Battery detection system and detection method

Abstract

The invention relates to the technical field of battery detection, in particular to a battery detection system and method. The method comprises the following steps: carrying out data acquisition on a battery operation state to obtain battery operation state data; performing voltage-current dynamic behavior capture on the battery operation state cleaning data to obtain voltage-current dynamic behavior data; performing line resistance fluctuation mapping according to the voltage-current behavior dynamic behavior data to obtain line resistance fluctuation mapping data; performing limit working condition response simulation according to the line resistance fluctuation mapping data to obtain line limit working condition response data; performing battery cycle attenuation gradient simulation according to the line limit working condition response data to obtain limit working condition battery cycle attenuation gradient data; and carrying out cycle life prediction on the battery cycle attenuation gradient data under the extreme working condition based on a logistic regression algorithm to obtain battery limit cycle life data. According to the invention, the battery detection technology is optimized, so that the battery detection technology is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of battery detection, and in particular to a battery detection system and a detection method. Background Art

[0002] Battery testing methods usually cover multiple aspects such as battery capacity testing, internal resistance testing, charging and discharging performance evaluation, and temperature monitoring. Traditional testing methods are mostly based on directly measuring battery voltage, current, temperature and other parameters, and using these data to infer the battery's health status. For example, the internal resistance test of a battery can help evaluate the efficiency and decay of its electrochemical reaction. Excessive internal resistance indicates that the battery's performance is declining or is about to fail. Capacity testing can reflect the remaining energy and service life of the battery. Reduced capacity often means that the battery has shown signs of aging or damage. On the other hand, with the development of intelligent and sophisticated technology, testing methods based on battery management systems (BMS) have gradually emerged. These systems can accurately evaluate the battery's health status through real-time monitoring of various battery parameters, and can warn of battery failures or dangers. Battery testing methods are not only used for battery quality control in industrial production, but are also widely used in the operation, maintenance and management of electric vehicles, power tools, energy storage systems and other fields. In the electric vehicle industry, with the growth of market demand and the continuous advancement of battery technology, the accuracy and technical requirements of battery testing are becoming increasingly higher. Battery monitoring can help car owners and operators achieve effective battery management, extend battery life, reduce failure rates, and avoid safety accidents. However, a traditional battery detection method has the problem of inaccurate identification of cycle attenuation under battery extreme conditions and inaccurate prediction of cycle life. Summary of the invention

[0003] Based on this, it is necessary to provide a battery detection system and a detection method to solve at least one of the above technical problems.

[0004] To achieve the above object, a battery detection method is provided, the method comprising the following steps:

[0005] Step S1: collecting data on the battery operation status to obtain battery operation status data; capturing the voltage-current dynamic behavior of the battery operation status cleaning data to obtain voltage-current dynamic behavior data;

[0006] Step S2: mapping the line resistance fluctuation according to the voltage-current behavior dynamic behavior data to obtain line resistance fluctuation mapping data; performing extreme operating condition response simulation according to the line resistance fluctuation mapping data to obtain line extreme operating condition response data;

[0007] Step S3: Perform battery cycle attenuation gradient simulation according to the line extreme operating condition response data to obtain extreme operating condition battery cycle attenuation gradient data; perform cycle life prediction on the extreme operating condition battery cycle attenuation gradient data based on a logistic regression algorithm to obtain battery extreme cycle life data.

[0008] The present invention first collects the operating status data of the battery through sensors and monitoring equipment, including key information such as the voltage, current, and temperature of the battery. These data reflect the performance and behavior of the battery during actual use. Next, the collected battery operating status data needs to be cleaned and preprocessed to remove noise and outliers to ensure the accuracy of the data. After data cleaning, the dynamic behavior of the voltage and current changes of the battery is captured to obtain the voltage-current (VI) dynamic behavior data of the battery. These data provide a basis for subsequent line resistance fluctuations and battery performance analysis. The voltage-current dynamic behavior data obtained in the first step is used to perform line resistance fluctuation mapping. During the operation of the battery, its internal line impedance will fluctuate with the load change. By modeling these fluctuation data, the fluctuation mapping data of the line resistance can be obtained, which provides an important basis for analyzing the current flow and power loss inside the battery. Further, according to the fluctuation of the line resistance, the response simulation under extreme conditions is performed, that is, the response characteristics of the line under extreme conditions (such as high temperature, high load, etc.). Through these simulations, the response data of the line under extreme conditions are obtained, thereby providing a simulation basis for the durability and extreme condition performance of the battery. The cycle attenuation gradient simulation of the battery is performed based on the line extreme operating condition response data obtained in step S2. During the long-term charging and discharging process, the performance of the battery will gradually decay due to the influence of line impedance and working conditions. By modeling and simulating the battery behavior under extreme working conditions, the attenuation gradient data of the battery under different cycle times can be obtained. These simulation data reflect the decay speed and capacity loss of the battery under extreme working conditions, and help predict the life change of the battery under extreme working conditions. Based on the above simulation results, the cycle attenuation gradient data of the battery is analyzed using a logistic regression algorithm to predict the cycle life of the battery. The logistic regression algorithm can effectively capture the relationship between the battery performance decay and the use environment, thereby predicting the actual cycle life of the battery under various extreme conditions, and ensuring the reliability and long-term stability of the battery in practical applications. Therefore, the present invention is an optimization process made to a traditional battery detection method, which solves the problem that a traditional battery detection method has inaccurate cycle attenuation recognition under the battery extreme working condition and inaccurate cycle life prediction, and improves the accuracy of cycle attenuation recognition under the battery extreme working condition and the accuracy of cycle life prediction.

[0009] Preferably, step S1 comprises the following steps:

[0010] Step S11: collecting data on the battery operating status to obtain battery operating status data;

[0011] Step S12: Cleaning the battery operation status data to obtain battery operation status cleansing data;

[0012] Step S13: Timestamp the battery operation status cleaning data to obtain battery operation status timestamp data;

[0013] Step S14: capturing the voltage-current dynamic behavior of the battery operation status cleaning data according to the battery operation status time stamp data to obtain voltage-current dynamic behavior data.

[0014] The present invention first uses appropriate sensors and measuring equipment to collect data on the real-time operating status of the battery. By collecting parameters such as the voltage, current, temperature, and number of charge and discharge times of the battery, the performance changes of the battery during actual use can be fully understood. These collected raw data provide the necessary basis for subsequent analysis of battery health, performance degradation, and life cycle prediction. Accurate data collection is a prerequisite for ensuring the accuracy and reliability of subsequent analysis and prediction results. The collected raw battery operating status data usually has noise, missing values, or outliers, and data cleaning is required to improve the data quality. In this process, the goal of data cleaning is to remove incomplete, erroneous, or invalid parts to ensure the accuracy and reliability of the data. For example, remove outliers caused by sensor failures, fill in missing data, unify time formats, etc. Through this processing, the obtained battery operating status cleaning data will be more accurate, providing a clean and accurate data basis for subsequent analysis. By timestamping the battery operating status cleaning data, it is ensured that each data point has a clear time identifier. This is to track the state changes of the battery at different time points, and then analyze the timing and change trend of battery performance. The introduction of timestamp tags not only allows data to have static numerical information, but also allows for dynamic analysis in combination with the time dimension, thereby revealing the performance fluctuations of the battery within a specific time period. This can provide more accurate time series data for battery performance optimization and life prediction. The voltage-current (VI) dynamic behavior is captured using the battery operating status data after timestamp marking. This process captures the dynamic response characteristics of the battery under various load conditions by analyzing the voltage and current changes of the battery at different time points. This data capture can not only reveal the instantaneous voltage-current behavior of the battery, but also reflect the battery's adaptation to load changes during long-term operation, providing in-depth information about the battery's health status. In this way, the dynamic behavior of the battery can be accurately captured, providing key data support for further performance evaluation, degradation analysis, and cycle life prediction.

[0015] Preferably, step S2 comprises the following steps:

[0016] Step S21: obtaining the internal circuit connection structure of the battery;

[0017] Step S22: mapping the line resistance fluctuation of the internal line connection structure of the battery according to the voltage-current behavior dynamic behavior data to obtain line resistance fluctuation mapping data;

[0018] Step S23: performing line fluctuation working condition stress assessment on the line resistance fluctuation mapping data to obtain line fluctuation working condition stress data;

[0019] Step S24: performing extreme operating condition response simulation on the line fluctuation operating condition stress data according to the voltage-current behavior dynamic behavior data and the line resistance fluctuation mapping data to obtain the line extreme operating condition response data.

[0020] The present invention obtains the circuit connection structure inside the battery by a detailed analysis of the internal structure of the battery. The battery usually contains multiple battery cells and connection lines, and the connection mode and structure of these lines will directly affect the performance and current distribution of the battery. Through physical modeling or the use of scanning technology (such as X-ray CT scanning, 3D modeling, etc.), the circuit layout and connection mode inside the battery can be accurately understood. The purpose of this step is to provide basic data and structural information for subsequent analysis of the current flow, voltage distribution and line impedance changes inside the battery. The internal circuit connection structure of the battery obtained in step S21 is mapped and analyzed by the voltage-current dynamic behavior data for line resistance fluctuations. Under different working conditions, the current and voltage of the battery will fluctuate, and these fluctuations reflect the impedance changes of the internal circuit of the battery. By modeling and calculating the dynamic voltage-current data of the battery, the resistance fluctuation of the circuit under different working conditions can be mapped. The mapping data provides an important basis for analyzing the current flow and power loss inside the battery, and helps to understand the changes in the electrical performance of the battery under different working conditions. Based on the line resistance fluctuation mapping data obtained in step S22, the fluctuation working condition of the internal circuit of the battery is stress evaluated. The resistance fluctuation of the internal circuit of the battery is directly related to the current change. These fluctuations produce stress on the internal materials and structures of the battery, which in turn affects the long-term stability and safety of the battery. By performing stress assessment on these fluctuating conditions, stress data of the circuit under different loads and working conditions can be obtained. These assessment data help identify potential risks and failures faced by the battery under specific loads and working environments, thereby providing guidance for the optimal design and health management of the battery. Based on the voltage-current behavior dynamic behavior data and the line resistance fluctuation mapping data, the extreme condition response simulation of the line fluctuation condition stress data is performed. This process simulates the response of the internal circuit of the battery under these extreme conditions by exposing the battery to harsh conditions such as extreme loads and temperature changes. The simulation results can reveal the performance, stability and durability of the internal circuit of the battery under extreme conditions, and further help evaluate the working ability of the battery under extreme environments. Through these simulations, the response data of the circuit under extreme conditions can be obtained, providing data support for battery design improvements, performance optimization and safety warnings.

[0021] Preferably, step S23 includes the following steps:

[0022] Step S231: performing point-by-point fluctuation amplitude statistical analysis on the line resistance fluctuation mapping data to obtain resistance fluctuation amplitude distribution data;

[0023] Step S232: quantifying the interval difference between nodes based on the resistance fluctuation amplitude distribution data to obtain the node fluctuation difference interval;

[0024] Step S233: performing line pressure thermal effect distribution analysis on the line resistance fluctuation mapping data based on the node fluctuation difference interval to obtain thermal effect pressure distribution data;

[0025] Step S234: performing high and low pressure dynamic response analysis on the thermal effect pressure distribution data according to the voltage-current behavior dynamic behavior data to obtain thermal effect voltage response data;

[0026] Step S235: performing point-by-point load strain analysis on the line according to the thermal effect pressure distribution data and the thermal effect voltage response data to obtain point-by-point load strain data on the line;

[0027] Step S236: Perform line fluctuation condition stress assessment based on the thermal effect pressure distribution data, the thermal effect voltage response data, and the line point-by-point load strain data to obtain line fluctuation condition stress data.

[0028] The present invention first analyzes the resistance fluctuation mapping data of the internal circuit of the battery point by point, and counts the resistance fluctuation amplitude of each measuring point. This process analyzes the voltage and current changes of each node and their impact on the impedance, evaluates the amplitude of its fluctuation, and thus obtains the resistance fluctuation amplitude distribution data of the entire circuit. These distribution data provide a basis for the subsequent analysis of the electrical performance and stress state of the circuit under different working conditions, help to understand the impact of impedance changes in different regions on battery performance, and provide an important reference for fault prediction and optimization design. Based on the resistance fluctuation amplitude distribution data obtained in step S231, the resistance fluctuations between different nodes in the circuit are further quantified. The purpose of this step is to identify the resistance fluctuation differences between the nodes in the circuit and quantify these differences into specific numerical intervals. Through this quantitative analysis, those node areas with large fluctuation amplitudes or frequent fluctuations can be found, and the weak links of the battery under different loads and working conditions can be identified. This process provides a specific node fluctuation difference interval for subsequent stress analysis and fault diagnosis, and provides a basis for optimization design. Combined with the node fluctuation difference interval information in step S232, the pressure thermal effect distribution analysis of the circuit resistance fluctuation mapping data is further performed. Current fluctuations inside the battery can cause local heating, which in turn generates thermal effect pressure. These thermal effects not only affect the electrical performance of the battery, but also cause material aging or damage. Through this analysis, the distribution data of thermal effect pressure in the battery circuit can be obtained, and it is clear which areas are subject to greater thermal stress under different load conditions, thereby providing an important basis for subsequent performance evaluation and safety management. Based on the voltage-current behavior dynamic behavior data, the high and low voltage dynamic response analysis of the thermal effect pressure distribution data is performed. This process simulates and analyzes the fluctuations of current and voltage under the high and low voltage conditions of the battery and their impact on the thermal effect pressure of the circuit. Through dynamic response analysis, it can be revealed how the thermal effect and pressure distribution of the circuit change under different voltage conditions, and analyze the performance of the battery under these dynamic conditions. Through this analysis, the potential risks of the battery under high and low voltage working conditions can be identified, providing a basis for the safety and stability evaluation of the battery. Combined with the thermal effect pressure distribution data and the thermal effect voltage response data, the point-by-point load strain of the internal circuit of the battery is analyzed. Since the battery will generate thermal effect pressure during operation, and this pressure will affect the physical form and material properties of the battery circuit, the stress and deformation degree of each node under specific working conditions can be evaluated through point-by-point load-strain analysis of the circuit. This step can reveal the damage of the circuit after long-term use or under specific working conditions, and help identify potential failure points inside the battery. Combining the thermal effect pressure distribution data, thermal effect voltage response data and point-by-point load-strain data of the circuit in the previous steps, a comprehensive evaluation of the fluctuating working condition stress of the internal circuit of the battery is carried out.Through this comprehensive assessment, we can understand the total stress on the internal circuits of the battery under different working conditions, as well as the impact on battery performance and safety. This process helps identify stress concentration areas in the battery under normal and extreme conditions, and provides guidance for battery design improvements, fault prediction and prevention measures, thereby improving battery reliability and service life.

[0029] Preferably, step S24 comprises the following steps:

[0030] Step S241: performing composite feature interaction analysis on the voltage-current behavior dynamic behavior data and the line resistance fluctuation mapping data to obtain composite feature interaction data;

[0031] Step S242: performing parameter instability analysis on the composite feature interaction data to obtain feature parameter instability data;

[0032] Step S243: performing limit state instability reconstruction according to characteristic parameter instability data to obtain limit state instability reconstruction data;

[0033] Step S244: performing operating condition boundary analysis on the voltage-current behavior dynamic behavior data and the line resistance fluctuation mapping data based on the limit state instability reconstruction data, and obtaining voltage-current operating condition boundary data and line resistance operating condition boundary data respectively;

[0034] Step S245: Perform extreme operating condition response simulation according to the voltage-current operating condition characteristic boundary data and the line resistance operating condition characteristic boundary data to obtain line extreme operating condition response data.

[0035] The present invention can capture the complex change characteristics of the power system operation state from multiple dimensions through the interactive analysis of the voltage-current dynamic behavior data and the line resistance fluctuation mapping data. The composite feature interaction data not only considers the mutual influence of voltage and current, but also introduces the fluctuation of line resistance into the analysis, and comprehensively reflects the changes of the line under various working conditions. In this way, more abundant input data can be provided for the subsequent instability analysis, thereby improving the accuracy and credibility of the subsequent analysis results. When performing parameter instability analysis, the system models the potential parameter instability in the composite feature interaction data, and identifies the volatility and change trend of voltage-current behavior and line resistance under different working conditions. The key value of this analysis is that it can reveal the dynamic instability of the system under external disturbances or load changes, and then capture the potential risks of causing failures or performance degradation. The obtained characteristic parameter instability data can provide an effective basis for the subsequent limit state instability reconstruction, and improve the accuracy and reliability of the prediction. Through the limit state instability reconstruction, the system can simulate the operating state of the power line under extreme conditions, especially the response behavior when approaching or exceeding the safety limit. The important significance of this process is that it can identify the potential instability points of the line in advance and warn of the line failure or safety risk that occurs. Through this step, the system can be deeply analyzed to understand the interaction between voltage, current and line resistance under different extreme conditions, ensuring that the power system can be effectively monitored and managed under extreme conditions. In the condition behavior boundary analysis, the system can establish the key condition boundaries of voltage-current behavior and line resistance fluctuations through deep mining of limit state instability reconstruction data. The beneficial effect of this step is that it can provide the system with boundary values ​​for voltage, current and resistance changes, thereby defining the stable operating range of the line under different conditions. The analysis of these boundary data helps to evaluate the reliability of the system under critical conditions and provide data support for the formulation of maintenance and optimization strategies. Extreme condition response simulation can accurately simulate the operating performance of the line under extreme conditions by applying the voltage-current condition behavior boundary data and the line resistance condition behavior boundary data, especially when approaching or exceeding the safety limit of the equipment. The beneficial effect of this step is that it can simulate the real response of the line under extreme conditions, predict abnormal situations, and provide early warning for emergency response and preventive maintenance. Through this precise simulation, the safety of the power system can be effectively improved and serious failures or shutdowns caused by factors such as equipment aging and load fluctuations can be prevented.

[0036] Preferably, step S244 includes the following steps:

[0037] Based on the limit state instability reconstruction data, the voltage-current behavior dynamic behavior data and the line resistance fluctuation mapping data are used to extract the voltage-current instability state data and the resistance instability state data, and the voltage-current instability state data and the resistance instability state data are obtained respectively;

[0038] Perform dynamic impedance phase decoupling according to the voltage-current instability data and the resistance instability state data to obtain dynamic impedance phase decoupling data;

[0039] Perform microwave conduction effect analysis on dynamic impedance phase decoupling data to obtain microwave conduction effect data;

[0040] According to the microwave conduction effect data, the electric field disturbance boundary analysis is performed on the voltage-current instability data and the resistance instability state data to obtain the electric field disturbance boundary data;

[0041] Based on the electric field disturbance boundary data and microwave conduction effect data, the voltage-current instability data and resistance instability state data are subjected to operating condition boundary analysis, and the voltage-current operating condition boundary data and line resistance operating condition boundary data are obtained respectively.

[0042] The present invention can accurately extract voltage-current instability state data and line resistance instability state data by analyzing the voltage-current behavior dynamic behavior data and line resistance fluctuation data based on the limit state instability reconstruction data. The key to this step is to identify the dynamic instability phenomenon occurring in the system and accurately predict the performance of the power system under extreme working conditions, thereby providing data support for the subsequent dynamic optimization of the power system. After obtaining the voltage-current instability data and the resistance instability state data, dynamic impedance phase decoupling is the key to further analyzing the response of the power system. This process can more clearly identify the dynamic impedance characteristics in the power system by separating the phase information of voltage and current fluctuations. The decoupled data is more operational, providing an accurate basis for fine-tuning and optimizing system stability, especially under high load or complex working conditions, and can warn of potential instability risks in advance. The microwave conduction effect analysis of the dynamic impedance phase decoupling data can study the influence of microwave signals on conductor materials in the power system, as well as the microwave conduction effect occurring under current fluctuations. This analysis helps to further understand the behavior of the power system in high-frequency and high-current environments, and reveals the coupling relationship between the electric field and the current. Through this analysis, the conductive performance of the power system can be optimized and the additional losses caused by current or voltage fluctuations can be reduced. The electric field disturbance boundary analysis based on the microwave conduction effect data can identify the disturbance range of the electric field in the system under unstable conditions. Through the analysis of the electric field disturbance boundary, the electric field distribution of the system under extreme voltage or current changes can be effectively predicted, and the power equipment can be prevented from being damaged by excessive or uneven electric fields, ensuring the safety and stability of the power system under complex operating conditions. Finally, the operating condition boundary analysis based on the electric field disturbance boundary data and the microwave conduction effect data can accurately define the safety boundary of the system under different working conditions. This analysis can not only help identify the optimal operating range of the power system under specific conditions, but also predict the stability boundary of the system under different behavior modes. On this basis, an accurate optimization solution can be provided to improve the adaptability and stability of the power system under various complex conditions.

[0043] Preferably, step S3 comprises the following steps:

[0044] Step S31: normalizing the line extreme operating condition response data to obtain normalized line extreme operating condition response data;

[0045] Step S32: performing battery cycle attenuation gradient simulation on the voltage-current behavior dynamic behavior data and the line resistance fluctuation mapping data according to the line extreme working condition response normalization data to obtain the extreme working condition battery cycle attenuation gradient data;

[0046] Step S33: performing spatial interpolation processing on the extreme operating condition battery cycle attenuation gradient data to obtain extreme cycle attenuation gradient interpolation data;

[0047] Step S34: Perform cycle life prediction on the limit cycle attenuation gradient interpolation data based on a logistic regression algorithm to obtain battery limit cycle life data.

[0048] The present invention unifies data of different dimensions and amplitudes into the same scale range, thereby eliminating the dimensional differences between different data and making the data more comparable and consistent. By normalizing the line extreme operating condition response data, outliers, extreme fluctuations or noise in the original data can be effectively removed, and the stability and reliability of the data can be enhanced. The purpose of this step is to ensure that subsequent analysis can be carried out based on a unified standard, avoid errors caused by data scale differences, and improve the accuracy and reliability of the entire analysis process. Battery cycle attenuation gradient simulation can reveal the attenuation behavior of the battery under different extreme operating conditions. The beneficial effect of this step is that by comprehensively considering the voltage-current behavior dynamic data and the line resistance fluctuation data, the decay process of the battery in actual working conditions can be more accurately simulated. Through the battery cycle attenuation gradient simulation, the decay rate of battery performance under different current loads and voltage fluctuations can be quantified, providing accurate basic data for predicting battery life and optimizing battery management. This analysis helps to formulate a more reasonable battery management strategy, extend battery life, and avoid performance degradation or failure due to excessive decay. The beneficial effect of spatial interpolation processing is that by interpolating the cycle attenuation gradient data of the battery under extreme working conditions, the missing data or insufficient data points that appear in the simulation process can be filled, thereby generating a more continuous and complete data set. This process ensures the spatial consistency of the data by estimating the attenuation data at unknown locations and avoids prediction bias caused by data sparsity. The interpolated data is more conducive to further analyzing the cycle attenuation trend of the battery under different working conditions, providing more accurate and comprehensive input for battery life prediction. The application of the logistic regression algorithm in this step can effectively model the extreme cycle attenuation gradient data and predict the extreme cycle life of the battery. Logistic regression is a statistical method widely used in classification and regression problems, which can predict the life of the battery under different working conditions based on the input feature data. The beneficial effect of this step is that it can make accurate life predictions based on the historical decay mode of the battery and the data of the current working conditions. By analyzing the extreme cycle attenuation gradient interpolation data, the failure point of the battery can be identified in advance, effective early warning and intervention can be carried out, and the power system can implement a scientific battery replacement and maintenance strategy, thereby improving the efficiency of battery use and reducing the risk of failure.

[0049] Preferably, step S32 includes the following steps:

[0050] Step S321: discretize the normalized data of the line extreme operating condition response to obtain discrete extreme operating condition response data;

[0051] Step S322: extracting the voltage-current dynamic behavior data in a dynamic hierarchical distribution according to the discrete limit condition response data to obtain voltage-current dynamic hierarchical distribution data;

[0052] Step S323: performing multi-dimensional electrochemical impedance coupling on the line resistance fluctuation mapping data according to the voltage-current dynamic hierarchical distribution data to obtain electrochemical impedance coupling data;

[0053] Step S324: Perform battery cycle attenuation gradient simulation based on electrochemical impedance coupling data to obtain extreme operating condition battery cycle attenuation gradient data.

[0054] The discretization processing of the present invention converts continuous line extreme condition response data into discrete data points, which is crucial for subsequent analysis and simulation. By converting the data into discrete form, complex continuous calculations can be reduced, and the data can be more conveniently processed and analyzed on a digital platform. The beneficial effect of this step is that it can improve the calculation efficiency of the data and make the extreme condition response data of the power system more operable in the model. After discretization, the data is easier to analyze further, especially when performing battery decay simulation and performance prediction, the discrete data can accurately capture the key behavior patterns under extreme conditions. The discretized extreme condition response data is combined with the voltage-current behavior dynamic data. Through the dynamic hierarchical distribution extraction technology, the behavior patterns of voltage and current can be stratified according to different levels, so as to deeply analyze the dynamic response of the system under different working conditions. This analysis helps to reveal the subtle changes in the voltage-current relationship under different operating conditions, and can identify the weaknesses and potential risk areas of the battery. Dynamic hierarchical distribution data can help accurately model the performance of the battery under multiple working conditions, predict the decay rate of the battery under different voltage and current conditions, and thus improve the management efficiency of the entire battery system. By coupling the voltage-current dynamic layered distribution data with the line resistance fluctuation mapping data in multiple dimensions, the coupling between the battery and the line can be captured in multiple dimensions. This coupling takes into account the mutual influence of multiple factors such as voltage, current, impedance, etc., and can accurately simulate the performance of the battery under different electrical loads and working environments. The acquisition of electrochemical impedance coupling data can help the system more accurately analyze the electrochemical reactions, ion migration processes and battery capacity attenuation mechanisms inside the battery. The importance of this step is that it can provide more detailed physical model support for battery performance evaluation and life prediction, and help to deeply understand the performance of the battery under extreme conditions. The battery cycle attenuation gradient simulation based on electrochemical impedance coupling data can accurately simulate the attenuation process of the battery under extreme conditions. Through this simulation, the rate of capacity decrease of the battery in multiple charge and discharge cycles can be quantified, especially how it affects the overall life of the battery under different current, voltage fluctuations and changes in battery materials. The beneficial effect of this step is that it provides a more refined and accurate attenuation model, which can help identify the potential failure points, performance bottlenecks and decline trends of the battery, thereby providing a scientific basis for battery maintenance and replacement cycles and reducing the risk of battery failure.

[0055] Preferably, step S34 includes the following steps:

[0056] Step S341: extracting feature vectors from the limit cycle attenuation gradient interpolation data to obtain cycle attenuation feature vectors;

[0057] Step S342: performing time series trend analysis on the cyclic decay feature vector to obtain cyclic decay time series trend data;

[0058] Step S343: performing probability distribution fitting on the cyclic decay time series trend data to obtain cyclic decay probability distribution data;

[0059] Step S344: Perform cycle life prediction on the cycle attenuation probability distribution data based on a logistic regression algorithm to obtain battery limit cycle life data.

[0060] The feature vector extraction of the present invention is a key step in data analysis. It extracts key patterns and trends from the limit cycle decay gradient interpolation data and compresses the original data into a more concise and representative vector. In this process, the multidimensional data of battery decay can be converted into a lower-dimensional feature vector for further analysis. The extracted feature vector can fully reflect the decay trend and key parameters of the battery under extreme conditions, such as capacity decay rate, voltage change, etc. By extracting these features, more accurate input data can be provided for subsequent prediction analysis, which helps to improve the accuracy of prediction and the efficiency of the model. Time series trend analysis is to analyze the changes in the cycle decay feature vector in the time series, aiming to reveal the long-term trends and laws in the battery decay process. Through this analysis, the changing characteristics of battery decay behavior at different time points can be captured, especially whether the battery performance decline shows an accelerated or slow trend during long-term use. The beneficial effect of this step is that it can reveal the periodic changes and potential abnormal trends of battery decay, which helps to predict the failure problem of the battery at some point in the future. By analyzing the time series data, the system can better identify the decay law of the battery, thereby providing data support for the management and maintenance of the battery. Probability distribution fitting is a commonly used method in statistical analysis. By fitting the cycle decay time series trend data, a probability model of the battery cycle decay process can be constructed. This step analyzes the distribution characteristics of the decay data and finds the most suitable probability distribution model (such as normal distribution, exponential distribution, etc.) to describe the randomness and uncertainty of the battery decay process. The fitted probability distribution data can more accurately reflect the possibility of battery decay under different working conditions, providing an important basis for battery life prediction. Its beneficial effect is to provide statistically based probability support for subsequent cycle life prediction, which can better estimate the reliability of the battery under various working conditions and reduce the error rate of prediction. The application of the logistic regression algorithm in this step can model and analyze the cycle decay probability distribution data and further predict the cycle life of the battery. Logistic regression is a powerful statistical learning method, especially suitable for dealing with binary or multi-classification problems, but it can also be effectively applied in regression tasks. By combining the battery decay data with probability distribution, logistic regression can establish a mapping relationship between decay mode and life, and provide prediction results on the battery limit cycle life. The beneficial effect is that logistic regression can combine the different operating conditions and attenuation behaviors of the battery to accurately predict the remaining life of the battery, help users optimize battery usage strategies, avoid premature replacement or unexpected failures, and thus improve battery management efficiency and economy.

[0061] Preferably, the present invention further provides a battery detection system for executing the battery detection method as described above, the battery detection system comprising:

[0062] The voltage-current capture module is used to collect data on the battery operation status to obtain battery operation status data; the voltage-current dynamic behavior capture of the battery operation status cleaning data is performed to obtain voltage-current dynamic behavior data;

[0063] The extreme working condition response simulation module is used to map the line resistance fluctuation according to the voltage-current behavior dynamic behavior data to obtain the line resistance fluctuation mapping data; perform extreme working condition response simulation according to the line resistance fluctuation mapping data to obtain the line extreme working condition response data;

[0064] The cycle life prediction module is used to simulate the battery cycle attenuation gradient according to the line extreme operating condition response data to obtain the extreme operating condition battery cycle attenuation gradient data; based on the logistic regression algorithm, the cycle life is predicted for the extreme operating condition battery cycle attenuation gradient data to obtain the battery extreme cycle life data.

[0065] The beneficial effects of the present invention are as follows: first, the operating status data of the battery is collected through sensors and monitoring equipment, including key information such as the voltage, current, and temperature of the battery. These data reflect the performance and behavior of the battery during actual use. Next, the collected battery operating status data needs to be cleaned and preprocessed to remove noise and outliers to ensure the accuracy of the data. After data cleaning, the voltage and current changes of the battery are dynamically captured to obtain the voltage-current (VI) dynamic behavior data of the battery. These data provide a basis for subsequent line resistance fluctuations and battery performance analysis. The voltage-current dynamic behavior data obtained in the first step is used to perform line resistance fluctuation mapping. During the operation of the battery, its internal line impedance fluctuates with changes in load. By modeling these fluctuation data, fluctuation mapping data of the line resistance can be obtained, which provides an important basis for analyzing the current flow and power loss inside the battery. Further, according to the fluctuation of the line resistance, the response simulation under extreme working conditions is performed, that is, the response characteristics of the line under extreme conditions (such as high temperature, high load, etc.). Through these simulations, the response data of the line under extreme working conditions are obtained, thereby providing a simulation basis for the durability and extreme working condition performance of the battery. The cycle attenuation gradient simulation of the battery is performed based on the line extreme working condition response data obtained in step S2. During the long-term charging and discharging process of the battery, the performance will gradually decay due to the influence of the line impedance and working conditions. By modeling and simulating the battery behavior under extreme working conditions, the attenuation gradient data of the battery under different cycle numbers can be obtained. These simulation data reflect the decay rate and capacity loss of the battery under extreme working conditions, and help predict the life change of the battery under extreme working conditions. Based on the above simulation results, the cycle attenuation gradient data of the battery is analyzed using a logistic regression algorithm to predict the cycle life of the battery. The logistic regression algorithm can effectively capture the relationship between the battery performance decay and the use environment, thereby predicting the actual cycle life of the battery under various extreme conditions, and ensuring the reliability and long-term stability of the battery in practical applications. Therefore, the present invention is an optimization process for a traditional battery detection method, which solves the problems of inaccurate recognition of cycle attenuation under extreme operating conditions of the battery and inaccurate prediction of cycle life of the traditional battery detection method, and improves the accuracy of recognition of cycle attenuation under extreme operating conditions of the battery and the accuracy of cycle life prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A schematic diagram of the steps of a battery detection method;

[0067] Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0068] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0069] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0070] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0071] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0072] To achieve this, please refer to Figure 1 to Figure 2 , a battery detection method, the method comprising the following steps:

[0073] Step S1: collecting data on the battery operation status to obtain battery operation status data; capturing the voltage-current dynamic behavior of the battery operation status cleaning data to obtain voltage-current dynamic behavior data;

[0074] Step S2: mapping the line resistance fluctuation according to the voltage-current behavior dynamic behavior data to obtain line resistance fluctuation mapping data; performing extreme operating condition response simulation according to the line resistance fluctuation mapping data to obtain line extreme operating condition response data;

[0075] Step S3: Perform battery cycle attenuation gradient simulation according to the line extreme operating condition response data to obtain extreme operating condition battery cycle attenuation gradient data; perform cycle life prediction on the extreme operating condition battery cycle attenuation gradient data based on a logistic regression algorithm to obtain battery extreme cycle life data.

[0076] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of a battery detection method according to the present invention. In this example, the battery detection method includes the following steps:

[0077] Step S1: collecting data on the battery operation status to obtain battery operation status data; capturing the voltage-current dynamic behavior of the battery operation status cleaning data to obtain voltage-current dynamic behavior data;

[0078] In an embodiment of the present invention, a high-precision multi-channel current and voltage sensor and a data acquisition system are used when data is collected for the battery operation status. The sensor uses milliseconds as the sampling period to record the voltage, current and temperature of the battery respectively to form the initial battery operation status data. During the acquisition process, the hardware filter circuit is used to filter out high-frequency noise to ensure data accuracy. The collected data is stored in real time in the local storage medium and arranged in timestamp order to ensure data continuity for subsequent operations. Subsequently, the battery operation status data is cleaned, and a bidirectional sliding window algorithm is used to remove abnormal points and sampling errors. First, the mean and variance of each data point in the sliding window are calculated, and the data points that deviate from the mean by 3 times the standard deviation are marked as abnormal values ​​by setting a fixed threshold, and then these points are repaired in combination with the linear interpolation method. After cleaning, the battery operation status cleaning data is generated. Then, the voltage-current dynamic behavior of the battery operation status cleaning data is captured, and the frequency components in the signal are extracted by Fourier transform to further identify the dynamic voltage-current relationship caused by load changes. For more accurate analysis, it is also necessary to combine the dynamic time warping (DTW) algorithm to align the dynamic behaviors of different time series to capture the voltage-current fluctuation characteristics during rapid changes, and finally obtain the voltage-current behavior dynamic behavior data.

[0079] Step S2: mapping the line resistance fluctuation according to the voltage-current behavior dynamic behavior data to obtain line resistance fluctuation mapping data; performing extreme operating condition response simulation according to the line resistance fluctuation mapping data to obtain line extreme operating condition response data;

[0080] In an embodiment of the present invention, when using the voltage-current behavior dynamic behavior data to carry out line resistance fluctuation mapping, it is necessary to first establish a connection topology model of the internal circuit of the battery. Combining the voltage and current data, the real-time resistance value of each connection path is calculated by Ohm's law and recorded in a two-dimensional matrix. The resistance value between the sampling points is continuously complemented by the data interpolation method, and the sampling error is reduced by Laplace smoothing to obtain more accurate line resistance fluctuation mapping data. When carrying out the extreme operating condition response simulation based on the above data, the finite element analysis method is introduced. First, the differential unit grid is divided according to the line resistance fluctuation mapping data, and the equivalent thermal resistance model of the internal circuit is constructed. By inputting the preset current impact signal, the current distribution and heat generation of each unit are calculated, and the temperature field change is simulated in combination with the heat transfer equation. Synchronously introduce intermittent current loading to evaluate the stability of the line resistance under extreme conditions, and finally generate the line extreme operating condition response data.

[0081] Step S3: Perform battery cycle attenuation gradient simulation according to the line extreme operating condition response data to obtain extreme operating condition battery cycle attenuation gradient data; perform cycle life prediction on the extreme operating condition battery cycle attenuation gradient data based on a logistic regression algorithm to obtain battery extreme cycle life data.

[0082] In an embodiment of the present invention, when carrying out battery cycle attenuation gradient simulation based on the line extreme condition response data, it is necessary to first perform quantitative modeling of the battery internal resistance and capacity attenuation. The multi-point interpolation method and cumulative effect analysis method are used to derive the nonlinear relationship between the attenuation rate and the number of cycles using the voltage change trend in the line extreme condition response data. This relationship is extended to the working condition of multiple cycle loading, and the attenuation rate under different loadings is calculated step by step and iteratively, and finally the extreme condition battery cycle attenuation gradient data is generated. When the above data are used for cycle life prediction using the logistic regression algorithm, feature extraction of the attenuation gradient data is required. The characteristic variables that mainly affect the life are extracted through principal component analysis (PCA), and these variables are input into the logistic regression model. Logistic regression takes the determination of the remaining capacity of the battery as the goal, calculates the model parameters through the maximum likelihood estimation method, predicts the cycle life of the battery after iterative optimization, and outputs the battery limit cycle life data.

[0083] Preferably, step S1 comprises the following steps:

[0084] Step S11: collecting data on the battery operating status to obtain battery operating status data;

[0085] Step S12: Cleaning the battery operation status data to obtain battery operation status cleansing data;

[0086] Step S13: Timestamp the battery operation status cleaning data to obtain battery operation status timestamp data;

[0087] Step S14: capturing the voltage-current dynamic behavior of the battery operation status cleaning data according to the battery operation status time stamp data to obtain voltage-current dynamic behavior data.

[0088] In an embodiment of the present invention, when data is collected for the battery operating status, a high-precision multi-channel data acquisition hardware module (such as a voltage and current sensor array and a thermistor array) is configured to monitor the battery operating status in real time. The acquisition hardware records the voltage, current, temperature and external environmental parameters (such as humidity and pressure) of the battery at a time interval of 1 millisecond. The collected signal is subjected to hardware low-pass filtering through a filter circuit to suppress high-frequency noise. Subsequently, a differential amplifier circuit is used to amplify the weak signal to ensure the accuracy and consistency of data acquisition. The data is synchronously transmitted to a local storage device, arranged by channel number and timestamp, and the order consistency of all sampling points is ensured. The collected data is saved in an uncompressed binary format to form battery operating status data for subsequent processing. When data cleaning is performed on the battery operating status data, all collected data is first imported and outliers are screened using a bidirectional sliding window algorithm. Specifically, a fixed window width is set, and the mean and standard deviation of the data in the window are calculated with each data point as the center, and points that deviate from the mean by three times the standard deviation are marked as outliers. In the cleaning stage, repeated sampling values ​​must also be detected, and repeated data records must be checked and deduplicated using a hash algorithm. The interpolation method is used to repair the abnormal data points. The first-order linear interpolation method is selected to calculate the estimated value of the missing point according to its adjacent valid data points. The cleaned data is further low-pass filtered, and the low-frequency part of the signal is extracted by fast Fourier transform (FFT) to remove high-frequency noise. After cleaning, it is reorganized into a matrix structure to obtain the battery operation status cleaned data. Based on the battery operation status cleaned data, when timestamping, an accurate time identifier is assigned to each data record. The timestamp marking process uses the hardware clock as a reference, and the synchronization correction algorithm is used to ensure the uniformity of the time base of different sampling devices. First, the initial timestamp is extracted according to the time record of the acquisition hardware, and the multi-point alignment method is used to correct the offset data block. The correction algorithm aims to minimize the time difference and corrects the time stamp error of the sampling point by linear weighting. Subsequently, the cleaned data is reordered in chronological order to ensure its continuity. The final time stamp data is integrated into the record of the cleaned data to generate the battery operation status time stamp data. When using the battery operating status time stamp data to capture the voltage-current dynamic behavior of the battery operating status cleaning data, the dynamic time warping (DTW) algorithm is used to match the voltage and current data of different time series. First, the voltage and current data are synchronized and aligned according to the time stamp data to ensure that the time series of the two have completely consistent starting points and step sizes. Then, Fourier transform is applied to the aligned voltage and current data to extract the frequency features to characterize the periodic changes in the voltage-current relationship. The differential algorithm is further combined to calculate the instantaneous derivatives of the voltage and current to capture the mutation features in the dynamic behavior, such as sudden voltage drops or short-term large current shocks.The above analysis results are normalized to form a unified data scale, and finally the voltage-current behavior dynamic behavior data is generated for use in subsequent steps.

[0089] Preferably, step S2 comprises the following steps:

[0090] Step S21: obtaining the internal circuit connection structure of the battery;

[0091] Step S22: mapping the line resistance fluctuation of the internal line connection structure of the battery according to the voltage-current behavior dynamic behavior data to obtain line resistance fluctuation mapping data;

[0092] Step S23: performing line fluctuation working condition stress assessment on the line resistance fluctuation mapping data to obtain line fluctuation working condition stress data;

[0093] Step S24: performing extreme operating condition response simulation on the line fluctuation operating condition stress data according to the voltage-current behavior dynamic behavior data and the line resistance fluctuation mapping data to obtain the line extreme operating condition response data.

[0094] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0095] Step S21: obtaining the internal circuit connection structure of the battery;

[0096] In an embodiment of the present invention, when obtaining the internal circuit connection structure of the battery, it is first necessary to perform a detailed physical structure scan of each circuit module inside the battery. Through high-resolution X-ray tomography (CT scanning) technology, a three-dimensional structure diagram of all battery cells, circuit connection points, protection circuits and thermal management systems inside the battery is obtained. In this process, the scanning resolution is set to micron level to ensure a clear presentation of the tiny structure inside the battery. The scanned data is processed by an image processing algorithm, and the different modules of the battery (such as battery cells, battery management system, thermal management circuit, etc.) are marked using image segmentation technology to extract the connection lines inside the battery. The scanned data is reconstructed using simulation software to obtain an accurate geometric model of the internal circuits of the battery, including the connection structure between each battery cell and the corresponding current flow path. The finite element analysis (FEA) method is further used to establish a physical model of the internal circuit connection structure of the battery to provide basic data for subsequent circuit behavior analysis.

[0097] Step S22: mapping the line resistance fluctuation of the internal line connection structure of the battery according to the voltage-current behavior dynamic behavior data to obtain line resistance fluctuation mapping data;

[0098] In an embodiment of the present invention, when mapping the line resistance fluctuation of the internal circuit connection structure of the battery according to the voltage-current behavior dynamic behavior data, the voltage and current data of the battery under different operating conditions are first collected in real time, and the battery voltage and current waveforms under different loads are obtained through a multi-channel synchronous data acquisition system. According to the relationship between the battery voltage and current changes, Ohm's law is used to derive the resistance value at each moment. On this basis, the dynamic time warping (DTW) algorithm is used to compare and match the voltage and current waveform data to ensure the time synchronization between the current and voltage. Then, according to the dynamic changes of voltage-current, the resistance fluctuation of the internal circuit of the battery is calculated, and the sliding window algorithm is used to evaluate the real-time change of resistance within a certain time window. To ensure the accuracy of the results, multiple measurements are performed under different working conditions of the battery, and finally the fluctuation data of the internal circuit resistance of the battery are obtained. These data can reflect the resistance change trend of the circuit under different loads and working environments, and form the line resistance fluctuation mapping data.

[0099] Step S23: performing line fluctuation working condition stress assessment on the line resistance fluctuation mapping data to obtain line fluctuation working condition stress data;

[0100] In an embodiment of the present invention, when the line resistance fluctuation mapping data is subjected to line fluctuation working condition stress assessment, the line resistance fluctuation data obtained in the previous step is first used to simulate the working condition using the finite element analysis (FEA) method to analyze the stress distribution of the internal circuit of the battery under different current loads, ambient temperatures and working hours. Specifically, by establishing a stress-strain relationship model of the circuit, the line resistance fluctuation data is input to simulate the thermal effect of the circuit under different current loads and the physical deformation caused by the current. Next, the current mutation, charge and discharge rate changes and other working conditions that occur during the operation of the battery are simulated to evaluate the stress changes borne by the internal circuit of the battery under these extreme working conditions. The stress assessment results are verified based on the yield point and fracture strength of the material to ensure that the obtained stress data is within a reasonable range. Through this method, the stress state of the internal circuit of the battery under various working conditions can be comprehensively evaluated, providing reliable data support for subsequent extreme working condition response simulations.

[0101] Step S24: performing extreme operating condition response simulation on the line fluctuation operating condition stress data according to the voltage-current behavior dynamic behavior data and the line resistance fluctuation mapping data to obtain the line extreme operating condition response data.

[0102] In an embodiment of the present invention, when simulating the extreme working condition response of the line fluctuation working condition stress data according to the voltage-current behavior dynamic behavior data and the line resistance fluctuation mapping data, a multi-dimensional battery working condition model is first constructed in combination with the actual operation of the battery. The model takes into account factors such as the battery's charge and discharge rate, current load, ambient temperature, humidity, etc., and uses the finite element analysis (FEA) method to simulate the extreme working condition. During the simulation process, the battery's voltage-current behavior data is used as an input condition, and the line resistance fluctuation mapping data and line stress data are used as boundary conditions to establish a comprehensive response model including various components inside the battery (such as battery cells, lines, connection points, etc.). The model simulates factors such as temperature rise, current mutation, and short-circuit protection under the battery's extreme load conditions to obtain the response results of the line under these extreme conditions. The results of the extreme working condition response simulation include data such as current changes, temperature changes, and stress fluctuations of each connecting line inside the battery under extreme working conditions. Through these simulation results, the reliability and potential failure risks of the battery under long-term high-load operation can be effectively predicted, providing important early warning information for the long-term use of the battery.

[0103] Preferably, step S23 includes the following steps:

[0104] Step S231: performing point-by-point fluctuation amplitude statistical analysis on the line resistance fluctuation mapping data to obtain resistance fluctuation amplitude distribution data;

[0105] Step S232: quantifying the interval difference between nodes based on the resistance fluctuation amplitude distribution data to obtain the node fluctuation difference interval;

[0106] Step S233: performing line pressure thermal effect distribution analysis on the line resistance fluctuation mapping data based on the node fluctuation difference interval to obtain thermal effect pressure distribution data;

[0107] Step S234: performing high and low pressure dynamic response analysis on the thermal effect pressure distribution data according to the voltage-current behavior dynamic behavior data to obtain thermal effect voltage response data;

[0108] Step S235: performing point-by-point load strain analysis on the line according to the thermal effect pressure distribution data and the thermal effect voltage response data to obtain point-by-point load strain data on the line;

[0109] Step S236: Perform line fluctuation condition stress assessment based on the thermal effect pressure distribution data, the thermal effect voltage response data, and the line point-by-point load strain data to obtain line fluctuation condition stress data.

[0110] In an embodiment of the present invention, when performing point-by-point fluctuation amplitude statistical analysis on the line resistance fluctuation mapping data, the resistance fluctuation values ​​at each moment and each node are first extracted from the resistance fluctuation mapping data. For these data, a time series statistical analysis method is used to perform amplitude statistics on the resistance fluctuation of each node at different time points. In the specific operation, the time window is first divided, for example, every 1 second is taken as an analysis unit, and the resistance fluctuation value in each unit is calculated to obtain the maximum value, minimum value and mean value in each time window, and the fluctuation amplitude, that is, the difference between the maximum value and the minimum value, is further calculated. Then, these fluctuation amplitudes are visualized and analyzed using a distribution histogram to intuitively display the distribution characteristics of the resistance fluctuation amplitude. Through this statistical analysis, the generated resistance fluctuation amplitude distribution data contains the fluctuation amplitude characteristics of each node in different time periods, providing accurate basic data for subsequent difference quantification and thermal effect analysis. When quantifying the interval difference between nodes based on the resistance fluctuation amplitude distribution data, the fluctuation amplitude range of each node is first extracted from the resistance fluctuation amplitude distribution data. The fluctuation amplitude difference between nodes is quantitatively analyzed, and the fluctuation amplitude is divided into multiple intervals according to size using the segmented interval method, for example, multiple amplitude intervals are divided at intervals of 0.01 ohms. Then, the amplitude difference between adjacent nodes is calculated, and the interval range to which the difference belongs is recorded, thereby forming the node fluctuation difference interval data. In this process, the quantification result of the amplitude difference between each pair of adjacent nodes is represented by a matrix, and the matrix elements are the difference interval numbers or interval center values ​​between the corresponding nodes. In order to ensure data accuracy and calculation efficiency, the deviation analysis method is further used to finely check the data to ensure the accuracy and reliability of the node fluctuation difference interval data. When analyzing the line pressure thermal effect distribution of the line resistance fluctuation mapping data based on the node fluctuation difference interval, the resistance fluctuation mapping data and the node fluctuation difference interval are first combined to determine the thermal potential difference of each line node. The thermal effect caused by the resistance fluctuation in the line is calculated using Joule's law, and the resistance difference between the nodes is multiplied by the square of the current to obtain the thermal effect distribution of each section of the line. Then, the overall thermal effect of the line is analyzed by the finite element method, the heat distribution is simulated as a grid, and the line pressure thermal effect distribution diagram is drawn to show the distribution characteristics of heat and pressure. During the analysis process, the stress concentration area is locally amplified, and the data of the high thermal effect pressure area is further extracted. The generated thermal effect pressure distribution data can provide a basis for battery line design and optimization. When performing high and low voltage dynamic response analysis on the thermal effect pressure distribution data based on the voltage-current behavior dynamic behavior data, the voltage-current behavior dynamic behavior data is first decomposed into high and low pressure states in multiple time periods, and the corresponding current values ​​and time points are extracted respectively. The extracted data is combined with the thermal effect pressure distribution data to analyze the dynamic response characteristics of voltage changes to thermal effect pressure.Specifically, the pressure gradient is calculated for the high and low pressure states in the thermal effect pressure distribution data, and the pressure change rate and stress mutation at the moment of high and low pressure switching are recorded. Through the time series analysis method, the dynamic curve of pressure response over time is drawn to obtain the thermal effect voltage response data. In order to further improve the analysis accuracy, the main frequency component of the dynamic response is extracted by combining the frequency domain analysis method to quantify the influence of high and low pressure on the thermal effect. When performing point-by-point load strain analysis of the line according to the thermal effect pressure distribution data and the thermal effect voltage response data, the stress-strain relationship calculation method is used, combined with the material mechanics theory and the thermal effect voltage response data, to perform point-by-point load strain evaluation on each node of the line. Through the calculation unit division method, the entire line is divided into several subdivided areas, each area corresponds to an analysis node. At each node, the corresponding stress value is calculated according to the thermal effect pressure distribution data, and the load strain value of the node is derived in combination with the results of the high and low pressure dynamic response analysis. The strain values ​​of each node are connected by the cubic spline interpolation method to form the load strain distribution curve of the entire line. The point-by-point load strain data of the line finally generated can accurately reflect the load characteristics of the battery line under different thermal effects and voltage changes. When evaluating the stress of line fluctuation conditions based on thermal effect pressure distribution data, thermal effect voltage response data, and line point-by-point load strain data, a multidimensional stress analysis method is used to integrate the above data. First, a finite element model for stress analysis is constructed, and the pressure distribution, strain value, and dynamic response characteristics of each node are input, and the entire battery line system is regarded as an elastic material structure. Then, the model is solved using an iterative algorithm to analyze the overall stress distribution characteristics of the line under different voltage states and thermal effects. The stress concentration point and its failure risk area are determined by analyzing the principal stress direction and calculating the shear stress distribution. Finally, the calculation results are plotted as a stress distribution cloud map to intuitively display the stress response of the line under extreme conditions. The obtained line fluctuation condition stress data provides a key basis for the safety analysis of the battery system.

[0111] Preferably, step S24 comprises the following steps:

[0112] Step S241: performing composite feature interaction analysis on the voltage-current behavior dynamic behavior data and the line resistance fluctuation mapping data to obtain composite feature interaction data;

[0113] Step S242: performing parameter instability analysis on the composite feature interaction data to obtain feature parameter instability data;

[0114] Step S243: performing limit state instability reconstruction according to characteristic parameter instability data to obtain limit state instability reconstruction data;

[0115] Step S244: performing operating condition boundary analysis on the voltage-current behavior dynamic behavior data and the line resistance fluctuation mapping data based on the limit state instability reconstruction data, and obtaining voltage-current operating condition boundary data and line resistance operating condition boundary data respectively;

[0116] Step S245: Perform extreme operating condition response simulation according to the voltage-current operating condition characteristic boundary data and the line resistance operating condition characteristic boundary data to obtain line extreme operating condition response data.

[0117] In an embodiment of the present invention, when performing composite feature interaction analysis on the voltage-current behavior dynamic behavior data and the line resistance fluctuation mapping data, the two sets of data are firstly feature decomposed, and the voltage-current behavior dynamic behavior data is decomposed into sub-features such as voltage change rate, peak current, and time series characteristics, and the line resistance fluctuation mapping data is decomposed into sub-features such as fluctuation amplitude, frequency characteristics, and distribution characteristics. Then, the feature interaction matrix construction method is applied to combine the two sets of features according to time and space correlation to generate a multidimensional feature matrix. The covariance analysis method is used to calculate the interaction relationship between the features, focusing on analyzing the correlation between the resistance fluctuation characteristics and the current peak response during voltage mutation. The dominant interaction characteristics are extracted by matrix eigenvalue decomposition, and the significant interaction characteristics are further screened by combining the statistical significance test method, and the data is quantified to generate composite feature interaction data. When performing parameter instability analysis on the composite feature interaction data, the composite feature interaction data is first divided into stability measurement intervals, and the voltage change rate, resistance fluctuation intensity, etc. are selected as instability parameters. The time series stability test method is used to evaluate the local stability of the composite feature interaction data through the sliding window method, and the mean, variance and change trend characteristics in each time window are extracted. The Markov chain analysis method is used to calculate the transition probability of the unstable state and clarify the change law of the unstable characteristics. Combined with the parameter perturbation response analysis method, the sensitivity of the composite characteristics is evaluated, the parameter fluctuation amplitude at the critical moment and its influence on other parameters are recorded, and the characteristic parameter instability data is generated. The dynamic nonlinear mapping algorithm is used to improve the calculation accuracy throughout the process, and the data results are displayed in the form of an instability distribution diagram. When reconstructing the limit state instability based on the characteristic parameter instability data, the instability parameters with a higher frequency in the instability data are first extracted and used as the core input of the reconstruction. The state space method is used to construct a multidimensional instability parameter model, and the limit state is defined as the boundary condition where the instability amplitude of the characteristic parameter exceeds the threshold. On this basis, combined with the limit analysis theory, the dynamic evolution process of the characteristic parameters is simulated by the incremental method, and the instability path of each state is reconstructed. The cumulative instability effects of different parameters are calculated by numerical integration methods to generate corresponding instability curves, and the eigenvalues ​​of the limit instability parameters are calculated based on the reconstructed curves. Finally, the instability reconstruction results are organized into a high-dimensional matrix to generate limit state instability reconstruction data to reveal the key instability characteristics of the battery circuit. When performing operating condition boundary analysis on the voltage-current behavior dynamic behavior data and line resistance fluctuation mapping data based on the limit state instability reconstruction data, the segmented boundary fitting method is used to extract the operating condition boundary of the voltage and resistance data. First, the limit state instability reconstruction data is matched with the voltage-current behavior dynamic behavior data, the mutation point and change gradient of the voltage-current behavior under the limit state are calculated, and the eigenvalue of the operating condition boundary is extracted.Subsequently, the line resistance fluctuation mapping data is associated with the limit state instability reconstruction data, the fluctuation limit of the line resistance is defined, and the resistance condition boundary equation is fitted by nonlinear regression analysis. The partition analysis method is used to divide the characteristics of different working conditions, and the voltage-current working condition characteristic boundary data and the line resistance working condition characteristic boundary data are generated respectively to ensure that the two sets of data can fully describe the respective working condition limit characteristics. When simulating the extreme working condition response based on the voltage-current working condition characteristic boundary data and the line resistance working condition characteristic boundary data, the finite element analysis method is used to construct a multi-physics field coupling model, and the two sets of boundary data are integrated as model input. First, the node potential distribution under the extreme working condition is calculated using the voltage-current working condition characteristic boundary data, and the result is passed to the resistance working condition characteristic model as the initial condition. Then, combined with the line resistance working condition characteristic boundary data, the resistance change in the line is dynamically simulated, and the distribution characteristics of current density and thermal effect are analyzed. By superimposing the simulation results of voltage and resistance, the stress concentration area and dynamic response characteristics under the extreme working condition are extracted. The step-by-step convergence method is used to optimize the simulation accuracy, and the output line extreme operating condition response data records in detail the physical state and dynamic response characteristics of the battery line under extreme conditions, providing an important basis for the operating safety assessment of the battery system.

[0118] Preferably, step S244 includes the following steps:

[0119] Based on the limit state instability reconstruction data, the voltage-current behavior dynamic behavior data and the line resistance fluctuation mapping data are used to extract the voltage-current instability state data and the resistance instability state data, and the voltage-current instability state data and the resistance instability state data are obtained respectively;

[0120] Perform dynamic impedance phase decoupling according to the voltage-current instability data and the resistance instability state data to obtain dynamic impedance phase decoupling data;

[0121] Perform microwave conduction effect analysis on dynamic impedance phase decoupling data to obtain microwave conduction effect data;

[0122] According to the microwave conduction effect data, the electric field disturbance boundary analysis is performed on the voltage-current instability data and the resistance instability state data to obtain the electric field disturbance boundary data;

[0123] Based on the electric field disturbance boundary data and microwave conduction effect data, the voltage-current instability data and resistance instability state data are subjected to operating condition boundary analysis, and the voltage-current operating condition boundary data and line resistance operating condition boundary data are obtained respectively.

[0124] In the embodiment of the present invention, based on the reconstruction data of the limit state instability, the voltage-current behavior dynamic behavior data and the line resistance fluctuation mapping data are first preliminarily processed and decomposed into time series and frequency domain features. By analyzing the voltage change and current response, the instantaneous frequency and amplitude in the signal are extracted using the Hilbert-Huang transform (HHT) to identify the voltage-current instability state. When the voltage fluctuation amplitude exceeds the preset threshold, or the current fluctuates violently, it is marked as an instability state, thereby extracting the voltage-current instability data. For the line resistance fluctuation mapping data, the high-frequency component of the resistance is separated by Fourier transform, and the area where significant changes occur during the instability is extracted in combination with the change law of the resistance value, and the resistance instability state data is obtained. The sliding window method is used to analyze the time-varying characteristics of the data throughout the process, and the noise is eliminated by signal smoothing in the sliding window. When the dynamic impedance phase decoupling is performed according to the voltage-current instability data and the resistance instability state data, the impedance calculation formula is first used to decompose the phase difference between the voltage and the current into two parts: static impedance and dynamic impedance. The decoupling method is applied to the phase difference between the voltage and current signals, and it is synchronously analyzed with the line resistance fluctuation mapping data. In this process, the dynamic impedance is decomposed into amplitude and phase parts through polar coordinate transformation on the complex plane, and the relationship between phase change and current and voltage is analyzed in detail. The decoupled data is used to accurately describe the dynamic response between voltage and current, and the dynamic impedance phase decoupling data is obtained. The calculation in this process relies on fast Fourier transform (FFT) to improve the calculation efficiency, and combines multi-point sampling technology to ensure data accuracy. When analyzing the microwave conduction effect of the dynamic impedance phase decoupling data, the time-varying response between voltage and current is first measured by time domain reflectometry (TDR) to analyze the current conduction characteristics at microwave frequencies. On this basis, the transmission model of microwave signals is used to simulate the distribution and conduction effect of the electric field in the line, and the propagation speed, reflection and attenuation characteristics of microwaves in the conductor are calculated by the incremental method. Through parametric analysis, the spectrum of the voltage-current signal is matched with the conduction effect of microwave transmission, and the special behavior of microwave conduction in the unstable state is identified. Based on this analysis, microwave conduction effect data is obtained, which includes information such as conductivity change, propagation loss and signal attenuation, which is used to further optimize the conductivity analysis in the battery detection process. According to the microwave conduction effect data, when performing electric field perturbation boundary analysis on the voltage-current instability data and the resistance instability state data, the boundary conditions of the electric field perturbation are first established based on the spatial distribution model of the electric field. Combined with the changes in voltage and current, the intensity distribution of the electric field is calculated by Gauss's theorem, and the range of change of the electric field during instability is defined by the boundary conditions. The finite difference method (FDM) is used to simulate the distribution of the electric field under different working conditions, focusing on analyzing the perturbation effect when the electric field intensity exceeds the specified threshold. By monitoring and analyzing the electric field disturbance area, the electric field disturbance boundary data is extracted to reflect the dynamic response of the electric field under the working state of the battery line.Based on the electric field perturbation boundary data and microwave conduction effect data, when analyzing the operating condition boundary of the voltage-current instability data and the resistance instability state data, the maximum response value of the voltage-current instability state is first set to define the limit values ​​of voltage and current under instability. For the resistance instability state, the upper and lower limits of the operating condition are set by the fluctuation amplitude of the resistance value and its rate of change. The electric field perturbation boundary data is adjusted by regularization technology to adapt it to the changes in dynamic working conditions. The voltage, current and resistance data are fitted by nonlinear regression method to obtain more accurate operating condition boundary. In this process, the thermal effect and load response of the battery under different working conditions are also taken into account to generate voltage-current operating condition boundary data and line resistance operating condition boundary data, providing the necessary operating condition judgment basis for the battery detection method.

[0125] Preferably, step S3 comprises the following steps:

[0126] Step S31: normalizing the line extreme operating condition response data to obtain normalized line extreme operating condition response data;

[0127] Step S32: performing battery cycle attenuation gradient simulation on the voltage-current behavior dynamic behavior data and the line resistance fluctuation mapping data according to the line extreme working condition response normalization data to obtain the extreme working condition battery cycle attenuation gradient data;

[0128] Step S33: performing spatial interpolation processing on the extreme operating condition battery cycle attenuation gradient data to obtain extreme cycle attenuation gradient interpolation data;

[0129] Step S34: Perform cycle life prediction on the limit cycle attenuation gradient interpolation data based on a logistic regression algorithm to obtain battery limit cycle life data.

[0130] In an embodiment of the present invention, when normalizing the line limit condition response data, the obtained line limit condition response data is first preliminarily screened to remove abnormal data points and noise signals to ensure the accuracy of the data. All data are mapped to the interval [0,1] through the minimum-maximum normalization method. This process first determines the minimum and maximum values ​​of the data. For data with different dimensions, the normalized data will maintain its original proportional relationship while eliminating the impact of the dimension, so that the data can be subsequently analyzed at the same scale. The normalized line limit condition response data can effectively reduce the calculation error caused by unit differences or excessive ranges, laying the foundation for the next step of data processing. Based on the normalized line limit condition response data, when simulating the battery cycle attenuation gradient for the voltage-current behavior dynamic behavior data and the line resistance fluctuation mapping data, a battery cycle attenuation model is first constructed. The model calculates the cycle attenuation gradient based on the voltage, temperature and load conditions of the battery under different working conditions, combined with the chemical reaction characteristics of the battery, using a theoretical formula based on life attenuation. When performing spatial interpolation processing on the cycle attenuation gradient data of the battery under extreme working conditions, the spatial distribution of the data points is first analyzed to determine the sampling interval of the data set and its position in the spatial coordinates. The irregularly distributed attenuation gradient data is smoothed by Lagrange interpolation or spline interpolation. When predicting the battery cycle life based on the extreme cycle attenuation gradient interpolation data, the logistic regression algorithm is used to model the data. First, the attenuation gradient data is integrated with the historical usage data (such as the number of cycles, charge and discharge status, etc.) and fitted by a logistic regression model. The goal of the logistic regression model is to predict the future life of the battery based on its attenuation pattern in different cycles.

[0131] Preferably, step S32 includes the following steps:

[0132] Step S321: discretize the normalized data of the line extreme operating condition response to obtain discrete extreme operating condition response data;

[0133] Step S322: extracting the voltage-current dynamic behavior data in a dynamic hierarchical distribution according to the discrete limit condition response data to obtain voltage-current dynamic hierarchical distribution data;

[0134] Step S323: performing multi-dimensional electrochemical impedance coupling on the line resistance fluctuation mapping data according to the voltage-current dynamic hierarchical distribution data to obtain electrochemical impedance coupling data;

[0135] Step S324: Perform battery cycle attenuation gradient simulation based on electrochemical impedance coupling data to obtain extreme operating condition battery cycle attenuation gradient data.

[0136] In an embodiment of the present invention, when discretizing the normalized data of the line extreme condition response, a piecewise discretization method is first selected to group the normalized data according to a fixed interval or a specific distribution threshold. Taking equal-width binning as an example, the interval length of the grouping is determined according to the maximum and minimum values ​​of the data. In the binning process, each data point belongs to the corresponding interval according to its value and is represented by an interval identifier. For non-uniformly distributed response data, a quantile binning method can also be used to group the data according to the quantile of the cumulative distribution, so as to more accurately reflect the distribution characteristics of the response data. Through discretization processing, the complexity of the data can be effectively reduced and structured input data can be provided for subsequent dynamic hierarchical distribution extraction. The discretized data is named discrete extreme condition response data. When the dynamic hierarchical distribution of the voltage-current behavior dynamic behavior data is extracted according to the discrete extreme condition response data, a multidimensional conditional clustering method is used. First, the discrete extreme condition response data is used as the classification basis to divide the voltage-current behavior dynamic behavior data into multiple subsets. Then, the probability distribution function of the voltage and current is calculated in each subset. During the extraction process, the voltage and current characteristics of different layers are described by constructing a dynamic hierarchical distribution matrix, and the voltage-current dynamic hierarchical distribution data is generated. This distribution describes the statistical characteristics of the voltage-current behavior under different line operating conditions, laying the foundation for the subsequent electrochemical impedance analysis. According to the voltage-current dynamic hierarchical distribution data, the impedance calculation method of the equivalent circuit model is used to perform multi-dimensional electrochemical impedance coupling processing on the line resistance fluctuation mapping data. Subsequently, the spatial distribution of the total impedance is calculated using the voltage-current distribution matrix and the line resistance fluctuation mapping data, and the impedances of different layers are coupled to the three-dimensional spatial model. The coupling process is achieved through numerical integration to ensure that each layer impedance is strictly associated with the corresponding voltage-current characteristics, and finally generate electrochemical impedance coupling data. This data reflects the impedance characteristic distribution of the line under complex dynamic behavior and its law of change with operating conditions. During the simulation process, by adjusting the relationship between the impedance change rate and the number of cycles, time series data reflecting the battery attenuation characteristics are generated. Combined with different operating conditions, the calculated gradient change describes the capacity attenuation trend of the battery under extreme conditions, and finally generates the extreme condition battery cycle attenuation gradient data, which provides a key basis for battery performance evaluation and life prediction.

[0137] Preferably, step S34 includes the following steps:

[0138] Step S341: extracting feature vectors from the limit cycle attenuation gradient interpolation data to obtain cycle attenuation feature vectors;

[0139] Step S342: performing time series trend analysis on the cyclic decay feature vector to obtain cyclic decay time series trend data;

[0140] Step S343: performing probability distribution fitting on the cyclic decay time series trend data to obtain cyclic decay probability distribution data;

[0141] Step S344: Perform cycle life prediction on the cycle attenuation probability distribution data based on a logistic regression algorithm to obtain battery limit cycle life data.

[0142] In an embodiment of the present invention, the limit cycle attenuation gradient interpolation data is processed as input. The limit cycle attenuation gradient interpolation data is usually obtained by interpolating the battery cycle attenuation data under different working conditions to ensure the smoothness and consistency of the data. Next, the key feature vector is extracted from the interpolation data using feature extraction technology. The specific method is to use dimensionality reduction techniques such as principal component analysis (PCA) or linear discriminant analysis (LDA) to analyze the data change trend in each attenuation cycle. Through these techniques, the main changing factors in the attenuation process can be identified and converted into several representative feature vectors. The extraction of feature vectors is usually based on the variance or information amount of the data, and the features that have a greater impact on the battery attenuation process are selected as the main input parameters. When extracting features, focus on the voltage and current changes of the battery under extreme working conditions and the related electrochemical characteristics, such as the influence of factors such as battery internal temperature and impedance on the attenuation rate. These feature vectors provide an accurate data basis for subsequent attenuation trend analysis. After obtaining the cycle attenuation feature vector, a time series trend analysis is performed to reveal the temporal law of battery cycle attenuation. At this point, time series analysis methods, such as autoregressive moving average model (ARMA) or long short-term memory network (LSTM) and other technologies, are used to further process the feature vector. First, the decay feature vectors of the battery are arranged in chronological order to form a time series data set. Next, the data changes at each time point are analyzed, especially the fluctuations of the decay rate in different time periods. Through time series analysis, trend changes in the decay process can be extracted, such as the aggravation or slowing down of the decay rate of the battery in certain cycles. The goal of this step is to find the key factors affecting the battery life by analyzing the trend of the time series and form specific decay trend data. These time series trend data not only reflect the overall change trajectory of battery performance decay, but also reveal the regularity of battery performance changes under different working conditions, providing an important basis for subsequent probability distribution fitting and life prediction. After obtaining the cyclic decay time series trend data, the goal of this step is to convert these data into a probability distribution form that is easier to handle. First, the data is fitted using classical statistical methods such as least squares or maximum likelihood estimation. By fitting the time series data, the probability distribution of the decay rate at each time point in the battery decay process can be obtained. Depending on the attenuation characteristics, appropriate probability distribution models will be selected, such as normal distribution, exponential distribution or Weibull distribution. These distribution models can effectively describe the different changing trends that occur during battery attenuation. Specifically, the time series data are first grouped, and similar attenuation rates are classified into one group. Then, the optimal probability distribution parameters are determined by fitting these data points. In this process, improving the fitting accuracy requires optimizing parameter selection through methods such as cross-validation or residual analysis.The fitted probability distribution data can accurately reflect the randomness and uncertainty in the attenuation process and provide a theoretical basis for subsequent cycle life prediction. Based on the obtained cycle attenuation probability distribution data, the battery limit cycle life prediction is carried out. In order to make accurate life prediction, the attenuation probability distribution data is modeled using a logistic regression algorithm. The specific steps include first standardizing the probability distribution data and normalizing the parameters of different attenuation models (such as attenuation rate, attenuation amplitude, etc.) to the same scale for further analysis. Then, a logistic regression model is constructed, in which the attenuation probability distribution data of the battery is used as the input feature and the battery life (or number of cycles) is used as the output target. By training the model, a set of weight coefficients is obtained to represent the degree of influence of different attenuation features on the battery life. During the training process, the training set and the validation set can be used for multiple rounds of iterations to optimize the model parameters and make the prediction results more accurate. The advantage of logistic regression is that it can provide the contribution of each input feature, helping to understand which attenuation factors have a greater impact on the battery life. Finally, after training and verification, the model can output the battery's limit cycle life data, which will help determine whether the battery needs maintenance or replacement and provide a decision-making basis for the use and management of the battery.

[0143] Preferably, the present invention further provides a battery detection system for executing the battery detection method as described above, the battery detection system comprising:

[0144] The voltage-current capture module is used to collect data on the battery operation status to obtain battery operation status data; the voltage-current dynamic behavior capture of the battery operation status cleaning data is performed to obtain voltage-current dynamic behavior data;

[0145] The extreme working condition response simulation module is used to map the line resistance fluctuation according to the voltage-current behavior dynamic behavior data to obtain the line resistance fluctuation mapping data; perform extreme working condition response simulation according to the line resistance fluctuation mapping data to obtain the line extreme working condition response data;

[0146] The cycle life prediction module is used to simulate the battery cycle attenuation gradient according to the line extreme operating condition response data to obtain the extreme operating condition battery cycle attenuation gradient data; based on the logistic regression algorithm, the cycle life is predicted for the extreme operating condition battery cycle attenuation gradient data to obtain the battery extreme cycle life data.

[0147] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0148] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A battery detection method, characterized in that: The following steps are involved: Step S1: collecting data on the battery operating status to obtain battery operating status data; Capturing the voltage-current dynamic behavior of the battery operation status cleaning data to obtain voltage-current dynamic behavior data; Step S2: mapping the line resistance fluctuation according to the voltage-current behavior dynamic behavior data to obtain line resistance fluctuation mapping data; simulating the extreme working condition response according to the line resistance fluctuation mapping data to obtain the line extreme working condition response data; Step S3: Perform battery cycle attenuation gradient simulation according to the line extreme operating condition response data to obtain extreme operating condition battery cycle attenuation gradient data; perform cycle life prediction on the extreme operating condition battery cycle attenuation gradient data based on a logistic regression algorithm to obtain battery extreme cycle life data.

2. The battery detection method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: collecting data on the battery operating status to obtain battery operating status data; Step S12: Cleaning the battery operation status data to obtain battery operation status cleansing data; Step S13: Timestamp the battery operation status cleaning data to obtain battery operation status timestamp data; Step S14: capturing the voltage-current dynamic behavior of the battery operation status cleaning data according to the battery operation status time stamp data to obtain voltage-current dynamic behavior data.

3. The battery detection method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: obtaining the internal circuit connection structure of the battery; Step S22: mapping the line resistance fluctuation of the internal line connection structure of the battery according to the voltage-current behavior dynamic behavior data to obtain line resistance fluctuation mapping data; Step S23: performing line fluctuation working condition stress assessment on the line resistance fluctuation mapping data to obtain line fluctuation working condition stress data; Step S24: performing extreme operating condition response simulation on the line fluctuation operating condition stress data according to the voltage-current behavior dynamic behavior data and the line resistance fluctuation mapping data to obtain the line extreme operating condition response data.

4. The battery detection method according to claim 3, characterized in that: Step S23 includes the following steps: Step S231: performing point-by-point fluctuation amplitude statistical analysis on the line resistance fluctuation mapping data to obtain resistance fluctuation amplitude distribution data; Step S232: quantifying the interval difference between nodes based on the resistance fluctuation amplitude distribution data to obtain the node fluctuation difference interval; Step S233: performing line pressure thermal effect distribution analysis on the line resistance fluctuation mapping data based on the node fluctuation difference interval to obtain thermal effect pressure distribution data; Step S234: performing high and low pressure dynamic response analysis on the thermal effect pressure distribution data according to the voltage-current behavior dynamic behavior data to obtain thermal effect voltage response data; Step S235: performing point-by-point load strain analysis on the line according to the thermal effect pressure distribution data and the thermal effect voltage response data to obtain point-by-point load strain data on the line; Step S236: Perform line fluctuation condition stress assessment based on the thermal effect pressure distribution data, the thermal effect voltage response data, and the line point-by-point load strain data to obtain line fluctuation condition stress data.

5. The battery detection method according to claim 3, characterized in that: Step S24 includes the following steps: Step S241: performing composite feature interaction analysis on the voltage-current behavior dynamic behavior data and the line resistance fluctuation mapping data to obtain composite feature interaction data; Step S242: performing parameter instability analysis on the composite feature interaction data to obtain feature parameter instability data; Step S243: performing limit state instability reconstruction according to characteristic parameter instability data to obtain limit state instability reconstruction data; Step S244: performing operating condition boundary analysis on the voltage-current behavior dynamic behavior data and the line resistance fluctuation mapping data based on the limit state instability reconstruction data, and obtaining voltage-current operating condition boundary data and line resistance operating condition boundary data respectively; Step S245: Perform extreme operating condition response simulation according to the voltage-current operating condition characteristic boundary data and the line resistance operating condition characteristic boundary data to obtain line extreme operating condition response data.

6. The battery detection method according to claim 5, characterized in that: Step S244 includes the following steps: Based on the limit state instability reconstruction data, the voltage-current behavior dynamic behavior data and the line resistance fluctuation mapping data are used to extract the voltage-current instability state data and the resistance instability state data, and the voltage-current instability state data and the resistance instability state data are obtained respectively; Perform dynamic impedance phase decoupling according to the voltage-current instability data and the resistance instability state data to obtain dynamic impedance phase decoupling data; Perform microwave conduction effect analysis on dynamic impedance phase decoupling data to obtain microwave conduction effect data; According to the microwave conduction effect data, the electric field disturbance boundary analysis is performed on the voltage-current instability data and the resistance instability state data to obtain the electric field disturbance boundary data; Based on the electric field disturbance boundary data and microwave conduction effect data, the voltage-current instability data and resistance instability state data are subjected to operating condition boundary analysis, and the voltage-current operating condition boundary data and line resistance operating condition boundary data are obtained respectively.

7. The battery detection method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: normalizing the line extreme operating condition response data to obtain normalized line extreme operating condition response data; Step S32: performing battery cycle attenuation gradient simulation on the voltage-current behavior dynamic behavior data and the line resistance fluctuation mapping data according to the line extreme working condition response normalization data to obtain the extreme working condition battery cycle attenuation gradient data; Step S33: performing spatial interpolation processing on the extreme operating condition battery cycle attenuation gradient data to obtain extreme cycle attenuation gradient interpolation data; Step S34: Perform cycle life prediction on the limit cycle attenuation gradient interpolation data based on a logistic regression algorithm to obtain battery limit cycle life data.

8. The battery detection method according to claim 7, characterized in that: Step S32 includes the following steps: Step S321: discretizing the normalized data of the line extreme operating condition response to obtain discrete extreme operating condition response data; Step S322: extracting the voltage-current dynamic behavior data in a dynamic hierarchical distribution according to the discrete limit condition response data to obtain voltage-current dynamic hierarchical distribution data; Step S323: performing multi-dimensional electrochemical impedance coupling on the line resistance fluctuation mapping data according to the voltage-current dynamic hierarchical distribution data to obtain electrochemical impedance coupling data; Step S324: Perform battery cycle attenuation gradient simulation based on electrochemical impedance coupling data to obtain extreme operating condition battery cycle attenuation gradient data.

9. The battery detection method according to claim 7, characterized in that: Step S34 includes the following steps: Step S341: extracting feature vectors from the limit cycle attenuation gradient interpolation data to obtain cycle attenuation feature vectors; Step S342: performing time series trend analysis on the cyclic decay feature vector to obtain cyclic decay time series trend data; Step S343: performing probability distribution fitting on the cyclic decay time series trend data to obtain cyclic decay probability distribution data; Step S344: Perform cycle life prediction on the cycle attenuation probability distribution data based on a logistic regression algorithm to obtain battery limit cycle life data.

10. A battery detection system, characterized in that: Used to perform the battery detection method as claimed in claim 1, the battery detection system comprises: The voltage-current capture module is used to collect data on the battery operation status to obtain battery operation status data; the voltage-current dynamic behavior capture of the battery operation status cleaning data is performed to obtain voltage-current dynamic behavior data; The extreme working condition response simulation module is used to map the line resistance fluctuation according to the voltage-current behavior dynamic behavior data to obtain the line resistance fluctuation mapping data; perform extreme working condition response simulation according to the line resistance fluctuation mapping data to obtain the line extreme working condition response data; The cycle life prediction module is used to simulate the battery cycle attenuation gradient according to the line extreme operating condition response data to obtain the extreme operating condition battery cycle attenuation gradient data; based on the logistic regression algorithm, the cycle life is predicted for the extreme operating condition battery cycle attenuation gradient data to obtain the battery extreme cycle life data.

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