precipitation. Specifically, the attenuation increment can be modeled by the following nonlinear relationship: in: is a constant that indicates the sensitivity of conductivity changes. is an exponential factor used to adjust the effect of conductivity on the attenuation coefficient.

[0090] As an option, in order to further improve the accuracy of the model, the attenuation coefficient can simultaneously consider the effects of the battery's operating temperature and the charge and discharge current on signal attenuation. When the battery is in a high temperature environment or under a large current load, lithium precipitation tends to intensify, resulting in a more significant change in conductivity. At this point, the expression for the attenuation coefficient can be expanded to: in: is the local temperature of the battery (unit: °C), is the charge and discharge current of the battery (unit: A).

[0091] Specifically, the temperature The impact on battery lithium deposition is more significant. The increase in temperature will accelerate the lithium deposition process inside the battery. Therefore, adding temperature factors to the model can more accurately reflect the impact of lithium deposition on signal attenuation. It will also affect the lithium deposition process. A larger load current will cause local overheating of the battery and aggravate lithium deposition. Therefore, adding the current term can further improve the accuracy of the model.

[0092] In some embodiments, the signal attenuation model can be used to monitor the working status of the battery in real time, and predict the lithium deposition status of the battery based on the signal strength data collected in real time. When the signal attenuation of the battery exceeds the preset threshold, the system can detect the occurrence of lithium deposition in time and trigger the alarm mechanism. By comparing the signal attenuation data with the attenuation model, the system can accurately determine the time and area of ​​lithium deposition, thereby providing a warning signal for the battery management system to ensure safe operation of the battery.

[0093] In summary, the establishment of the signal attenuation model provides a theoretical basis and technical support for battery lithium plating detection. By combining multiple factors such as battery conductivity, temperature, and charge and discharge current, the attenuation model can accurately describe the impact of lithium plating on signal propagation, thereby helping to achieve efficient and non-destructive battery lithium plating detection. With the continuous optimization of model parameters, the system can more accurately predict the occurrence and development of lithium plating, providing strong support for the safety monitoring of battery management systems.

[0094] S4: Multi-objective optimization algorithm;

[0095] In the aforementioned steps, a solid foundation has been laid for battery lithium plating detection through data acquisition, preprocessing and the establishment of signal attenuation models. Next, it is necessary to further improve the performance of the system through a multi-objective optimization algorithm. The purpose of multi-objective optimization is to find an optimal balance between multiple objectives. Specifically, the optimization objectives in this embodiment include: detection accuracy, real-time performance and operating costs. In order to achieve this goal, this embodiment uses a particle swarm optimization algorithm (PSO) for multi-objective optimization. The PSO algorithm can achieve an effective balance between multiple objectives, thereby obtaining a most suitable solution.

[0096] In this embodiment, the particle swarm optimization algorithm searches by simulating group behavior (bird flocks foraging), and each particle represents a solution. The current position and speed of the particle determine the state parameters of the system (signal processing parameters, model parameters, etc.). Through multiple iterations, the particle swarm continuously adjusts its position and eventually converges to the optimal solution. Specifically, the goal of particle swarm optimization is to simultaneously minimize three optimization objectives, namely, detection error , response delay and system cost .

[0097] In general, multi-objective optimization algorithms require a trade-off between multiple objectives, which are often contradictory. For example, when improving detection accuracy, the response time of the system will increase, while reducing the response time will reduce the detection accuracy of the system. Therefore, by reasonably designing the objective function, the overall performance of the system can be optimized while meeting different requirements.

[0098] In this embodiment, the present invention integrates the optimization objectives in the form of weighted sum, and the objective function is as follows: in: is the detection error, which indicates the probability of misjudgment and missed judgment during the battery lithium deposition detection process (error rate of detecting lithium deposition); The response delay refers to the time interval from signal acquisition to the system making a decision; The operating cost of the system, including hardware equipment cost, computing resource consumption, algorithm complexity, etc. , , is the weight in the objective function, indicating the relative importance of detection accuracy, response time, and cost.

[0099] Specifically, the goal is to minimize detection errors. The existence of false positives and false negatives will directly affect the performance of the battery management system. False positives refer to the system's incorrect judgment that lithium deposition has occurred in the battery, while false positives refer to the failure to detect the occurrence of lithium deposition in a timely manner. Therefore, optimizing detection errors is the key to improving battery safety and service life.

[0100] As an option, respond with a delay Aims to reduce the time it takes for the system to make decisions. Since the battery changes rapidly during the charging and discharging process, the system needs to be able to respond quickly to detect lithium plating in time and take appropriate safety measures. Optimizing response delay can ensure that the system can make decisions in time when the battery status changes, avoiding battery damage caused by delayed response.

[0101] In one possible implementation, the system cost It is the total cost of hardware, computing resources and algorithm optimization. The purpose of cost optimization is to balance the high-performance system and the economy in practical applications. Generally, by selecting appropriate hardware and optimization algorithms, the system cost can be reduced while ensuring that the system's performance in terms of accuracy and real-time performance is not affected.

[0102] In this embodiment, the particle swarm optimization algorithm is implemented using the following formula to update the position and velocity of particles: in: For the The velocity of a particle, which indicates the speed at which the particle moves in the search space; For the The position of a particle represents the current solution of the particle, that is, the parameters of the system (signal processing parameters, model parameters, etc.); For the The historical optimal position of a particle indicates the optimal solution found by the particle during the search process; is the global optimal position, indicating the optimal solution found among all particles; is the inertia weight, which controls the inertia of particles and affects the stability of the particle search process; and are acceleration constants, which control the degree to which the particle approaches the historical optimal position and the global optimal position respectively; and is a random number used to increase the randomness of the search process.

[0103] As another option, during the particle swarm optimization process, the parameters , , To improve the search efficiency of the algorithm. In some embodiments, the parameter As the number of iterations decreases, the particles have greater exploration capabilities in the initial search, and can find the optimal solution more precisely in the later convergence. and It can also be adjusted dynamically based on feedback during the optimization process to further improve the convergence speed.

[0104] In this embodiment, the particle swarm optimization algorithm will go through several iterations, and each iteration will update the system parameters according to the current particle position and speed. Through continuous optimization and updating, the particle swarm will eventually find a set of optimal parameters to achieve the best balance between detection accuracy, response delay and cost.

[0105] In summary, the particle swarm optimization algorithm can find the optimal balance between multiple objectives by simulating group behavior. By adjusting the weights between detection accuracy, response delay and cost, the particle swarm optimization algorithm can effectively improve the overall performance of the system. The algorithm provides a flexible and efficient optimization framework that can adapt to different battery management needs and ensure that in practical applications, the battery lithium deposition detection system can balance multiple objectives and provide efficient, accurate and economical detection results.

[0106] S5: Multidimensional variable coupling analysis;

[0107] Based on data preprocessing and signal attenuation model, the next key step is multi-dimensional variable coupling analysis. By combining multiple battery state parameters (internal resistance, temperature and signal strength) for analysis, lithium plating can be monitored and predicted more comprehensively and accurately. Multi-dimensional variable coupling analysis not only helps understand the mechanism of battery lithium plating, but also captures the dynamic evolution of lithium plating by integrating multi-dimensional data, thereby achieving early warning and safety management.

[0108] In this embodiment, the present invention adopts a multi-dimensional coupling analysis model of internal resistance, temperature and signal strength. This model combines multiple important physical properties of the battery and establishes the relationship between them to infer the occurrence of lithium precipitation and its impact on battery performance. As lithium precipitation occurs, the temperature increases. The signal strength will fluctuate with the battery status. The system is directly affected by the change in battery conductivity. Through the dynamic changes of these three factors, the system can accurately detect the potential occurrence of lithium plating at an early stage.

[0109] In this multi-dimensional coupling analysis model, the internal resistance, temperature and signal strength of the battery are regarded as a dynamic coupling system, which affect each other and change over time. The present invention describes the relationship between these variables through a dynamic equation of a multi-dimensional system: in: is the internal resistance of the battery (unit: Ω), is the battery temperature (unit: °C), is the signal strength (unit: A), is the charge and discharge current of the battery (unit: A), is the system state transfer matrix, which is used to describe the coupling relationship between battery internal resistance, temperature and signal strength. is the control matrix, representing the charge and discharge current Impact on battery internal resistance, temperature and signal strength.

[0110] Specifically, the system state transfer matrix represents the interaction between the battery parameters. The internal resistance and temperature of the battery will affect the signal strength, and the signal strength will in turn affect the conductivity of the battery, thereby affecting the internal resistance and temperature. Therefore, the matrix The elements in the system need to be fitted according to the physical characteristics of the battery and the actual test data to ensure that the system can accurately reflect the changes in the battery state.

[0111] As an option, the control matrix It indicates the charge and discharge current Impact on battery status. When the battery is working, the charge and discharge current will not only cause the battery temperature to change, but also intensify or slow down the lithium deposition process. Therefore, the impact of current on the battery internal resistance and signal strength cannot be ignored. With this design, the system can better capture the impact of charge and discharge current on battery lithium deposition.

[0112] In some embodiments, in order to further improve the accuracy of the model, the system also considers the impact of other working conditions of the battery (battery operating frequency, load changes, etc.) on lithium deposition. These factors will cause nonlinear changes in the internal resistance, temperature and signal strength of the battery. Therefore, adding these parameters to the model can improve the system's ability to predict lithium deposition.

[0113] In this embodiment, by solving the above dynamic equations, the system can obtain the changes in the battery internal resistance, temperature and signal strength in real time, and deduce the occurrence and development process of lithium precipitation. and temperature When the preset threshold is exceeded, the system will And multi-dimensional coupling analysis results are used to determine the occurrence of lithium precipitation and issue an alarm signal.

[0114] As another option, in order to further improve the accuracy of multi-dimensional variable coupling analysis, the system can adaptively adjust the model through historical data. The changing patterns of the battery's internal resistance, temperature, and signal strength under different working conditions can be learned through historical data to optimize the matrix and The system can more accurately reflect the performance of the battery under different load and temperature conditions by adjusting the parameters of the system. Through this adaptive learning, the system can flexibly adjust according to the actual situation and improve the accuracy and real-time performance of lithium plating detection.

[0115] In summary, through the multi-dimensional variable coupling analysis model, this embodiment can integrate data from multiple dimensions such as the internal resistance, temperature, and signal strength of the battery to monitor the occurrence of lithium plating in real time. The model can accurately capture changes in the working state of the battery, thereby providing timely warnings for the battery management system. By optimizing parameters and combining historical data, the system can adapt to different battery types and working environments, and achieve efficient lithium plating detection and battery safety management.

[0116] S6: Real-time monitoring and feedback mechanism;

[0117] In the previous steps, the present invention has built a powerful framework for monitoring and detecting the lithium deposition phenomenon of batteries through data acquisition, preprocessing, signal attenuation model and multi-dimensional variable coupling analysis. The next key step is the real-time monitoring and feedback mechanism, which ensures that the system can respond in a timely manner when the working state of the battery changes. Through real-time monitoring, the system can detect the abnormality of the battery at the early stage of lithium deposition, and effectively intervene through the feedback mechanism to prevent uncontrollable safety problems of the battery.

[0118] In this embodiment, the real-time monitoring process relies on the aforementioned signal attenuation model and multi-dimensional variable coupling analysis model. ,temperature and signal strength The system can capture early signals of lithium precipitation in real time. When the changes in these signals exceed the set threshold, the system will trigger an alarm and take corresponding safety measures.

[0119] Generally, when performing real-time monitoring, the system needs to continuously track the battery's internal resistance, temperature, and signal strength. During the battery's charge and discharge process, changes in internal resistance and temperature are often accompanied by the occurrence of lithium deposition, while increased signal attenuation is a direct reflection of changes in conductivity. Therefore, by monitoring these three parameters, the system can identify signs of lithium deposition in advance and issue an alarm in a timely manner.

[0120] Specifically, the core of real-time monitoring is to compare the current state of the battery with the preset threshold. and temperature Reaching the preset safety threshold and The system will immediately activate the alarm mechanism. In addition, the system will monitor the attenuation trend of signal strength. If it shows an aggravated attenuation trend and exceeds the normal range, the system will also judge it as a warning signal of lithium plating.

[0121] In this embodiment, the real-time monitoring function of the system is calculated and judged by the following formula: in: is the battery internal resistance relative to the initial value The change in (unit: Ω), is the battery temperature relative to the initial value The change in (unit: °C), and The internal resistance and temperature of the battery are monitored in real time.

[0122] As an option, if the real-time monitored internal resistance change or temperature change Exceeding the preset safety threshold and , the system will issue an alarm signal to indicate the presence of lithium plating. These thresholds can be adjusted according to the battery type, usage environment, and charge and discharge status.

[0123] Specifically, in order to further improve the accuracy of real-time monitoring, the system will include the battery's signal attenuation into the analysis. According to the aforementioned signal attenuation model, when the battery's signal strength

[0124] When a clear attenuation trend is shown, the system will calculate the attenuation coefficient

[0125] And determine whether it exceeds the normal range: in: is the signal attenuation coefficient (unit: s -1 ), For the moment The signal strength (unit: A), is the rate of change of signal strength (unit: A / s).

[0126] By calculating the signal attenuation coefficient , the system can accurately determine whether the signal attenuation is abnormal and respond in time. , it means that lithium deposition has occurred in the battery, and the system will immediately issue a warning and initiate emergency measures.

[0127] In some embodiments, the system can automatically adjust the alarm threshold based on real-time monitoring results. For example, when the operating temperature of the battery

[0128] When the value is higher than a certain threshold, the system will automatically lower the threshold of internal resistance and signal attenuation, so that lithium deposition can be detected under lower internal resistance changes and smaller signal attenuation. In this way, the system can flexibly respond to changes in different batteries and working environments.

[0129] Alternatively, the system can also adjust the parameters of real-time monitoring by learning from historical data. By analyzing the internal resistance, temperature, and signal attenuation patterns in historical battery operation data, the system can better predict the occurrence of lithium plating and adjust the alarm threshold accordingly. This adaptive learning mechanism can continuously improve the accuracy and response speed of the system.

[0130] In this embodiment, real-time monitoring is not limited to the alarm function. The system also has a feedback mechanism. When the internal resistance or temperature of the battery exceeds the safety threshold, the system will issue an early warning to the battery management system through the feedback mechanism and recommend taking appropriate measures, such as stopping charging, limiting battery load, starting the battery cooling system, etc. The system will also provide detailed diagnostic information to help maintenance personnel analyze the problem and take appropriate repair measures.

[0131] As an option, the system can be linked with hardware facilities such as temperature control systems and battery cooling equipment. When the system detects lithium deposition, the temperature control system can automatically start to reduce the battery temperature to a safe range and slow down the process of lithium deposition. The cooling system can also adjust the operating temperature of the battery to delay the intensification of lithium deposition, thereby providing additional protection for the battery.

[0132] In summary, the real-time monitoring and feedback mechanism ensures early detection of battery lithium deposition and timely response. By real-time monitoring of battery internal resistance, temperature, signal strength and other parameters, the system can accurately determine the occurrence of lithium deposition and provide decision support for the battery management system. This mechanism not only improves battery safety, but also optimizes the battery life, providing a guarantee for the efficient operation of the battery management system.

[0133] High-efficiency and non-destructive lithium deposition detection system for batteries;

[0134] In the previous steps, the present invention has established a theoretical and algorithmic framework for battery lithium deposition detection through data acquisition, preprocessing, signal attenuation modeling, and multi-dimensional variable coupling analysis. The system integrates multiple sensors, signal processing units, model optimization units, and control and decision units to monitor the battery status in real time, accurately determine the lithium deposition phenomenon, and provide effective early warning and decision support.

[0135] In this embodiment, the system is composed of multiple key components, including a data acquisition module, a signal processing unit, a model optimization unit, and a control and decision unit. Each component works closely together to achieve efficient detection and management of battery lithium deposition.

[0136] First, the data acquisition module is responsible for acquiring data such as the battery's internal resistance, voltage, current, temperature, and signal strength from multiple sensors. These data provide real-time battery status information for subsequent analysis and decision-making. The sensor data is processed and transmitted in real time through the acquisition unit.

[0137] In general, the data acquisition module uses multiple high-precision sensors to ensure accurate monitoring of various working parameters of the battery. The internal resistance sensor measures the internal resistance of the battery, the temperature sensor monitors the local temperature of the battery, and the signal sensor captures the attenuation of the internal signal of the battery. This information is transmitted to the subsequent signal processing and analysis unit in real time through the high-speed transmission module.

[0138] As an option, data collection between sensors can be transmitted via wireless networks to ensure the flexibility and scalability of the system. In addition, the data collection system can automatically adjust the sampling frequency according to the working status of the battery and environmental conditions to ensure that sufficient data can be obtained in real time under different working modes.

[0139] Next, the signal processing unit receives the collected raw data and further processes the data. The main task of the signal processing unit is to remove noise, filter, and convert the data into a format suitable for further analysis. The parameters in the signal attenuation model and the multi-dimensional variable coupling analysis model are also updated at this stage.

[0140] Specifically, in the signal processing process, denoising and filtering are first performed to remove high-frequency noise and low-frequency interference in the data. Through methods such as Kalman filtering, the system can effectively reduce the impact of sensor errors on data accuracy. Then, after standardization, the data will be passed to the subsequent analysis module in a unified format.

[0141] After the data processing is completed, the signal processing unit will calculate the current state of the battery based on the signal attenuation model and multi-dimensional variable coupling analysis model established above. These models help the system determine the occurrence of lithium deposition and infer the severity and location of lithium deposition based on the model calculation results.

[0142] As another option, the signal processing unit is not only responsible for traditional data processing tasks, but can also be dynamically adjusted through adaptive algorithms. For example, the system can optimize and adjust the parameters of the signal attenuation model and the multi-dimensional variable coupling analysis model according to the battery's usage history and working environment. In this way, the system can more accurately detect lithium deposition in different types of batteries.

[0143] Next, the system optimizes the parameters in the signal processing and data analysis process through the model optimization unit. The goal of the model optimization unit is to improve the accuracy, real-time and reliability of the system. By using the particle swarm optimization (PSO) multi-objective optimization algorithm, the system is able to minimize latency and computational overhead while ensuring high accuracy.

[0144] Through the optimization algorithm, the system will adjust the weight parameters according to different needs. The optimization algorithm will iterate repeatedly and eventually find the best system parameters to achieve the best balance between detection accuracy, real-time performance and cost.

[0145] Finally, the control and decision-making unit makes a decision based on the calculation results of the model optimization unit. The control and decision-making unit monitors the status of the battery in real time by receiving data from the signal processing unit and the optimization module. If lithium plating is confirmed, the system will activate the alarm mechanism and provide battery maintenance suggestions based on the test results. For example, if the internal resistance and temperature exceed the preset thresholds, the system recommends deactivating the battery or starting the cooling system to reduce the temperature.

[0146] As an option, the control and decision unit can also adjust the operating conditions of the battery based on real-time feedback. If the battery is under high load, the system can automatically adjust the current and temperature to slow down the occurrence of lithium deposition. The decision results of the control unit are fed back to the battery management system through a feedback mechanism, providing real-time guidance to ensure battery safety.

[0147] In summary, the battery lithium deposition detection system in this embodiment completes real-time monitoring and analysis of the battery status through the collaborative work of multiple modules. Modules such as data acquisition, signal processing, model optimization, and control decision-making work closely together to achieve efficient detection and timely response to battery lithium deposition. Through optimization algorithms and dynamic adjustments, the system can adapt to different battery types and working environments, and provide high-precision, low-latency, and low-cost battery lithium deposition detection and management solutions.

[0148] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An efficient and non-destructive lithium deposition detection method for batteries, characterized in that: The following steps are involved: S1. Collect the working data of the battery in real time through multiple sensors, including the internal resistance, temperature, signal strength, voltage and current of the battery; S2. De-noise and standardize the collected data, and perform time-series alignment on the data to ensure data consistency and accuracy; S3. Based on the influence of battery lithium deposition on the propagation of high-frequency signals, an attenuation model of high-frequency signals is established. The model describes the relationship between the conductivity of the battery and the signal attenuation coefficient, where the signal attenuation coefficient is related to the change in local conductivity caused by battery lithium deposition. S4. Through multi-dimensional variable coupling analysis, combined with battery internal resistance, temperature, and signal strength data, a dynamic equation is constructed to infer the location and severity of lithium precipitation; S5. Optimize the objective function using a multi-objective optimization algorithm. The optimization objectives include detection accuracy, real-time performance, and cost factors. The particle swarm optimization algorithm is used for tuning. S6. Based on the optimized model, the lithium deposition status of the battery is monitored in real time, and when lithium deposition is detected, an alarm signal and battery management suggestions are provided, and further measures are taken to repair or replace the battery.

2. The high-efficiency non-destructive lithium deposition detection method for a battery according to claim 1, characterized in that: The high-frequency signal attenuation model includes calculating the attenuation coefficient of the signal based on the change in the conductivity of the battery, and describing the occurrence of lithium deposition through the attenuation coefficient. The change in conductivity is closely related to the increase in resistance of the local lithium deposition area of ​​the battery.

3. The high-efficiency non-destructive lithium deposition detection method for a battery according to claim 1, characterized in that: The multi-dimensional variable coupling analysis includes the dynamic coupling of internal resistance, temperature and signal strength. Specifically, the prediction result of battery lithium deposition is obtained by controlling the input solution. By calculating the coupling relationship between variables, the spatial distribution of the lithium deposition process and its impact on the overall performance of the battery are inferred.

4. The high-efficiency non-destructive lithium deposition detection method for a battery according to claim 1, characterized in that: The multi-objective optimization algorithm adopts the particle swarm optimization algorithm, and performs a balanced optimization on the detection accuracy, real-time performance and cost according to the weight in the objective function, where the objective function is: ， in, represents the detection error, Indicates detection delay, is the operating cost of the system, , , is the weight in the objective function, indicating the relative importance of detection accuracy, response time, and cost.

5. The high-efficiency non-destructive lithium deposition detection method for a battery according to claim 1, characterized in that: The high-frequency signal processing is based on the nonlinear relationship between the high-frequency signal strength and the battery conductivity. The severity of lithium plating is inferred by analyzing the signal attenuation, specifically by continuously monitoring the relationship between the change in signal strength and the conductivity during the battery charging and discharging process.

6. The high-efficiency non-destructive lithium deposition detection method for a battery according to claim 1, characterized in that: The steps include real-time detection of the battery's working status and analysis of the battery's internal resistance, temperature changes, signal attenuation and current factors. If lithium plating occurs, an alarm is triggered in real time and maintenance suggestions are sent to the battery management system, including deactivation or replacement of the battery.

7. A high-efficiency non-destructive lithium deposition detection system for batteries based on the method of claim 1, characterized in that: include: Multiple sensors for real-time data collection of the battery's internal resistance, temperature, current, voltage, and signal strength; A data processing unit, used for denoising and standardizing the collected signal data, and performing time sequence alignment; The signal processing unit is used to process high-frequency signal data according to the influence of battery lithium deposition on signal attenuation, calculate the signal attenuation caused by conductivity change, and infer the lithium deposition phenomenon; The model optimization unit is used to optimize parameters by using a multi-objective optimization algorithm through multi-dimensional variable coupling analysis, combined with data on battery internal resistance, temperature, and signal strength, so as to estimate the occurrence of lithium precipitation; The control and decision-making unit is used to monitor the lithium deposition status of the battery in real time according to the optimized model, and provide alarm signals and battery management suggestions when lithium deposition occurs.

8. The high-efficiency non-destructive lithium deposition detection system for batteries according to claim 7, characterized in that: The signal processing unit calculates the signal attenuation coefficient according to the nonlinear relationship between the signal attenuation and the battery conductivity, and then infers the occurrence of the lithium precipitation phenomenon.

9. The high-efficiency non-destructive lithium deposition detection system for batteries according to claim 7, characterized in that: The model optimization unit includes a particle swarm optimization algorithm, which adjusts system parameters through a multi-objective optimization method to optimize the balance between detection accuracy, real-time performance and system cost.

10. The high-efficiency non-destructive lithium deposition detection system for batteries according to claim 7, characterized in that: The control and decision unit is used to analyze the internal resistance, temperature, and signal attenuation data of the battery in real time, and provide real-time feedback if lithium plating occurs, including alarms, maintenance suggestions, and battery replacement measures.

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