Method for evaluating short circuit in battery

Abstract

A battery internal short circuit evaluation method comprises a data acquisition module, a data preprocessing module, a feature extraction and fusion module, a parameter and consumption effect analysis module, a fusion diagnosis module and a verification and optimization module, the data acquisition module is used for battery data acquisition, and the data preprocessing module is used for data preprocessing. The feature extraction and fusion module is used for extracting and fusing parameter features and consumption features, the parameter and consumption effect analysis module is used for analyzing parameter effects and consumption effects, the fusion diagnosis module is used for diagnosing the short circuit state in the battery, and the verification and optimization module is used for verifying and optimizing the evaluation method of the short circuit in the battery. The invention provides a multi-source dynamic collaborative enhanced battery internal short circuit monitoring algorithm for feature extraction and feature fusion, provides a battery internal short circuit third-order collaborative analysis algorithm for parameter effect analysis, provides a battery internal short circuit multi-scale dynamic coupling analysis algorithm for consumption effect analysis, and provides a better scheme for a battery internal short circuit evaluation method.

Description

Technical Field

[0001] The invention relates to the fields of battery safety monitoring and multi-physics field coupling analysis, and in particular to a method for evaluating internal short circuits in batteries. Background Art

[0002] Battery safety monitoring technology refers to a technical system that achieves early warning and accurate diagnosis of internal short-circuit safety risks in batteries by real-time collection and analysis of multi-dimensional physical parameters of batteries. Its core goal is to identify signs of micro-short circuits before thermal runaway occurs, thereby gaining a critical time window for safety protection. Battery safety monitoring technology includes traditional experimental simulation methods (such as mechanical puncture tests, overcharge / over-discharge tests), simulation modeling technologies (such as multi-physics field coupling models), and early warning algorithms (such as those based on voltage / temperature thresholds and electrochemical impedance spectroscopy). The mutual collaboration of these technologies provides a more efficient monitoring method for internal short-circuit monitoring in batteries.

[0003] Multi-physics coupling analysis technology is an interdisciplinary method that studies the interaction of multiple physical processes (electricity, heat, and force) within the battery through mathematical modeling and simulation. In the assessment of internal short circuits in batteries, its core value is to reveal the fault evolution mechanism, achieve precise positioning, and break through the limitations of a single field. Multi-physics coupling analysis technology includes pure electrochemical models, thermal abuse models, electrical-thermal coupling models, and thermal-mechanical coupling models. The mutual collaboration of these technologies provides a more accurate analysis method for the assessment and analysis of internal short circuits in batteries. Summary of the Invention

[0004] In view of the above problems, the present invention aims to provide a method for evaluating short circuit in a battery.

[0005] To achieve the above-mentioned objectives, the present invention provides the following technical solutions: a method for evaluating internal short circuits in batteries, comprising a data acquisition module, a data preprocessing module, a feature extraction and fusion module, a parameter and consumption effect analysis module, a fusion diagnosis module and a verification and optimization module, wherein the data acquisition module is used to collect high-precision electrical parameters, thermal parameters and mechanical parameters, the data preprocessing module is used to preprocess the collected multiple data, the feature extraction and fusion module proposes a multi-source dynamic collaborative enhanced internal short circuit monitoring algorithm for battery to extract and fuse parameter features and consumption features respectively, the parameter and consumption effect analysis module includes a parameter effect analysis unit and a consumption effect analysis unit, the parameter effect analysis unit proposes a third-order collaborative analysis algorithm for internal short circuits in batteries to analyze parameter effects, the consumption effect analysis unit proposes a multi-scale dynamic coupling analysis algorithm for internal short circuits in batteries to analyze consumption effects, the fusion diagnosis module is used to fuse the parameter effect analysis results and the consumption effect analysis results to diagnose the internal short circuit status of the battery, and the verification and optimization module is used to verify and optimize the evaluation method for internal short circuits in batteries.

[0006] Furthermore, the data acquisition module is used to collect high-precision electrical parameters, thermal parameters and mechanical parameters, collect single-cell terminal voltage data through 16-bit ADC, collect current data through shunt + Hall sensor dual-mode detection, collect impedance spectrum data through online EIS technology, collect temperature data through NTC thermistor, and collect battery expansion force change data through micro strain gauges mounted on the surface of the battery shell.

[0007] Furthermore, the data preprocessing module performs noise reduction preprocessing on the collected electrical parameters through wavelet packet transform, performs local abnormal heating capture preprocessing on the collected thermal parameters through spatial gradient calculation, and performs signal correction preprocessing on the mechanical parameters through temperature drift compensation.

[0008] Furthermore, the feature extraction and fusion module proposes a multi-source dynamic collaborative enhanced battery short-circuit monitoring algorithm to extract and fuse parameter features and consumption features respectively.

[0009] Furthermore, the multi-source dynamic collaborative enhanced battery internal short circuit monitoring algorithm is as follows: Assuming that the collected multi-source data is ,in, is the electrical parameter data, that is ,in, This is the first electrical parameter data. For the second electrical parameter data, For the Electrical parameter data, is the thermal parameter data, that is ,in, is the first thermal parameter data, is the second thermal parameter data, For the Thermal parameter data, is the mechanical parameter data, that is ,in, For the first mechanical parameter data, For the second mechanical parameter data, For the pieces of mechanical parameter data, assuming the sampling period is , then the multi-source data after sampling is ,in, is the battery multi-source data matrix after sampling, For the The electrical parameter data of the subsamples, For the subsampled thermal parameter data, For the In order to improve the signal-to-noise ratio of micro-short circuit transient characteristics, the enhanced differential operation is proposed to enhance the transient feature extraction capability. The enhanced differential operation is , ,in, For data in The subsampled differential eigenvalues, To eliminate the influence of sampling period, the standardization coefficient is: is the difference operator, for The difference operation of For the The electrical parameter data after sampling, For the The electrical parameter data after sampling, For the The electrical parameter data after sampling, For the The electrical parameter data after sampling is used to achieve feature weight adaptation. The SOH correlation function is proposed to improve the contribution of key parameters. The multi-physics field weighted energy is , , ,in, Weighted energy for multiphysics, is the weight coefficient of the electrical parameters, is the weight coefficient of thermal parameters, is the weight coefficient of the mechanical parameters, is a space vector, is the mechanical parameter data after differentiation, is the dynamic weight coefficient, is the weight decay slope, For the In order to quantify the disorder of the signal, it is proposed to change the fixed reference to the sliding mean to reduce the influence of baseline drift caused by cycle aging. Therefore, the multimodal data feature extraction is ,in, For the The information entropy eigenvalue of the subsampling is is the sliding window size, is the offset index within the window, is the historical sampling data, is the sliding window mean, To calculate the squared deviation of each data point from the window mean;

[0010] Then, feature fusion processing is performed. In order to capture the collaborative anomalies of multiple physical fields, multimodal data frequency domain coupling analysis is proposed to compress the amount of calculation. The coupling power spectrum density is ,in, is the coupled power spectral density, is the upper limit of the fault-sensitive frequency band, is the lower limit of the fault sensitive frequency band, is the fast Fourier transform, is the frequency independent variable, is the spatial gradient, is the change in mechanical parameter data of adjacent sampling points, As a differential operator, in order to enhance the early signal characteristics, it is proposed to improve the fixed bandwidth into aging-related dynamic focusing to increase the sensitivity of the spectrum width to weak faults. The dynamic focusing bandwidth is ,in, is the dynamic focusing bandwidth, is the center frequency of the spectrum, is the nonlinear attenuation factor, is the cumulative history of voltage changes, that is, ,in, For the The characteristic voltage change of the sub-cycle is For the absolute value of voltage change, the multi-source dynamic collaborative enhanced battery short-circuit monitoring algorithm first proposes enhanced differential operation to enhance the transient feature extraction capability, then proposes the SOH correlation function to achieve feature weight adaptation, and then proposes to change the fixed benchmark to the sliding mean to reduce the impact of baseline drift caused by cycle aging, and then proposes multimodal data frequency domain coupling analysis and improves the fixed bandwidth to aging-related dynamic focusing for feature fusion, realizing the extraction and fusion of battery parameter features and consumption features.

[0011] Furthermore, the parameter effect analysis unit proposes a third-order collaborative analysis algorithm for internal short circuit of the battery to analyze the parameter effect.

[0012] Furthermore, the third-order collaborative analysis algorithm for internal short circuit of battery is as follows: In order to solve the problem that the traditional bottom-order differential is insensitive to weak signals, the third-order differential feature enhancement is proposed to improve the sensitivity to weak signals. The third-order differential feature value is ,in, is the third-order differential eigenvalue, that is, the battery multi-source data at time The transient change intensity, For the subsampling, is the sampling period, is the standardized coefficient of the third-order differential method, for The third-order differential operation of , For the The electrical parameter data of the subsamples, For the Sub-sampled battery multi-source data, For the Sub-sampled battery multi-source data, For the To solve the problem of single parameter misreporting, a multi-physics field collaborative verification is proposed to realize short circuit location based on the multi-source battery data of the sub-sample. The verification result is calculated as ,in, The verification result of the calculation is is the basic threshold of the battery, is the norm of the temperature gradient, For the mechanical parameter data after differentiation, in order to achieve the threshold adjustment of the whole life cycle, an aging adaptive decision is proposed, namely ,in, is the aging adaptive threshold, is the aging sensitivity coefficient, The voltage change history accumulation amount, the voltage decay rate after charging is calculated as follows: ,in, is the decay rate factor, The charging end time is The moment of relaxation ends, is the voltage change rate, is the absolute value of the voltage change rate, and a three-dimensional thermal model is constructed to perform three-dimensional thermal residual analysis, that is, ,in, is the length direction of the battery, In the battery width direction, is the thickness direction of the battery, is the temperature residual, is the measured temperature, is the thermoelectric coupling coefficient, is the operating current, The spatial thermal resistance distribution is then monitored by online EIS and the characteristic time constant is identified by DRT. The impedance DRT analysis is calculated as , ,in, is the characteristic time constant of the SEI film, is the measured impedance spectrum, is the time constant, is the distribution relaxation time function, is the model impedance spectrum, is the L2 norm, For the regularization coefficient, the third-order collaborative analysis algorithm for internal battery short circuits first proposes third-order differential feature enhancement to improve the sensitivity to weak signals, then proposes multi-physical field collaborative verification to achieve short circuit positioning, and then proposes aging adaptive decision-making to achieve full life cycle threshold adjustment. Finally, the voltage decay rate is calculated, the three-dimensional thermal model is constructed, and the characteristic time constant is calculated through DRT identification, so as to achieve accurate analysis of parameter effects.

[0013] Furthermore, the consumption effect analysis unit proposes a multi-scale dynamic coupling analysis algorithm for internal short circuit of the battery to analyze the consumption effect.

[0014] Furthermore, the multi-scale dynamic coupling analysis algorithm for internal short circuit of the battery is as follows: The Coulomb efficiency of each cycle is calculated as , ,in, For the The coulombic efficiency of the cycle, is the discharge capacity, is the charging capacity, is the capacity attenuation, is the current full charge capacity, is the initial full charge capacity. To identify the loss of active material, the differential capacity is used for calculation and analysis, i.e. , ,in, is the differential capacity, For the discrete capacity points, For the discrete capacity points, For the discrete voltage points, For the discrete voltage points, is the characteristic peak voltage offset, is the current cycle peak voltage, is the initial reference peak voltage, and under the static condition of ambient temperature, the abnormal loss is calculated by the energy balance equation, that is, , ,in, is the self-discharge rate, is the starting time of standing still, The end time of stillness, is the rate of change of voltage with time, is the energy loss, is the equivalent leakage current, is the electrical parameter data, and the OCV-SOC relationship changes to ,in, is the OCV curve offset integral, is the current open circuit voltage-SOC curve, For reference OCV-SOC curve, For the charging state, the multi-scale dynamic coupling analysis algorithm of internal short circuit of the battery first proposes a Coulomb efficiency calculation method to monitor the capacity attenuation, then proposes differential capacity analysis to identify the loss of active materials, and then proposes abnormal loss calculation through the energy balance equation. Finally, the change of OCV-SOC relationship is proposed to diagnose the consumption effect, so as to achieve accurate analysis of the consumption effect.

[0015] Furthermore, the fusion diagnosis module is used to fuse the parameter effect analysis results and the consumption effect analysis results to diagnose the short-circuit status of the battery. First, a correlation matrix containing all characteristic parameters is constructed, and the improved DS evidence theory is used to process uncertain information. Decision fusion is achieved through fuzzy logic reasoning. The early warning system implements a three-level response mechanism: when a single parameter exceeds the threshold, a first-level warning is triggered; when multiple parameters are abnormal, it is upgraded to a second-level warning; only when parameter abnormalities and consumption abnormalities occur at the same time can a third-level warning be triggered; the verification and optimization module is used to verify and optimize the evaluation method of battery internal short circuits. The system continuously optimizes performance through closed-loop verification. Under controllable laboratory conditions, internal short circuits of different severity are artificially created to verify the accuracy of the diagnostic algorithm. The accumulated fault cases will continuously enrich the sample library and gradually realize the optimization of the evaluation method.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] 1. A method for evaluating internal short circuits in batteries is provided, and a multi-source dynamic collaborative enhanced internal short circuit monitoring algorithm for batteries is proposed to extract and fuse parameter features and consumption features respectively. The innovation of the present invention lies in that the multi-source dynamic collaborative enhanced internal short circuit monitoring algorithm for batteries first proposes enhanced differential operations to enhance transient feature extraction capabilities, and then proposes an SOH correlation function to achieve feature weight adaptation, and then proposes changing the fixed benchmark to a sliding mean to reduce the impact of baseline drift caused by cycle aging, and then proposes multimodal data frequency domain coupling analysis and improves the fixed bandwidth to aging-related dynamic focusing for feature fusion, thereby realizing the extraction and fusion of battery parameter features and consumption features.

[0018] 2. A third-order collaborative analysis algorithm for internal battery short circuits is proposed to analyze parameter effects. The innovation of the present invention lies in that the third-order collaborative analysis algorithm for internal battery short circuits first proposes third-order differential feature enhancement to improve the sensitivity to weak signals, then proposes multi-physical field collaborative verification to achieve short circuit positioning, and then proposes aging adaptive decision-making to achieve full life cycle threshold adjustment. Finally, voltage decay rate calculation, three-dimensional thermal model construction and characteristic time constant calculation are performed through DRT identification to achieve accurate analysis of parameter effects.

[0019] 3. A multi-scale dynamic coupling analysis algorithm for internal short circuit of the battery is proposed to analyze the consumption effect. The innovation of the present invention lies in that the multi-scale dynamic coupling analysis algorithm for internal short circuit of the battery first proposes a Coulomb efficiency calculation method for capacity attenuation monitoring, then proposes differential capacity analysis to identify active material loss, and then proposes abnormal loss calculation through the energy balance equation, and finally proposes changes in the OCV-SOC relationship to diagnose the consumption effect, so as to achieve accurate analysis of the consumption effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The invention is further illustrated by the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the invention. A person skilled in the art can obtain other drawings based on the following drawings without making any creative effort.

[0021] Figure 1 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] A method for evaluating internal short circuits in batteries includes a data acquisition module, a data preprocessing module, a feature extraction and fusion module, a parameter and consumption effect analysis module, a fusion diagnosis module, and a verification and optimization module. The data acquisition module is used to acquire high-precision electrical parameters, thermal parameters, and mechanical parameters. The data preprocessing module is used to preprocess the acquired data. The feature extraction and fusion module proposes a multi-source dynamic collaborative enhanced internal short circuit monitoring algorithm for battery to extract and fuse parameter features and consumption features respectively. The parameter and consumption effect analysis module includes a parameter effect analysis unit and a consumption effect analysis unit. The parameter effect analysis unit proposes a third-order collaborative analysis algorithm for internal short circuits in batteries to analyze parameter effects. The consumption effect analysis unit proposes a multi-scale dynamic coupling analysis algorithm for internal short circuits in batteries to analyze consumption effects. The fusion diagnosis module is used to fuse parameter effect analysis results and consumption effect analysis results to diagnose the internal short circuit state of the battery. The verification and optimization module is used to verify and optimize the evaluation method for internal short circuits in batteries.

[0024] Preferably, the data acquisition module is used to collect high-precision electrical parameters, thermal parameters and mechanical parameters, collect single-cell terminal voltage data through a 16-bit ADC (±0.1mV accuracy), collect current data through shunt + Hall sensor dual-mode detection, collect impedance spectrum data through online EIS technology, collect temperature data through NTC thermistors, and collect battery expansion force change data through micro-strain gauges mounted on the surface of the battery casing.

[0025] Preferably, the data preprocessing module performs noise reduction preprocessing on the collected electrical parameters through wavelet packet transform (using Db6 wavelet basis for 5-layer decomposition, and high-frequency coefficient soft threshold processing to retain the effective frequency band of 0.01-1Hz), performs capture preprocessing of local abnormal heating on the collected thermal parameters through spatial gradient calculation, and performs signal correction preprocessing on the mechanical parameters through temperature drift compensation (eliminating pseudo strain signal data).

[0026] Preferably, the feature extraction and fusion module proposes a multi-source dynamic collaborative enhanced battery internal short circuit monitoring algorithm to extract and fuse parameter features and consumption features respectively.

[0027] Specifically, the multi-source dynamic collaborative enhanced battery internal short circuit monitoring algorithm is as follows: Assume that the collected multi-source data is ,in, is the electrical parameter data, that is ,in, This is the first electrical parameter data. For the second electrical parameter data, For the Electrical parameter data, is the thermal parameter data, that is ,in, is the first thermal parameter data, is the second thermal parameter data, For the Thermal parameter data, is the mechanical parameter data, that is ,in, For the first mechanical parameter data, For the second mechanical parameter data, For the pieces of mechanical parameter data, assuming the sampling period is , then the multi-source data after sampling is ,in, is the battery multi-source data matrix after sampling, For the The electrical parameter data of the subsamples, For the subsampled thermal parameter data, For the In order to improve the signal-to-noise ratio of micro-short circuit transient characteristics, the enhanced differential operation is proposed to enhance the transient feature extraction capability. The enhanced differential operation is , ,in, For data in The subsampled differential eigenvalues, To eliminate the influence of sampling period, the standardization coefficient is: is the difference operator, for The difference operation of For the The electrical parameter data after sampling, For the The electrical parameter data after sampling, For the The electrical parameter data after sampling, For the The differential operation of electrical parameter data, thermal parameter data and mechanical data after sampling is consistent with the differential operation process of electrical parameters. In order to achieve feature weight adaptation, the SOH correlation function is proposed to improve the contribution of key parameters. The multi-physics field weighted energy is , , ,in, Weighted energy for multiphysics, is the weight coefficient of the electrical parameters, is the weight coefficient of thermal parameters, is the weight coefficient of the mechanical parameters, is a space vector, is the mechanical parameter data after differentiation, is the dynamic weight coefficient, is the weight decay slope, For the In order to quantify the disorder of the signal, it is proposed to change the fixed reference to the sliding mean to reduce the influence of baseline drift caused by cycle aging. Therefore, the multimodal data feature extraction is ,in, For the The information entropy eigenvalue of the subsampling is is the sliding window size, is the offset index within the window, is the historical sampling data, is the sliding window mean, To calculate the squared deviation of each data point from the window mean;

[0028] Then, feature fusion processing is performed. In order to capture the collaborative anomalies of multiple physical fields, multimodal data frequency domain coupling analysis is proposed to compress the amount of calculation. The coupling power spectrum density is ,in, is the coupled power spectral density, is the upper limit of the fault-sensitive frequency band, is the lower limit of the fault sensitive frequency band, is the fast Fourier transform, is the frequency independent variable, is the spatial gradient, is the change in mechanical parameter data of adjacent sampling points, As a differential operator, in order to enhance the early signal characteristics, it is proposed to improve the fixed bandwidth into aging-related dynamic focusing to increase the sensitivity of the spectrum width to weak faults. The dynamic focusing bandwidth is ,in, is the dynamic focusing bandwidth, is the center frequency of the spectrum, is the nonlinear attenuation factor, is the cumulative history of voltage changes (reflecting aging consistency), that is, ,in, For the The characteristic voltage change of the sub-cycle is For the absolute value of voltage change, the multi-source dynamic collaborative enhanced battery short-circuit monitoring algorithm first proposes enhanced differential operation to enhance the transient feature extraction capability, then proposes the SOH correlation function to achieve feature weight adaptation, and then proposes to change the fixed benchmark to the sliding mean to reduce the impact of baseline drift caused by cycle aging, and then proposes multimodal data frequency domain coupling analysis and improves the fixed bandwidth to aging-related dynamic focusing for feature fusion, realizing the extraction and fusion of battery parameter features and consumption features.

[0029] Preferably, the parameter effect analysis unit proposes a third-order collaborative analysis algorithm for internal short circuit of the battery to analyze the parameter effect.

[0030] Specifically, the third-order collaborative analysis algorithm for internal short circuit of battery is as follows: In order to solve the problem that the traditional bottom-order differential is insensitive to weak signals, the third-order differential feature enhancement is proposed to improve the sensitivity to weak signals. The third-order differential eigenvalue is ,in, is the third-order differential eigenvalue, that is, the battery multi-source data at time The transient change intensity, For the subsampling, is the sampling period, is the standardized coefficient of the third-order differential method, for The third-order differential operation of , For the The electrical parameter data of the subsamples, For the Sub-sampled battery multi-source data, For the Sub-sampled battery multi-source data, For the To solve the problem of single parameter misreporting, a multi-physics field collaborative verification is proposed to realize short circuit location based on the multi-source battery data of the sub-sample. The verification result is calculated as ,in, The verification result of the calculation is is the basic threshold of the battery, is the norm of the temperature gradient, For the mechanical parameter data after differentiation, in order to achieve the threshold adjustment of the whole life cycle, an aging adaptive decision is proposed, namely ,in, is the aging adaptive threshold (dynamically relaxed with the degree of aging), is the aging sensitivity coefficient, The voltage change history accumulation amount, the voltage decay rate after charging is calculated as follows: ,in, is the decay rate factor, The charging end time is The moment of relaxation ends, is the voltage change rate, is the absolute value of the voltage change rate, and a three-dimensional thermal model is constructed to perform three-dimensional thermal residual analysis, that is, ,in, is the length direction of the battery (the axis from the negative electrode to the positive electrode), is the width direction of the battery (parallel to the transverse plane of the electrode), is the thickness direction of the battery (the vertical axis from the bottom of the shell to the top cover), is the temperature residual, is the measured temperature, is the thermoelectric coupling coefficient, is the operating current, The spatial thermal resistance distribution is then monitored by online EIS and the characteristic time constant is identified by DRT. The impedance DRT analysis is calculated as , ,in, is the characteristic time constant of the SEI film, is the measured impedance spectrum, is the time constant, is the distribution relaxation time function, is the model impedance spectrum, is the L2 norm, For the regularization coefficient, the third-order collaborative analysis algorithm for internal battery short circuits first proposes third-order differential feature enhancement to improve the sensitivity to weak signals, then proposes multi-physical field collaborative verification to achieve short circuit positioning, and then proposes aging adaptive decision-making to achieve full life cycle threshold adjustment. Finally, the voltage decay rate is calculated, the three-dimensional thermal model is constructed, and the characteristic time constant is calculated through DRT identification, so as to achieve accurate analysis of parameter effects.

[0031] Preferably, the consumption effect analysis unit proposes a multi-scale dynamic coupling analysis algorithm for internal short circuit of the battery to analyze the consumption effect.

[0032] Specifically, the multi-scale dynamic coupling analysis algorithm for internal short circuit of the battery is as follows: The Coulomb efficiency of each cycle is calculated as , ,in, For the The coulombic efficiency of the cycle, is the discharge capacity, is the charging capacity, is the capacity attenuation, is the current full charge capacity, is the initial full charge capacity. To identify the loss of active material, the differential capacity is used for calculation and analysis, i.e. , ,in, is the differential capacity, For the discrete capacity points, For the discrete capacity points, For the discrete voltage points, For the discrete voltage points, is the characteristic peak voltage offset, is the current cycle peak voltage, is the initial reference peak voltage, and under the static condition of ambient temperature, the abnormal loss is calculated by the energy balance equation, that is, , ,in, is the self-discharge rate, is the starting time of standing still, The end time of stillness, is the rate of change of voltage with time, is the energy loss, is the equivalent leakage current, is the electrical parameter data, and the OCV-SOC relationship changes to ,in, is the OCV curve offset integral, is the current open circuit voltage-SOC curve, For reference OCV-SOC curve, For the charging state, the multi-scale dynamic coupling analysis algorithm of internal short circuit of the battery first proposes a Coulomb efficiency calculation method to monitor the capacity attenuation, then proposes differential capacity analysis to identify the loss of active materials, and then proposes abnormal loss calculation through the energy balance equation. Finally, the change of OCV-SOC relationship is proposed to diagnose the consumption effect, so as to achieve accurate analysis of the consumption effect.

[0033] Preferably, the fusion diagnosis module is used to fuse the parameter effect analysis results and the consumption effect analysis results to diagnose the short circuit status of the battery. First, a correlation matrix containing all characteristic parameters is constructed, and the improved DS evidence theory is used to process uncertain information. Decision fusion is achieved through fuzzy logic reasoning. The early warning system implements a three-level response mechanism: when a single parameter exceeds the threshold, a first-level warning is triggered, and when multiple parameters are abnormal, it is upgraded to a second-level warning. Only when parameter abnormalities and consumption abnormalities occur at the same time (such as a sudden voltage drop accompanied by abnormal self-discharge) will a third-level warning be triggered; the verification and optimization module is used to verify and optimize the evaluation method of the battery short circuit. The system continuously optimizes performance through closed-loop verification. Under controllable laboratory conditions, internal short circuits of different severity are artificially created to verify the accuracy of the diagnosis algorithm. The accumulated fault cases will continuously enrich the sample library and gradually realize the optimization of the evaluation method.

[0034] A method for evaluating internal short circuits in batteries is proposed, which is used for early warning and accurate diagnosis of internal short circuits in batteries. By integrating a data acquisition module, a data preprocessing module, a feature extraction and fusion module, a parameter and consumption effect analysis module, a fusion diagnosis module and a verification and optimization module, a method for evaluating internal short circuits in batteries is provided. A multi-source dynamic collaborative enhanced internal short circuit monitoring algorithm for batteries is proposed to extract and fuse parameter features and consumption features respectively. The innovation of the present invention lies in that the multi-source dynamic collaborative enhanced internal short circuit monitoring algorithm for batteries first proposes enhanced differential operations to enhance the transient feature extraction capability, then proposes an SOH correlation function to achieve feature weight adaptation, then proposes changing the fixed benchmark to a sliding mean to reduce the impact of baseline drift caused by cycle aging, then proposes multimodal data frequency domain coupling analysis and improves the fixed bandwidth to aging-related dynamic focusing for feature fusion, realizing the extraction and fusion of battery parameter features and consumption features, and proposes a third-order collaborative analysis algorithm for internal short circuits in batteries to analyze parameter effects. The innovation of the present invention lies in that the third-order collaborative analysis algorithm for internal short circuits in batteries first proposes third-order differential feature enhancement to improve the sensitivity to weak signals, then proposes multi-objective feature extraction and fusion. The invention uses a multi-physics field collaborative verification to realize short circuit positioning, and then proposes an aging adaptive decision to realize full life cycle threshold adjustment. Finally, the voltage decay rate is calculated, the three-dimensional thermal model is constructed, and the characteristic time constant is calculated through DRT identification, so as to realize the accurate analysis of parameter effects, and a multi-scale dynamic coupling analysis algorithm for internal short circuit of battery is proposed to analyze the consumption effect. The innovation of the invention lies in that the multi-scale dynamic coupling analysis algorithm for internal short circuit of battery first proposes a Coulomb efficiency calculation method for capacity decay monitoring, then proposes differential capacity analysis to identify active material loss, and then proposes abnormal loss calculation through energy balance equation, and finally proposes the change of OCV-SOC relationship to diagnose the consumption effect, so as to realize the accurate analysis of consumption effect, effectively improve the working effect of a method for evaluating internal short circuit of battery, provide more comprehensive and accurate technical support for the method, and provide better decision support for scientific and efficient method for evaluating internal short circuit of battery. At the same time, the invention relates to battery safety monitoring and multi-physics field coupling analysis technology, provides people with an accurate and efficient method for evaluating internal short circuit of battery, and contributes important application value to the method.

[0035] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for evaluating a short circuit in a battery, characterized in that: It includes a data acquisition module, a data preprocessing module, a feature extraction and fusion module, a parameter and consumption effect analysis module, a fusion diagnosis module and a verification and optimization module. The data acquisition module is used to collect high-precision electrical parameters, thermal parameters and mechanical parameters. The data preprocessing module is used to preprocess the collected multiple data. The feature extraction and fusion module proposes a multi-source dynamic collaborative enhanced battery short circuit monitoring algorithm to extract and fuse parameter features and consumption features respectively. The parameter and consumption effect analysis module includes a parameter effect analysis unit and a consumption effect analysis unit. The parameter effect analysis unit proposes a three-order collaborative analysis algorithm for battery short circuit to analyze the parameter effect. The consumption effect analysis unit proposes a multi-scale dynamic coupling analysis algorithm for battery short circuit to analyze the consumption effect. The fusion diagnosis module is used to fuse the parameter effect analysis results and the consumption effect analysis results to diagnose the battery short circuit status. The verification and optimization module is used to verify and optimize the battery short circuit evaluation method.

2. The method for evaluating a short circuit in a battery according to claim 1, wherein: The data acquisition module is used to collect high-precision electrical parameters, thermal parameters and mechanical parameters. It collects single-cell terminal voltage data through a 16-bit ADC, collects current data through shunt + Hall sensor dual-mode detection, collects impedance spectrum data through online EIS technology, collects temperature data through NTC thermistors, and collects battery expansion force change data through micro-strain gauges mounted on the battery casing surface.

3. The method for evaluating a short circuit in a battery according to claim 1, wherein: The data preprocessing module performs noise reduction preprocessing on the collected electrical parameters through wavelet packet transform, captures local abnormal heating of the collected thermal parameters through spatial gradient calculation, and performs signal correction preprocessing on the mechanical parameters through temperature drift compensation.

4. The method for evaluating a short circuit in a battery according to claim 1, wherein: The feature extraction and fusion module proposes a multi-source dynamic collaborative enhanced battery short-circuit monitoring algorithm to extract and fuse parameter features and consumption features respectively.

5. The method for evaluating a short circuit in a battery according to claim 4, wherein: The multi-source dynamic collaborative enhanced battery internal short circuit monitoring algorithm is as follows: Assuming that the collected multi-source data is ,in, is the electrical parameter data, that is ,in, This is the first electrical parameter data. For the second electrical parameter data, For the Electrical parameter data, is the thermal parameter data, that is ,in, is the first thermal parameter data, is the second thermal parameter data, For the Thermal parameter data, is the mechanical parameter data, that is ,in, For the first mechanical parameter data, For the second mechanical parameter data, For the pieces of mechanical parameter data, assuming the sampling period is , then the multi-source data after sampling is ,in, is the battery multi-source data matrix after sampling, For the The electrical parameter data of the sampling time is For the subsampled thermal parameter data, For the In order to improve the signal-to-noise ratio of micro-short circuit transient characteristics, the enhanced differential operation is proposed to enhance the transient feature extraction capability. The enhanced differential operation is , ,in, For data in The subsampled differential eigenvalues, To eliminate the influence of sampling period, the standardization coefficient is: is the difference operator, for The difference operation of For the The electrical parameter data after sampling, For the The electrical parameter data after sampling, For the The electrical parameter data after sampling, For the The electrical parameter data after sampling is used to achieve feature weight adaptation. The SOH correlation function is proposed to improve the contribution of key parameters. The multi-physics field weighted energy is , , ,in, Weighted energy for multiphysics, is the weight coefficient of the electrical parameters, is the weight coefficient of thermal parameters, is the weight coefficient of the mechanical parameters, is a space vector, is the mechanical parameter data after differentiation, is the dynamic weight coefficient, is the weight decay slope, For the In order to quantify the disorder of the signal, it is proposed to change the fixed reference to the sliding mean to reduce the influence of baseline drift caused by cycle aging. Therefore, the multimodal data feature extraction is ,in, For the The information entropy eigenvalue of the subsampling is is the sliding window size, is the offset index within the window, is the historical sampling data, is the sliding window mean, To calculate the square deviation of each data point from the window mean; then perform feature fusion processing, and propose multimodal data frequency domain coupling analysis to compress the amount of calculation in order to capture the multi-physics field collaborative anomaly, the coupling power spectrum density is ,in, is the coupled power spectral density, is the upper limit of the fault-sensitive frequency band, is the lower limit of the fault sensitive frequency band, is the fast Fourier transform, is the frequency independent variable, is the spatial gradient, is the change in mechanical parameter data of adjacent sampling points, As a differential operator, in order to enhance the early signal characteristics, it is proposed to improve the fixed bandwidth into aging-related dynamic focusing to increase the sensitivity of the spectrum width to weak faults. The dynamic focusing bandwidth is ,in, is the dynamic focusing bandwidth, is the center frequency of the spectrum, is the nonlinear attenuation factor, is the cumulative history of voltage changes, that is, ,in, For the The characteristic voltage change of the sub-cycle is For the absolute value of voltage change, the multi-source dynamic collaborative enhanced battery short-circuit monitoring algorithm first proposes enhanced differential operation to enhance the transient feature extraction capability, then proposes the SOH correlation function to achieve feature weight adaptation, and then proposes to change the fixed benchmark to the sliding mean to reduce the impact of baseline drift caused by cycle aging, and then proposes multimodal data frequency domain coupling analysis and improves the fixed bandwidth to aging-related dynamic focusing for feature fusion, realizing the extraction and fusion of battery parameter features and consumption features.

6. The method for evaluating a short circuit in a battery according to claim 1, wherein: The parameter effect analysis unit proposes a third-order collaborative analysis algorithm for internal short circuit of the battery to analyze the parameter effect.

7. The method for evaluating a short circuit in a battery according to claim 6, wherein: The specific details of the third-order collaborative analysis algorithm for internal short circuit of battery are as follows: In order to solve the problem that the traditional bottom-order differential is insensitive to weak signals, the third-order differential feature enhancement is proposed to improve the sensitivity to weak signals. The third-order differential eigenvalue is ,in, is the third-order differential eigenvalue, that is, the battery multi-source data at time The transient change intensity, For the subsampling, is the sampling period, is the standardized coefficient of the third-order differential method, for The third-order differential operation of , For the The electrical parameter data of the sampling time is For the Sub-sampled battery multi-source data, For the Sub-sampled battery multi-source data, For the To solve the problem of single parameter misreporting, a multi-physics field collaborative verification is proposed to realize short circuit location based on the multi-source battery data of the sub-sample. The verification result is calculated as ,in, The verification result of the calculation is is the basic threshold of the battery, is the norm of the temperature gradient, For the mechanical parameter data after differentiation, in order to achieve the threshold adjustment of the whole life cycle, an aging adaptive decision is proposed, namely ,in, is the aging adaptive threshold, is the aging sensitivity coefficient, The voltage change history accumulation amount, the voltage decay rate after charging is calculated as follows: ,in, is the decay rate factor, The charging end time is The moment of relaxation ends, is the voltage change rate, is the absolute value of the voltage change rate, and a three-dimensional thermal model is constructed to perform three-dimensional thermal residual analysis, that is, ,in, is the length direction of the battery, In the battery width direction, is the thickness direction of the battery, is the temperature residual, is the measured temperature, is the thermoelectric coupling coefficient, is the operating current, The spatial thermal resistance distribution is then monitored by online EIS and the characteristic time constant is identified by DRT. The impedance DRT analysis is calculated as , ,in, is the characteristic time constant of the SEI film, is the measured impedance spectrum, is the time constant, is the distribution relaxation time function, is the model impedance spectrum, is the L2 norm, For the regularization coefficient, the third-order collaborative analysis algorithm for internal battery short circuits first proposes third-order differential feature enhancement to improve the sensitivity to weak signals, then proposes multi-physical field collaborative verification to achieve short circuit positioning, and then proposes aging adaptive decision-making to achieve full life cycle threshold adjustment. Finally, the voltage decay rate is calculated, the three-dimensional thermal model is constructed, and the characteristic time constant is calculated through DRT identification, so as to achieve accurate analysis of parameter effects.

8. The method for evaluating a short circuit in a battery according to claim 1, wherein: The consumption effect analysis unit proposes a multi-scale dynamic coupling analysis algorithm for internal short circuit of the battery to analyze the consumption effect.

9. The method for evaluating a short circuit in a battery according to claim 8, wherein: The multi-scale dynamic coupling analysis algorithm for internal short circuit of battery is as follows: The Coulomb efficiency of each cycle is calculated as follows: , ,in, For the The coulombic efficiency of the cycle, is the discharge capacity, is the charging capacity, is the capacity attenuation, is the current full charge capacity, is the initial full charge capacity. To identify the loss of active material, the differential capacity is used for calculation and analysis, i.e. , ,in, is the differential capacity, For the discrete capacity points, For the discrete capacity points, For the discrete voltage points, For the discrete voltage points, is the characteristic peak voltage offset, is the current cycle peak voltage, is the initial reference peak voltage, and under the static condition of ambient temperature, the abnormal loss is calculated by the energy balance equation, that is, , ,in, is the self-discharge rate, is the starting time of standing still, The end time of stillness, is the rate of change of voltage with time, is the energy loss, is the equivalent leakage current, For electrical parameter data, the OCV-SOC relationship changes to ,in, is the OCV curve offset integral, is the current open circuit voltage-SOC curve, For reference OCV-SOC curve, For the charging state, the multi-scale dynamic coupling analysis algorithm of internal short circuit of the battery first proposes a Coulomb efficiency calculation method to monitor the capacity attenuation, then proposes differential capacity analysis to identify the loss of active materials, and then proposes abnormal loss calculation through the energy balance equation. Finally, the change of OCV-SOC relationship is proposed to diagnose the consumption effect, so as to achieve accurate analysis of the consumption effect.

10. The method for evaluating a short circuit in a battery according to claim 1, wherein: The fusion diagnosis module is used to fuse the results of parameter effect analysis and consumption effect analysis to diagnose the short circuit status of the battery. First, a correlation matrix containing all characteristic parameters is constructed, and an improved DS evidence theory is used to process uncertain information. Decision fusion is achieved through fuzzy logic reasoning. The early warning system implements a three-level response mechanism: when a single parameter exceeds the threshold, a first-level warning is triggered. When multiple parameters are abnormal, it is upgraded to a second-level warning. Only when parameter anomalies and consumption anomalies occur simultaneously can a third-level warning be triggered. The verification and optimization module is used to verify and optimize the evaluation method for internal short circuits in batteries. The system continuously optimizes performance through closed-loop verification. Under controllable laboratory conditions, internal short circuits of varying severity are artificially created to verify the accuracy of the diagnostic algorithm. The accumulated fault cases will continuously enrich the sample library and gradually optimize the evaluation method.

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