Health assessment method and system for energy storage lithium battery system

Abstract

The invention relates to the technical field of lithium batteries, in particular to a health assessment method and system for an energy storage lithium battery system. The method comprises the following steps: collecting broadband domain complex impedance response data of an energy storage lithium battery system in a floating charge state, and carrying out frequency domain segmentation to obtain lithium battery impedance characteristic data; determining a calendar-microcirculation aging distribution weight based on the impedance characteristic data of the lithium battery; carrying out microcalorimetric detection on the energy storage lithium battery system to obtain real-time battery heat flow time sequence data; performing DSC test on the energy storage lithium battery system based on the real-time battery heat flow time sequence data to obtain a DSC heat flow curve; and performing side reaction product evolution prediction on the energy storage lithium battery system based on the DSC heat flow curve to obtain a positive electrode interface side reaction parameter set. According to the invention, real-time and accurate evaluation and life prediction of the health state of the energy storage lithium battery system are realized.

Description

Technical Field

[0001] The present invention relates to the field of lithium battery technology, and in particular to a health assessment method and system for an energy storage lithium battery system. Background Art

[0002] In communication base station energy storage applications, lithium battery systems serve as backup power sources during utility power outages, playing a key role in ensuring the continuous operation of base stations. This scenario presents distinct characteristics: the battery maintains a high float charge voltage (typically 53.5-54V) for extended periods, undergoes only very shallow daily charge and discharge cycles (mostly less than 5% depth of discharge), and must operate continuously within a high-temperature, sealed cabinet. This combined operating condition of "high-voltage constant-voltage float charge + micro-circulation + high-temperature environment" differs significantly from the deep-cycle mode of electric vehicles or grid-level energy storage, causing traditional health assessment models to frequently fail in this scenario.

[0003] Existing technologies often overlook the nonlinear coupling effects of calendar aging and microcirculation aging. During their multi-year service life, base station batteries spend 99% of their time in a fully charged float state, where high temperatures accelerate electrolyte decomposition and SEI thickening. The remaining 1% of the time is spent carrying transient loads (such as equipment startup and shutdown) or short-term discharges (such as mains power outages). Experiments have shown that side reaction products at the positive electrode interface, triggered by high-voltage float charging, cross-catalyze lithium deposition at the negative electrode caused by microcirculation, causing a step-like increase in the capacity decay rate in the middle and late stages of service. Summary of the Invention

[0004] Based on this, it is necessary for the present invention to provide a health assessment method and system for an energy storage lithium battery system to solve at least one of the above technical problems.

[0005] To achieve the above objectives, a health assessment method for an energy storage lithium battery system includes the following steps:

[0006] Step S1: collecting wide-frequency complex impedance response data of the energy storage lithium battery system in a floating charge state, and performing frequency domain segmentation to obtain lithium battery impedance characteristic data; determining a calendar-microcycle aging distribution weight based on the lithium battery impedance characteristic data;

[0007] Step S2: performing microcalorimetry on the energy storage lithium battery system to obtain real-time battery heat flow time series data; performing DSC testing on the energy storage lithium battery system based on the real-time battery heat flow time series data to obtain a DSC heat flow curve; predicting the evolution of side reaction products of the energy storage lithium battery system based on the DSC heat flow curve to obtain a positive electrode interface side reaction parameter set;

[0008] Step S3: configuring a potential step relaxation test device for the energy storage lithium battery system, and quantifying the degree of lithium deposition of the energy storage lithium battery system based on the potential step relaxation test device to obtain lithium deposition degree data of the lithium battery; predicting the capacity attenuation of the lithium battery based on the lithium deposition degree data to obtain capacity sudden drop inflection point prediction data;

[0009] Step S4: constructing a lithium battery health state transfer equation based on the calendar-microcycle aging distribution weight; performing a lithium battery health state assessment based on the lithium battery health state transfer equation to obtain key health indicators of the lithium battery;

[0010] Step S5: Based on the key health indicators of the lithium battery and the capacity drop inflection point prediction data, the remaining life of the energy storage lithium battery system is evaluated to obtain the corrected life prediction data of the lithium battery.

[0011] By collecting wide-band complex impedance response data and performing frequency domain segmentation, the present invention can capture the impedance characteristics of lithium batteries at different frequencies based on the electrochemical nature, and then accurately extract impedance characteristic data. This not only takes into account the charge transfer characteristics of the electrode materials inside the battery, but also combines the electrochemical reaction dynamics of the electrolyte and electrode interface, so that it can effectively distinguish the nonlinear effects of calendar aging and microcirculation aging on battery performance, and accurately determine the aging distribution weight. This weight determination method based on impedance characteristics fundamentally solves the problem of insufficient understanding of the aging mechanism in traditional methods. By combining microcalorimetry detection and differential scanning calorimetry (DSC) testing, the evolution of side reaction products at the positive electrode interface is predicted from a thermodynamic perspective. This can accurately capture the heat flow changes of side reactions inside the battery, and by analyzing the heat flow curve, reveal key parameters such as the enthalpy change characteristics, reaction rate constants, and product concentrations of the side reactions. This can not only monitor the side reaction dynamics inside the battery in real time, but also predict the potential degradation risk of battery performance through the evolution trend of the side reaction products, thereby providing a more comprehensive basis for health status assessment. Potential step relaxation testing equipment is used to quantify the extent of lithium plating and predict the capacity drop inflection point. This approach, based on electrochemical kinetics, monitors the characteristic parameters of negative electrode potential relaxation to accurately assess the impact of lithium plating on battery performance. Combining nuclear magnetic resonance spectroscopy (NMR) and scanning electron microscopy (SEM), the extent of lithium plating is further quantified at the atomic and microscopic levels, enabling precise prediction of battery capacity decay. This effectively captures microstructural changes within the battery, providing more accurate quantitative indicators for health status assessment. By constructing a health status transition equation based on aging distribution weights and combining remaining life assessment with key health indicators and capacity drop inflection point prediction data, real-time, accurate health status assessment and lifespan prediction of lithium-ion battery energy storage systems are achieved. This not only provides early warning of battery health issues but also, by accurately predicting the capacity drop inflection point, provides a scientific basis for battery maintenance and replacement, effectively extending battery life, reducing the operation and maintenance costs of communication base station energy storage systems, and improving system reliability and cost-effectiveness.

[0012] Preferably, step S1 collects wide-frequency domain complex impedance response data of the energy storage lithium battery system in a floating charge state and performs frequency domain segmentation, including:

[0013] Configure the multi-channel data acquisition interface for the energy storage lithium battery system to obtain the electrochemical impedance spectroscopy test parameter configuration;

[0014] Based on the electrochemical impedance spectroscopy test parameter configuration, a wide-frequency domain scan range is set for the energy storage lithium battery system to obtain the wide-frequency domain impedance scan parameters. The wide-frequency domain frequency scan range is set to 0.01Hz-10kHz, using a logarithmic distribution method, with 10 test points set for each frequency octave. The disturbance signal amplitude is set to 5mV, the phase accuracy is ±0.1°, the impedance accuracy is ±0.1%, and the single scan time is controlled within 30 minutes.

[0015] The impedance spectrum of the energy storage lithium battery system is collected under floating charge state according to the wide-frequency domain impedance scanning parameters to obtain the original complex impedance response data;

[0016] Perform noise filtering on the original complex impedance response data to obtain lithium battery purification impedance spectrum data;

[0017] Normalize the lithium battery purification impedance spectrum data to generate a standard lithium battery impedance spectrum data set;

[0018] The standard lithium battery impedance spectrum dataset is segmented in the frequency domain according to the preset frequency domain segmentation rules to obtain the lithium battery impedance characteristic data. The frequency domain is divided into three intervals: high frequency band, medium frequency band, and low frequency band. Four characteristic parameters of impedance real part, imaginary part, modulus value, and phase angle are extracted from each frequency band to form a 12-dimensional feature vector.

[0019] Preferably, determining the calendar-microcycle aging distribution weight based on the lithium battery impedance characteristic data in step S1 includes:

[0020] Select a wavelet basis function according to the lithium battery impedance characteristic data, and obtain wavelet basis function selection parameters;

[0021] Based on the wavelet basis function selection parameters, the impedance data of each frequency point in the lithium battery impedance characteristic data is subjected to continuous wavelet transform to obtain the lithium battery impedance coefficient set in the time-frequency domain;

[0022] Calculate the wavelet coefficient energy at each scale of the lithium battery impedance coefficient set in the time-frequency domain to obtain the energy distribution data at each scale;

[0023] Based on the energy distribution data of each scale, the top 8 main scales with a concentrated energy proportion greater than 5% of the lithium battery impedance coefficient in the time-frequency domain are selected, and the corresponding wavelet coefficient real part, imaginary part, modulus and phase information are extracted to form a time-frequency domain impedance feature vector set;

[0024] Perform Hilbert-Huang transform on the impedance eigenvector set in the time-frequency domain to obtain the impedance empirical mode decomposition parameters;

[0025] According to the impedance empirical mode decomposition parameters, each dimension of the impedance eigenvector set in the time-frequency domain is decomposed dimension by dimension to obtain the initial set of eigenmode functions;

[0026] Perform Hilbert transform on the initial set of intrinsic mode functions to obtain the modal instantaneous frequency and amplitude data; perform frequency characteristic statistics on the modal instantaneous frequency and amplitude data to obtain a multi-band intrinsic mode function set;

[0027] The aging mechanism weights were calculated based on the multi-band intrinsic mode function set, and the calendar-microcirculation aging distribution weights were obtained.

[0028] Preferably, step S2 includes the following steps:

[0029] Step S21: configuring a microcalorimetric device for the energy storage lithium battery system to obtain battery heat flow detection system parameters;

[0030] Step S22: performing float charge state heat flow monitoring on the energy storage lithium battery system based on the battery heat flow detection system parameters to obtain original battery heat flow time series data;

[0031] Step S23: performing baseline drift correction on the original battery heat flow time series data to obtain purified battery heat flow time series data;

[0032] Step S24: identifying the heat flow peak value of the purification battery heat flow time series data to obtain the side reaction heat flow characteristic point;

[0033] Step S25: pre-configuring DSC test parameters for the energy storage lithium battery system according to the side reaction heat flow characteristic points to obtain a DSC test parameter configuration;

[0034] Step S26: performing a temperature program on the battery sample of the energy storage lithium battery system according to the DSC test parameter configuration, and recording a relationship curve between heat flow and temperature to obtain a DSC heat flow curve;

[0035] Step S27: Predicting the evolution of side reaction products of the energy storage lithium battery system based on the DSC heat flow curve to obtain a positive electrode interface side reaction parameter set.

[0036] Preferably, step S27 includes the following steps:

[0037] Step S271: performing peak separation and fitting on the DSC heat flow curve to obtain characteristic data of side reaction enthalpy change, wherein the characteristic data of side reaction enthalpy change includes peak temperature, peak intensity, peak width and peak area;

[0038] Step S272: Repeating DSC testing on battery samples of the energy storage lithium battery system at different temperature points at each peak temperature, and calculating reaction rate constants at different temperatures to obtain battery reaction rate constant parameters;

[0039] Step S273: preparing the energy storage lithium battery system for X-ray photoelectron spectroscopy testing to obtain XPS test condition parameters;

[0040] Step S274: Detecting the surface composition of the positive electrode of the energy storage lithium battery system according to the XPS test condition parameters to obtain chemical state spectrum data of the lithium battery;

[0041] Step S275: performing peak decomposition and quantification of side reaction products on the lithium battery chemical state spectrum data to obtain side reaction product concentration data;

[0042] Step S276: constructing a side reaction model for the energy storage lithium battery system according to the battery reaction rate constant parameter and the side reaction product concentration data to obtain a lithium battery side reaction model;

[0043] Step S277: Based on the preset prediction cycle and the lithium battery side reaction model, the side reaction product evolution of the energy storage lithium battery system is predicted to obtain the positive electrode interface degradation trend data; the positive electrode interface degradation trend data is parameter extracted and integrated to obtain the positive electrode interface side reaction parameter set.

[0044] Preferably, configuring a potential step relaxation test device for the energy storage lithium battery system in step S3, and quantifying the degree of lithium deposition of the energy storage lithium battery system based on the potential step relaxation test device includes:

[0045] Perform potential step relaxation test equipment configuration on the energy storage lithium battery system and obtain electrochemical workstation parameters;

[0046] Based on the electrochemical workstation parameters, the microcirculation potential of the energy storage lithium battery system is monitored to obtain the original data of the negative electrode potential response;

[0047] Noise filtering is performed on the raw data of the negative electrode potential response to obtain purified lithium battery potential relaxation data;

[0048] Perform relaxation curve fitting on the potential relaxation data of purified lithium batteries to obtain potential relaxation characteristic parameters;

[0049] The lithium deposition criterion is established based on the potential relaxation characteristic parameters, and the critical potential threshold of lithium deposition is obtained;

[0050] The lithium deposition degree of the energy storage lithium battery system is quantified according to the critical potential threshold of lithium deposition and the potential relaxation data of the purified lithium battery, and the lithium deposition degree data of the lithium battery is obtained.

[0051] Preferably, the step S3 of predicting the capacity attenuation of the lithium battery based on the lithium deposition degree data of the lithium battery includes:

[0052] Prepare the energy storage lithium battery system for nuclear magnetic resonance spectroscopy testing and obtain NMR test parameter configuration; perform lithium metal quantitative testing on the energy storage lithium battery system based on the NMR test parameter configuration and obtain 7Li NMR spectrum data;

[0053] Perform peak decomposition and lithium deposition quantification on 7Li NMR spectrum data to obtain lithium deposition data of lithium battery negative electrode;

[0054] Based on the lithium battery negative electrode lithium plating data, the energy storage lithium battery system is configured with a scanning electron microscope to obtain the scanning electron microscope detection parameters;

[0055] Scan the negative electrode surface morphology of the energy storage lithium battery system according to the scanning electron microscope detection parameters to obtain the lithium deposition morphology characteristic data;

[0056] A simulation model of the energy storage lithium battery system is constructed to obtain the MD simulation parameter configuration; based on the MD simulation parameter configuration and the positive electrode interface side reaction parameter set, the cross-catalytic effect of the energy storage lithium battery system is carried out to obtain the intermolecular interaction energy data;

[0057] Based on the intermolecular interaction energy data, the catalytic reaction of the energy storage lithium battery system is simulated to obtain the cross-catalytic reaction activation energy data;

[0058] Based on the lithium deposition degree data of lithium batteries, the lithium deposition data of lithium batteries' negative electrodes and the activation energy data of cross-catalytic reactions, the coupling effect is evaluated to obtain the positive and negative electrode interface coupling parameters;

[0059] The capacity attenuation of the energy storage lithium battery system is predicted based on the positive and negative electrode interface coupling parameters, and the capacity drop inflection point prediction data is obtained.

[0060] Preferably, step S4 includes the following steps:

[0061] Step S41: synchronously collecting data of the energy storage lithium battery system according to a preset three-level time scale collection system to obtain lithium battery time series synchronous collection parameters;

[0062] Step S42: performing time alignment of degradation information on lithium battery timing synchronous acquisition parameters according to calendar-microcirculation aging distribution weights to obtain a lithium battery synchronous degradation data table;

[0063] Step S43: extracting degradation feature vectors from the lithium battery synchronous degradation data table to obtain lithium battery degradation feature data;

[0064] Step S44: establishing a lithium battery state space model based on the lithium battery degradation characteristic data;

[0065] Step S45: solving the state covariance of the lithium battery state space model based on the lithium battery degradation characteristic data to obtain lithium battery state covariance data;

[0066] Step S46: performing covariance correction on the lithium battery state covariance data to obtain a Kalman filter parameter set;

[0067] Step S47: performing lithium battery state space modeling according to the Kalman filter parameter set to obtain a lithium battery health state transition equation;

[0068] Step S48: Evaluate the health status of the lithium battery based on the lithium battery health status transfer equation to obtain key health indicators of the lithium battery.

[0069] Preferably, step S5 includes the following steps:

[0070] Step S51: performing support vector machine classifier parameter configuration on the key health indicators of the lithium battery to obtain the SVM classifier parameter configuration;

[0071] Step S52: constructing training samples for key health indicators of lithium batteries according to the SVM classifier parameter configuration to obtain an SVM classification training data set;

[0072] Step S53: performing SVM model training based on the SVM classification training data set to obtain an SVM classifier model; performing cross-validation evaluation on the SVM classifier model to obtain SVM classifier performance evaluation data;

[0073] Step S54: classifying the health status of the energy storage lithium battery system according to the SVM classifier performance evaluation data and the key health indicators of the lithium battery to obtain health status grade classification data;

[0074] Step S55: performing historical data statistics on the energy storage lithium battery system based on the health status classification data to obtain health threshold adjustment benchmark data;

[0075] Step S56: performing adaptive threshold calculation on the energy storage lithium battery system according to the health threshold adjustment benchmark data to obtain the lithium battery health dynamic threshold parameter;

[0076] Step S57: setting warning rules for the energy storage lithium battery system based on the lithium battery health dynamic threshold parameters and health status classification data to obtain lithium battery health warning rules;

[0077] Step S58: Based on the lithium battery health warning rules and the capacity drop inflection point prediction data, the remaining life of the energy storage lithium battery system is evaluated to obtain the lithium battery corrected life prediction data.

[0078] Preferably, the present invention further provides a health assessment system for an energy storage lithium battery system, which is used to perform the health assessment method for an energy storage lithium battery system as described above. The health assessment system for an energy storage lithium battery system includes:

[0079] The impedance analysis module is used to collect the wide-frequency complex impedance response data of the energy storage lithium battery system in the floating charge state, and perform frequency domain segmentation to obtain the lithium battery impedance characteristic data; based on the lithium battery impedance characteristic data, the calendar-microcycle aging distribution weight is determined;

[0080] The heat flow monitoring module is used to perform microcalorimetry on the energy storage lithium battery system to obtain real-time battery heat flow time series data; perform DSC testing on the energy storage lithium battery system based on the real-time battery heat flow time series data to obtain the DSC heat flow curve; and predict the evolution of side reaction products of the energy storage lithium battery system based on the DSC heat flow curve to obtain the positive electrode interface side reaction parameter set;

[0081] The lithium deposition quantification module is used to configure the potential step relaxation test equipment of the energy storage lithium battery system, and quantify the lithium deposition degree of the energy storage lithium battery system based on the potential step relaxation test equipment to obtain the lithium deposition degree data of the lithium battery; based on the lithium deposition degree data of the lithium battery, the lithium battery capacity attenuation is predicted to obtain the capacity sudden drop inflection point prediction data;

[0082] The health assessment module is used to construct a lithium battery health state transition equation based on the calendar-microcycle aging distribution weight; perform a lithium battery health state assessment based on the lithium battery health state transition equation to obtain key health indicators of the lithium battery;

[0083] The life prediction module is used to evaluate the remaining life of the energy storage lithium battery system based on the key health indicators of the lithium battery and the capacity drop inflection point prediction data, and obtain the corrected life prediction data of the lithium battery.

[0084] This invention uses multi-dimensional data fusion to accurately extract lithium battery impedance characteristics, monitor side reaction dynamics, and quantify lithium plating from the perspectives of electrochemistry, thermodynamics, and electrochemical kinetics. This comprehensive analysis reflects the aging characteristics of lithium batteries under complex operating conditions, providing a rich information foundation for health status assessment. Based on impedance characteristic data, the invention determines calendar-microcycle aging distribution weights, accurately reflecting the impact of different aging factors on battery performance, addressing the lack of understanding of aging mechanisms in traditional methods. Through a heat flow monitoring module and DSC testing, the system monitors the evolution of side reaction products at the cathode interface in real time, providing early warning of battery health issues and a scientific basis for maintenance optimization. The lithium plating quantification module quantifies the extent of lithium plating at the atomic and microscopic scales, accurately predicting the capacity drop inflection point, further improving the accuracy of health status assessment. The health assessment module constructs a health status transition equation based on aging distribution weights. Combining key health indicators with capacity drop inflection point prediction data, this module enables real-time, accurate health status assessment and lifespan prediction for lithium batteries. This timely reflects battery performance trends, provides a scientific basis for battery maintenance and replacement, effectively extending battery life and reducing operation and maintenance costs. The present invention provides early warning of battery health problems through accurate health status assessment and life prediction, avoiding communication base station interruption due to battery failure, and significantly improving the reliability and economy of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Other features, objects and advantages of the present invention will become more apparent from reading the detailed description made with reference to the following drawings:

[0086] Figure 1 A schematic flow chart of the steps of a health assessment method for an energy storage lithium battery system according to one embodiment is shown.

[0087] Figure 2 A detailed flowchart of step S27 of an embodiment is shown. DETAILED DESCRIPTION

[0088] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0089] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0090] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0091] To achieve this, please refer to Figures 1 to 2 The present invention provides a health assessment method for an energy storage lithium battery system, comprising the following steps:

[0092] Step S1: collecting wide-frequency complex impedance response data of the energy storage lithium battery system in a floating charge state, and performing frequency domain segmentation to obtain lithium battery impedance characteristic data; determining a calendar-microcycle aging distribution weight based on the lithium battery impedance characteristic data;

[0093] Step S2: performing microcalorimetry on the energy storage lithium battery system to obtain real-time battery heat flow time series data; performing DSC testing on the energy storage lithium battery system based on the real-time battery heat flow time series data to obtain a DSC heat flow curve; predicting the evolution of side reaction products of the energy storage lithium battery system based on the DSC heat flow curve to obtain a positive electrode interface side reaction parameter set;

[0094] Step S3: configuring a potential step relaxation test device for the energy storage lithium battery system, and quantifying the degree of lithium deposition of the energy storage lithium battery system based on the potential step relaxation test device to obtain lithium deposition degree data of the lithium battery; predicting the capacity attenuation of the lithium battery based on the lithium deposition degree data to obtain capacity sudden drop inflection point prediction data;

[0095] Step S4: constructing a lithium battery health state transfer equation based on the calendar-microcycle aging distribution weight; performing a lithium battery health state assessment based on the lithium battery health state transfer equation to obtain key health indicators of the lithium battery;

[0096] Step S5: Based on the key health indicators of the lithium battery and the capacity drop inflection point prediction data, the remaining life of the energy storage lithium battery system is evaluated to obtain the corrected life prediction data of the lithium battery.

[0097] Preferably, step S1 collects wide-frequency domain complex impedance response data of the energy storage lithium battery system in a floating charge state and performs frequency domain segmentation, including:

[0098] Configure the multi-channel data acquisition interface for the energy storage lithium battery system to obtain the electrochemical impedance spectroscopy test parameter configuration;

[0099] Based on the electrochemical impedance spectroscopy test parameter configuration, a wide-frequency domain scan range is set for the energy storage lithium battery system to obtain the wide-frequency domain impedance scan parameters. The wide-frequency domain frequency scan range is set to 0.01Hz-10kHz, using a logarithmic distribution method, with 10 test points set for each frequency octave. The disturbance signal amplitude is set to 5mV, the phase accuracy is ±0.1°, the impedance accuracy is ±0.1%, and the single scan time is controlled within 30 minutes.

[0100] The impedance spectrum of the energy storage lithium battery system is collected under floating charge state according to the wide-frequency domain impedance scanning parameters to obtain the original complex impedance response data;

[0101] Perform noise filtering on the original complex impedance response data to obtain lithium battery purification impedance spectrum data;

[0102] Normalize the lithium battery purification impedance spectrum data to generate a standard lithium battery impedance spectrum data set;

[0103] The standard lithium battery impedance spectrum dataset is segmented in the frequency domain according to the preset frequency domain segmentation rules to obtain the lithium battery impedance characteristic data. The frequency domain is divided into three intervals: high frequency band, medium frequency band, and low frequency band. Four characteristic parameters of impedance real part, imaginary part, modulus value, and phase angle are extracted from each frequency band to form a 12-dimensional feature vector.

[0104] In this example, a Gamry Reference 3000 electrochemical workstation was used as a multi-channel data acquisition device in a laboratory environment. This device has multiple channels and can connect multiple lithium battery samples simultaneously, facilitating batch testing. The data acquisition interface was configured using the Gamry software interface, with the sampling frequency set to 1000 Hz. Based on the test requirements, the potentiostat mode was selected, and the potential control accuracy was set to 0.01 mV. After completing the interface configuration, the electrochemical impedance spectroscopy test parameter configuration was recorded, including the sampling frequency, operating mode, and accuracy settings. Based on the Gamry Reference 3000 electrochemical workstation parameter configuration, the wide-band scan range was set to 0.01 Hz to 10 kHz. A logarithmic distribution was used, with 10 test points set in each frequency octave to ensure sufficient data points were obtained across different frequency ranges to reflect the impedance characteristics. The perturbation signal amplitude was set to 5 mV, an experimentally verified appropriate amplitude that effectively stimulates the electrochemical reaction within the battery without causing irreversible damage. The phase accuracy was set to ±0.1°, and the impedance accuracy was set to ±0.1%. The single scan time was controlled within 30 minutes to improve test efficiency and reduce environmental interference during the test. After completing the above parameter settings, the complete wide-band impedance scan parameters were obtained. The lithium-ion battery system under test was connected to a Gamry Reference 3000 electrochemical workstation, and an impedance spectrum was acquired under floating charge according to the set wide-band impedance scan parameters. During the acquisition process, the battery was kept in floating charge to simulate the high floating voltage conditions encountered in actual use scenarios. The acquisition process was monitored in real time using the electrochemical workstation software. After acquisition, raw complex impedance response data was obtained, which contained information such as the real and imaginary impedance components, modulus, and phase angle of the battery at different frequencies. The acquired raw complex impedance response data was processed using MATLAB software. The raw data file was loaded and noise was filtered using built-in MATLAB filtering functions, such as medfilt1 (median filtering) or movmean (moving average filtering). For median filtering, an appropriate window size, such as 5, was selected. The median value of each data point and its two adjacent data points was calculated to remove random noise from the data. After noise filtering, the purified impedance spectrum data of the lithium battery is obtained. The purified impedance spectrum data is normalized using MATLAB software. The maximum and minimum normalization method is selected to normalize the real part, imaginary part, modulus, and phase angle data of the impedance at each frequency point to the range of 0 to 1. The specific operation is to calculate the maximum and minimum values ​​of each characteristic parameter in the entire data set, subtract the minimum value from the value of each data point, and divide it by the difference between the maximum and minimum values. In this way, a standard lithium battery impedance spectrum data set is generated.According to the preset frequency domain segmentation rules, the standard lithium battery impedance spectrum data set is divided into three intervals: high frequency band (1kHz-10kHz), medium frequency band (0.1Hz-1kHz) and low frequency band (0.01Hz-0.1Hz). For each frequency band, the four characteristic parameters of the impedance real part, imaginary part, modulus and phase angle are extracted respectively. For example, in MATLAB, the data points of each frequency band are filtered out through logical indexing or loop statements, and the average value or characteristic value of each characteristic parameter in each frequency band is calculated. Finally, the 12 characteristic parameters of the three frequency bands are combined into a 12-dimensional characteristic vector, which can fully reflect the impedance characteristics of the lithium battery in different frequency domains.

[0105] Preferably, determining the calendar-microcycle aging distribution weight based on the lithium battery impedance characteristic data in step S1 includes:

[0106] Select a wavelet basis function according to the lithium battery impedance characteristic data, and obtain wavelet basis function selection parameters;

[0107] Based on the wavelet basis function selection parameters, the impedance data of each frequency point in the lithium battery impedance characteristic data is subjected to continuous wavelet transform to obtain the lithium battery impedance coefficient set in the time-frequency domain;

[0108] Calculate the wavelet coefficient energy at each scale of the lithium battery impedance coefficient set in the time-frequency domain to obtain the energy distribution data at each scale;

[0109] Based on the energy distribution data of each scale, the top 8 main scales with a concentrated energy proportion greater than 5% of the lithium battery impedance coefficient in the time-frequency domain are selected, and the corresponding wavelet coefficient real part, imaginary part, modulus and phase information are extracted to form a time-frequency domain impedance feature vector set;

[0110] Perform Hilbert-Huang transform on the impedance eigenvector set in the time-frequency domain to obtain the impedance empirical mode decomposition parameters;

[0111] According to the impedance empirical mode decomposition parameters, each dimension of the impedance eigenvector set in the time-frequency domain is decomposed dimension by dimension to obtain the initial set of eigenmode functions;

[0112] Perform Hilbert transform on the initial set of intrinsic mode functions to obtain the modal instantaneous frequency and amplitude data; perform frequency characteristic statistics on the modal instantaneous frequency and amplitude data to obtain a multi-band intrinsic mode function set;

[0113] The aging mechanism weights were calculated based on the multi-band intrinsic mode function set, and the calendar-microcirculation aging distribution weights were obtained.

[0114] In this embodiment, MATLAB software is used to perform wavelet analysis on the lithium battery impedance characteristic data. Morlet wavelet is selected as the basis function. In MATLAB, the Morlet wavelet basis function is generated by the morlet function, and its parameters are set, such as the center frequency is 5Hz and the bandwidth is 1.5Hz. The selection of these parameters is based on a preliminary analysis of the spectral characteristics of the lithium battery impedance data, and the wavelet basis function selection parameters are finally obtained. The cwt function in MATLAB is used to perform continuous wavelet transform on the lithium battery impedance characteristic data. The previously selected Morlet wavelet basis function and its parameters are passed to the cwt function to transform the impedance data at each frequency point. During the transformation process, the cwt function calculates the wavelet coefficients at different scales to obtain a set of lithium battery impedance coefficients in the time-frequency domain. These coefficient sets contain the characteristic information of the impedance data at different time scales and frequencies. By drawing a time-frequency graph of the wavelet coefficients through a visualization tool (such as MATLAB's imagesc function), the changing trend of the impedance data in different frequency bands can be intuitively observed. In MATLAB, energy calculation is performed on the time-frequency domain lithium battery impedance coefficient set. The specific method is to square the wavelet coefficients at each scale to obtain the energy value at that scale. By traversing all scales, calculating the summed energy at each scale and normalizing it to a range of 0 to 1, we obtain energy distribution data for each scale. This data reflects the energy contribution of the impedance signal at different scales and can help identify key scales that significantly influence the impedance characteristics. For example, the modulus of the wavelet coefficients is calculated using the MATLAB abs function, and the energy value is then calculated using the .^2 operator. Finally, the energy distribution curve is plotted using the plot function to visually display the energy distribution at each scale. Based on the obtained energy distribution data, the top eight major scales with an energy contribution greater than 5% are selected. In MATLAB, logical indexing is used to locate these scales, and the real, imaginary, modulus, and phase information of the corresponding wavelet coefficients are extracted. For example, the real, imag, abs, and angle functions are used to extract the real, imaginary, modulus, and phase of the wavelet coefficients, respectively. This feature information is combined into a eigenvector, forming a set of impedance eigenvectors in the time-frequency domain. Each eigenvector contains the key features of the impedance signal at the corresponding scale. The set of impedance eigenvectors in the time-frequency domain is processed using the MATLAB Hilbert-Huang Transform Toolbox. Perform empirical mode decomposition (EMD) on each eigenvector. Using the MATLAB emd function, the impedance eigenvector is decomposed into several intrinsic mode functions (IMFs). The decomposition parameters of each IMF, such as the number of IMFs and the energy contribution of each IMF, are recorded. These parameters serve as the impedance empirical mode decomposition parameters. By plotting the waveform of each IMF using visualization tools (such as the plot function), the characteristics of the impedance signal in different modes can be intuitively observed.Based on the obtained impedance empirical mode decomposition parameters, each eigenvector in the time-frequency domain impedance eigenvector set is subjected to EMD decomposition dimension by dimension. In MATLAB, the emd function is used to decompose each eigenvector to obtain the corresponding intrinsic mode function (IMF). For example, the real impedance eigenvector is decomposed into several IMFs using the emd function. Similarly, the imaginary, modulus, and phase eigenvectors are subjected to EMD decomposition. The IMFs derived from all eigenvector decompositions are combined into a set to form the initial set of eigenmode functions. Using the surf function to plot the three-dimensional time-frequency plot of the IMFs visually demonstrates the distribution characteristics of each IMF in time and frequency. A Hilbert transform is performed on each IMF in the initial set of eigenmode functions. In MATLAB, the Hilbert function is used to transform each IMF to obtain a complex analytic signal. By calculating the instantaneous frequency and amplitude of the complex analytic signal, the modal instantaneous frequency and amplitude data are obtained. For example, the instantaneous amplitude is calculated using the abs function, the instantaneous phase is calculated using the angle function, and the instantaneous frequency is then obtained by differentiation. Frequency feature statistics are performed on these instantaneous frequency and amplitude data, such as calculating the average frequency, frequency range, and amplitude distribution of each IMF. These feature data are organized into a multi-band intrinsic mode function set. Based on the feature data of the multi-band intrinsic mode function set and combined with the aging mechanism of the lithium battery, the calendar-microcirculation aging distribution weight is calculated, and the IMF features of different frequency bands are associated with the aging mechanism. For example, the IMF features of the high-frequency band are related to microcirculation aging, while the IMF features of the low-frequency band are related to calendar aging. The calendar-microcirculation aging distribution weight is obtained by calculating the degree of influence of the IMF features of each frequency band on the overall impedance characteristics. The specific method can be to perform a weighted summation of the IMF features of each frequency band, and the weight is determined according to its correlation with the aging mechanism, and finally obtain the calendar-microcirculation aging distribution weight, which can reflect the degree of influence of different aging mechanisms on the health status of lithium batteries.

[0115] Preferably, step S2 includes the following steps:

[0116] Step S21: configuring a microcalorimetric device for the energy storage lithium battery system to obtain battery heat flow detection system parameters;

[0117] Step S22: performing float charge state heat flow monitoring on the energy storage lithium battery system based on the battery heat flow detection system parameters to obtain original battery heat flow time series data;

[0118] Step S23: performing baseline drift correction on the original battery heat flow time series data to obtain purified battery heat flow time series data;

[0119] Step S24: identifying the heat flow peak value of the purification battery heat flow time series data to obtain the side reaction heat flow characteristic point;

[0120] Step S25: pre-configuring DSC test parameters for the energy storage lithium battery system according to the side reaction heat flow characteristic points to obtain a DSC test parameter configuration;

[0121] Step S26: performing a temperature program on the battery sample of the energy storage lithium battery system according to the DSC test parameter configuration, and recording a relationship curve between heat flow and temperature to obtain a DSC heat flow curve;

[0122] Step S27: Predicting the evolution of side reaction products of the energy storage lithium battery system based on the DSC heat flow curve to obtain a positive electrode interface side reaction parameter set.

[0123] In this embodiment, a TA Instruments MicroCal PEAQ-DSC microcalorimeter is used to perform battery heat flow detection. The microcalorimeter is connected to a computer and the device is configured through its accompanying Origin software. In the software, the temperature range is set to 25°C to 60°C to cover the operating temperature range of the battery in the floating charge state. The heating rate is set to 5°C / min. The atmosphere of the sample cell is set to nitrogen with a flow rate of 50mL / min to prevent the battery from undergoing oxidation reaction during the test. After completing the equipment configuration, record the battery heat flow detection system parameters, including temperature range, heating rate and atmosphere conditions. Place the energy storage lithium battery system to be tested in the sample cell of the microcalorimeter and ensure that the battery is in the floating charge state. According to the previously set battery heat flow detection system parameters, start the microcalorimeter for heat flow monitoring. During the monitoring process, the microcalorimeter will record the heat flow changes of the battery at different temperatures in real time and transmit the data to the Origin software on the computer. After the monitoring is completed, the original battery heat flow time series data is exported from the software. These data contain the heat flow change curve of the battery in the floating charge state over time. Use Origin software to perform baseline drift correction on the original battery heat flow time series data. In the software, select the "Baseline Correction" function module and use the polynomial fitting method to correct the baseline. The specific operation is to select the starting point and end point of the data curve as the reference points for baseline correction, and set the polynomial order to 3 to ensure that the baseline drift can be accurately fitted. The software will automatically calculate the polynomial fitting curve of the baseline drift and subtract the curve from the original data to obtain the purified battery heat flow time series data. In the Origin software, the heat flow peak of the purified battery heat flow time series data is identified. Use the "Peak Find" function of the software and set the peak detection sensitivity to 0.1mW. Set the minimum width of the peak to 1min to avoid mistakenly identifying noise as a peak. The software will automatically scan the data curve, identify the heat flow peak, and mark the position and size of the side reaction heat flow characteristic points. These characteristic points correspond to the side reaction heat flow changes that occur in the battery when it is in the floating charge state. Based on the side reaction heat flow characteristic points identified in step S24, the DSC test parameters of the energy storage lithium battery system are pre-configured. In the Origin software of the Micro Cal PEAQ-DSC microcalorimeter, adjust the temperature range of the DSC test according to the temperature range of the side reaction heat flow characteristic points. For example, if the side reaction heat flow characteristic points are mainly concentrated between 40°C and 50°C, set the temperature range of the DSC test to 35°C to 55°C to ensure that the heat flow changes of the side reactions can be captured more accurately. Maintain a heating rate of 5°C / min, a nitrogen atmosphere, and a flow rate of 50mL / min. After completing the parameter adjustment, record the DSC test parameter configuration. Place the battery sample of the energy storage lithium battery system in the sample cell of the DSC microcalorimeter to ensure good thermal contact between the sample and the reference cell.Based on the DSC test parameters configured in step S25, the microcalorimeter is started to perform a programmed temperature test. During the test, the microcalorimeter heats the battery sample at the set heating rate and records the heat flow vs. temperature curve in real time. After the test is complete, the DSC heat flow curve is exported from the Origin software. This curve clearly shows the changes in heat flow of the battery at different temperatures, reflecting the thermal effects of the battery's internal side reactions. For the detailed implementation process of step S27, please refer to the sub-steps of step S27.

[0124] Preferably, step S27 includes the following steps:

[0125] Step S271: performing peak separation and fitting on the DSC heat flow curve to obtain characteristic data of side reaction enthalpy change, wherein the characteristic data of side reaction enthalpy change includes peak temperature, peak intensity, peak width and peak area;

[0126] Step S272: Repeating DSC testing on battery samples of the energy storage lithium battery system at different temperature points at each peak temperature, and calculating reaction rate constants at different temperatures to obtain battery reaction rate constant parameters;

[0127] Step S273: preparing the energy storage lithium battery system for X-ray photoelectron spectroscopy testing to obtain XPS test condition parameters;

[0128] Step S274: Detecting the surface composition of the positive electrode of the energy storage lithium battery system according to the XPS test condition parameters to obtain chemical state spectrum data of the lithium battery;

[0129] Step S275: performing peak decomposition and quantification of side reaction products on the lithium battery chemical state spectrum data to obtain side reaction product concentration data;

[0130] Step S276: constructing a side reaction model for the energy storage lithium battery system according to the battery reaction rate constant parameter and the side reaction product concentration data to obtain a lithium battery side reaction model;

[0131] Step S277: Based on the preset prediction cycle and the lithium battery side reaction model, the side reaction product evolution of the energy storage lithium battery system is predicted to obtain the positive electrode interface degradation trend data; the positive electrode interface degradation trend data is parameter extracted and integrated to obtain the positive electrode interface side reaction parameter set.

[0132] In this embodiment, the DSC heat flow curve data obtained using the MicroCal PEAQ-DSC microcalorimeter of TA Instruments were peak separated and fitted using the Origin software. In Origin, select the "Peak Analysis" tool in the "Analysis" menu to load the DSC heat flow curve data. Set the sensitivity of peak detection to 0.05mW. Use a Gaussian function to fit each detected peak, and adjust the fitting parameters to ensure that the goodness of fit between the fitting curve and the original data reaches more than 95%. Through the fitting results, the peak temperature, peak intensity, peak width and peak area of ​​each peak are extracted, and these data are the characteristic data of the side reaction enthalpy change. For example, a typical side reaction peak has the characteristics of a peak temperature of 45°C, a peak intensity of 0.2mW, a peak width of 5°C, and a peak area of ​​1.5mW·°C. For each side reaction peak temperature obtained in step S271, for example, 45°C, multiple temperature points (such as 40°C, 42.5°C, 45°C, 47.5°C, and 50°C) are selected near this temperature to repeat the DSC test on the energy storage lithium battery system. Use TAInstruments' MicroCal PEAQ-DSC microcalorimeter, maintain a heating rate of 2°C / min, a nitrogen atmosphere, and a flow rate of 50mL / min. Repeat the test 3 times at each temperature point. After the test is completed, the Arrhenius equation is used to analyze the heat flow data at different temperatures to calculate the reaction rate constant. For example, at 45°C, the reaction rate constant is 1.2×10 -3 s -1 , while at 50℃ it is 2.5×10 -3 s -1 Before X-ray photoelectron spectroscopy (XPS) testing, the energy storage lithium battery system needs to be surface treated. Use an ion beam etcher to clean the surface of the battery positive electrode and remove the surface oxide layer. The ThermoFisher Scientific K-Alpha XPS system was selected for testing. The XPS test condition parameters were set, including an X-ray source of Al Kα (1486.6eV), a beam spot diameter of 400μm, and a vacuum degree better than 1×10 -8Torr. Set the acquisition angle of the electron energy analyzer to 0° and the energy resolution to 0.5eV. Place the surface-treated energy storage lithium battery positive electrode sample on the sample stage of the XPS system. According to the set XPS test condition parameters, start the instrument to detect the surface composition of the positive electrode. The instrument automatically collects XPS spectrum data, including core energy spectra such as C1s, O1s, and Li1s. For example, the C1s spectrum shows a CC peak at 284.8eV and a CO peak at 286.5eV, and the O1s spectrum shows an O2- peak at 530.0eV. Use XPS Peak Fit software to perform peak decomposition on the obtained XPS spectrum data. Taking the C1s spectrum as an example, select the Gaussian-Lorentzian mixed function to fit the spectrum and decompose the peaks of different chemical states such as CC, CO, and C=O. By calculating the area ratio of each peak, the relative concentration of different chemical states is obtained. For example, the area ratio of the CO peak is 30%, and the area ratio of the C=O peak is 20%. Combined with the total carbon content of the cathode material, the absolute concentration of side reaction products (such as lithium carbonate) is calculated. Assuming the total carbon content of the cathode material is 10%, the concentration of lithium carbonate is 2%.

[0133] Combining the obtained reaction rate constant and the obtained side reaction product concentration data, a lithium battery side reaction model was constructed using MATLAB software. The model is based on the Arrhenius equation and the law of conservation of mass to describe the relationship between the generation rate of side reaction products and temperature and concentration. For example, assuming that the exponential relationship between the generation rate of lithium carbonate and temperature is k(T)=k0exp(-E a / RT), where k0 is the frequency factor, E a is the activation energy, R is the gas constant, and T is the absolute temperature. By fitting the experimental data, the model parameters k0 and E are determined. a , thus obtaining a complete lithium battery side reaction model. Based on the constructed lithium battery side reaction model, the prediction period is set to 6 months. MATLAB software is used to predict the evolution of side reaction products of the energy storage lithium battery system during this period. The concentration change of side reaction products at each time point is calculated using the numerical integration method. For example, the prediction results show that the concentration of lithium carbonate increases from 2% to 5% within 6 months. Parameters are extracted from the prediction results, including the concentration change rate and maximum concentration, and integrated into the positive electrode interface side reaction parameter set.

[0134] Preferably, configuring a potential step relaxation test device for the energy storage lithium battery system in step S3, and quantifying the degree of lithium deposition of the energy storage lithium battery system based on the potential step relaxation test device includes:

[0135] Perform potential step relaxation test equipment configuration on the energy storage lithium battery system and obtain electrochemical workstation parameters;

[0136] Based on the electrochemical workstation parameters, the microcirculation potential of the energy storage lithium battery system is monitored to obtain the original data of the negative electrode potential response;

[0137] Noise filtering is performed on the raw data of the negative electrode potential response to obtain purified lithium battery potential relaxation data;

[0138] Perform relaxation curve fitting on the potential relaxation data of purified lithium batteries to obtain potential relaxation characteristic parameters;

[0139] The lithium deposition criterion is established based on the potential relaxation characteristic parameters, and the critical potential threshold of lithium deposition is obtained;

[0140] The lithium deposition degree of the energy storage lithium battery system is quantified according to the critical potential threshold of lithium deposition and the potential relaxation data of the purified lithium battery, and the lithium deposition degree data of the lithium battery is obtained.

[0141] In this embodiment, a potential step relaxation test is performed using a Gamry Reference 3000 electrochemical workstation. The energy storage lithium battery system is connected to the test port of the electrochemical workstation. In the Gamry software interface, the test parameters are configured, including setting the potential step amplitude to 10mV, the step duration to 10 seconds, and the sampling frequency to 10Hz. After completing the configuration, record the parameter settings of the electrochemical workstation. According to the configured electrochemical workstation parameters, microcirculation potential monitoring is started. During the monitoring process, the electrochemical workstation applies a potential step to the battery according to the set step amplitude and duration, and records the response of the negative electrode potential in real time. During the monitoring process, the battery is kept in a floating charge state to simulate the actual usage scenario. After the monitoring is completed, the raw data of the negative electrode potential response are exported from the Gamry software. These data include the relaxation process after the potential step. The raw data of the negative electrode potential response are noise filtered using MATLAB software. In MATLAB, the raw data file is loaded, and the movmean function is used for moving average filtering. Select an appropriate window size, for example, a window size of 5 data points, and perform an average calculation on each data point and its two adjacent data points to remove random noise in the data. After filtering, the purified lithium battery potential relaxation data is obtained. In MATLAB, the relaxation curve is fitted on the purified lithium battery potential relaxation data. Use the fit function to select an exponential decay model (such as a single exponential or double exponential model) to fit the data. For example, for a single exponential decay model, the fitting formula is V(t) = V0 + Aexp(-t / τ), where V0 is the initial potential, A is the amplitude, and τ is the relaxation time constant. Through fitting, the potential relaxation characteristic parameters are obtained, including the relaxation time constant τ and amplitude A. These parameters can reflect the speed and amplitude of the potential relaxation process. Based on the obtained potential relaxation characteristic parameters and combined with existing lithium precipitation research data, a lithium precipitation criterion is established. By analyzing the relaxation time constant τ and amplitude A at different potentials, the critical potential threshold of lithium precipitation is determined. For example, it is found through experiments that when the relaxation time constant τ is less than 0.5 seconds and the amplitude A is greater than 10mV, lithium deposition occurs in the battery. Therefore, the critical potential threshold of lithium deposition is set to V threshold=V0-10mV, where V0 is the initial potential. This threshold can effectively distinguish between normal potential relaxation and potential changes caused by lithium plating. Using MATLAB software, the obtained critical potential threshold for lithium plating and the purified lithium battery potential relaxation data are combined to quantify the degree of lithium plating in the energy storage lithium battery system. The specific method is to compare the purified potential relaxation data with the critical potential threshold for lithium plating, and count the number and duration of data points exceeding the threshold. For example, if the proportion of data points exceeding the threshold reaches 10% and the duration exceeds 1 second, it is judged that the battery has obvious lithium plating. According to the degree of exceeding the threshold, the lithium plating degree data is quantified. For example, the lithium plating degree is divided into three levels: mild (1%-5%), moderate (5%-10%) and severe (>10%), and finally the lithium plating degree data of the lithium battery is obtained.

[0142] Preferably, the step S3 of predicting the capacity attenuation of the lithium battery based on the lithium deposition degree data of the lithium battery includes:

[0143] Prepare the energy storage lithium battery system for nuclear magnetic resonance spectroscopy testing and obtain NMR test parameter configuration; perform lithium metal quantitative testing on the energy storage lithium battery system based on the NMR test parameter configuration and obtain 7Li NMR spectrum data;

[0144] Perform peak decomposition and lithium deposition quantification on 7Li NMR spectrum data to obtain lithium deposition data of lithium battery negative electrode;

[0145] Based on the lithium battery negative electrode lithium plating data, the energy storage lithium battery system is configured with a scanning electron microscope to obtain the scanning electron microscope detection parameters;

[0146] Scan the negative electrode surface morphology of the energy storage lithium battery system according to the scanning electron microscope detection parameters to obtain the lithium deposition morphology characteristic data;

[0147] A simulation model of the energy storage lithium battery system is constructed to obtain the MD simulation parameter configuration; based on the MD simulation parameter configuration and the positive electrode interface side reaction parameter set, the cross-catalytic effect of the energy storage lithium battery system is carried out to obtain the intermolecular interaction energy data;

[0148] Based on the intermolecular interaction energy data, the catalytic reaction of the energy storage lithium battery system is simulated to obtain the cross-catalytic reaction activation energy data;

[0149] Based on the lithium deposition degree data of lithium batteries, the lithium deposition data of lithium batteries' negative electrodes and the activation energy data of cross-catalytic reactions, the coupling effect is evaluated to obtain the positive and negative electrode interface coupling parameters;

[0150] The capacity attenuation of the energy storage lithium battery system is predicted based on the positive and negative electrode interface coupling parameters, and the capacity drop inflection point prediction data is obtained.

[0151] In this embodiment, a Bruker AVANCE III 400MHz nuclear magnetic resonance spectrometer is used for quantitative detection of lithium metal. The negative electrode material in the energy storage lithium battery system is removed and sample preparation is performed. The NMR test parameters are configured in the Bruker TopSpin software, including setting the magnetic field strength to 400MHz, the pulse sequence to a standard spin echo sequence (CPMG), a repetition time of 2 seconds, an echo time of 0.5 milliseconds, and a scan number of 128 times. The selection of these parameters is based on the optimal detection effect of the lithium metal signal. After completing the configuration, the NMR test parameter configuration is recorded. The prepared negative electrode sample is placed in the sample tube of the NMR spectrometer, the Bruker AVANCE III 400MHz spectrometer is started, and the lithium metal quantitative detection is performed according to the set test parameters. During the detection process, the spectrometer automatically collects 7Li NMR signals and generates an NMR spectrum. After the detection is completed, the 7Li NMR spectrum data is exported from the TopSpin software, which contains the characteristic signals of lithium metal. The 7Li NMR spectrum data is peak decomposed using Origin software. In Origin, select the "Peak Analysis" tool from the "Analyze" menu and load the NMR spectrum data. Set the peak detection sensitivity to 0.01. Fit each detected peak using a Lorentzian function, adjusting the fitting parameters to ensure a goodness-of-fit of the fitted curve with the original data of at least 95%. Based on the fitting results, extract the peak area of ​​the lithium metal signal and, combined with the sample weight and the molar mass of lithium, calculate the lithium metal content in the anode material. For example, assuming a peak area of ​​1000, a sample weight of 1 gram, and a molar mass of lithium of 6.94 g / mol, the lithium metal content is 15%. This data represents the lithium deposition data from the lithium battery anode. Based on the obtained lithium deposition data from the lithium battery anode, select an appropriate scanning electron microscope (SEM) to scan the anode surface morphology. A Zeiss Supra 55 field emission scanning electron microscope was used with settings of 5 kV acceleration voltage, 10 mm working distance, and 1000x magnification. These parameters were selected based on optimal observation of the anode surface morphology. Select secondary electron imaging mode to obtain clear surface morphology images. After completing the configuration, record the SEM detection parameters. Place the processed negative electrode sample on the sample stage of the Zeiss Supra 55 scanning electron microscope, start the SEM, and scan the negative electrode surface morphology according to the set detection parameters. During the scanning process, the electron microscope automatically collects secondary electron signals and generates a morphological image of the negative electrode surface. After the scan is completed, the morphological images are exported from the control software of the electron microscope. These images clearly show the lithium deposition morphological characteristics of the negative electrode surface, including the size, distribution and morphology of the lithium deposition particles. For example, the image shows that the lithium deposition particles are distributed in a dendritic manner with an average diameter of 1 micron. These data are the lithium deposition morphological characteristic data.Use Materials Studio software to construct a molecular dynamics (MD) simulation model for the energy storage lithium battery system. In Materials Studio, select an appropriate force field (such as the COMPASS force field) and a model building module (such as the AmorphousCell module) to build a molecular model of the anode material. Set the model size to 10 nm × 10 nm × 10 nm, containing approximately 1000 lithium atoms and corresponding electrolyte molecules. Adjust the chemical bond parameters and reaction active sites in the model based on the positive electrode interface side reaction parameter set. Set the simulation parameters, including temperature to 300 K, pressure to 1 atmosphere, and simulation time to 10 nanoseconds. After completing the model construction and parameter configuration, record the MD simulation parameter configuration. Launch Materials Studio software and run a molecular dynamics simulation according to the MD simulation parameter configuration set in step 6. During the simulation, the software calculates the intermolecular interaction energy in real time, including van der Waals forces, Coulomb interactions, and hydrogen bonds. After the simulation is complete, extract the intermolecular interaction energy data from the analysis module in Materials Studio. For example, the interaction energy between the lithium atoms in the anode material and the electrolyte molecules is -0.5 eV. These data reflect the interaction strength between the negative electrode material and the electrolyte. Gaussian 16 software was used to simulate the catalytic reaction of the energy storage lithium battery system. In Gaussian 16, the obtained intermolecular interaction energy data was input, and an appropriate theoretical method (such as B3LYP / 6-31G*) was selected to perform a reaction path scan. By calculating the energy changes along the reaction path, the activation energy data of the cross-catalytic reaction was obtained. For example, the simulation results show that the activation energy of the cross-catalytic reaction between the lithium atoms in the negative electrode material and the electrolyte molecules is 0.8 eV. Combining the obtained lithium battery lithium deposition degree data, lithium battery negative electrode lithium deposition data and cross-catalytic reaction activation energy data, MATLAB software was used to evaluate the coupling effect. In MATLAB, a coupling model including the lithium deposition degree, negative electrode lithium deposition content and cross-catalytic reaction activation energy was established. Through model calculation, the positive and negative electrode interface coupling parameters were obtained, such as a coupling strength coefficient of 0.6 and a coupling reaction rate constant of 1.2×10-3. s-1. These parameters reflect the interaction strength and reaction rate between the positive and negative electrode interfaces. Using MATLAB software, a capacity decay prediction model was constructed based on the obtained positive and negative electrode interface coupling parameters. The model describes the change of battery capacity over time based on the electrochemical kinetic equation and coupling parameters. Through numerical simulation, the attenuation of battery capacity under different usage cycles is predicted. For example, the prediction results show that at the 100th cycle, the battery capacity will drop to 80% of the initial capacity, and at the 200th cycle, the capacity will drop to 60%. According to the prediction results, the capacity drop inflection point is determined to be the 150th cycle, at which time the capacity drop rate is significantly accelerated. These data are the capacity drop inflection point prediction data.

[0152] Preferably, step S4 includes the following steps:

[0153] Step S41: synchronously collecting data of the energy storage lithium battery system according to a preset three-level time scale collection system to obtain lithium battery time series synchronous collection parameters;

[0154] Step S42: performing time alignment of degradation information on lithium battery timing synchronous acquisition parameters according to calendar-microcirculation aging distribution weights to obtain a lithium battery synchronous degradation data table;

[0155] Step S43: extracting degradation feature vectors from the lithium battery synchronous degradation data table to obtain lithium battery degradation feature data;

[0156] Step S44: establishing a lithium battery state space model based on the lithium battery degradation characteristic data;

[0157] Step S45: solving the state covariance of the lithium battery state space model based on the lithium battery degradation characteristic data to obtain lithium battery state covariance data;

[0158] Step S46: performing covariance correction on the lithium battery state covariance data to obtain a Kalman filter parameter set;

[0159] Step S47: performing lithium battery state space modeling according to the Kalman filter parameter set to obtain a lithium battery health state transition equation;

[0160] Step S48: Evaluate the health status of the lithium battery based on the lithium battery health status transfer equation to obtain key health indicators of the lithium battery.

[0161] In this embodiment, a three-level time scale acquisition system is constructed using NI (National Instruments) data acquisition card and Lab VIEW software. The three time scales are: short-term (1 minute), medium-term (1 hour) and long-term (1 day). Short-term data is used to capture rapid dynamic changes, medium-term data is used to monitor short-term circulation effects, and long-term data is used to evaluate overall aging trends. The data acquisition card is configured using Lab VIEW software, and the sampling frequencies are set to 10 Hz for short-term, 1 Hz for medium-term and 0.1 Hz for long-term. The voltage, current and temperature sensors of the energy storage lithium battery system are connected to the data acquisition card to synchronously collect these parameters. After the acquisition is completed, the lithium battery time series synchronous acquisition parameters are obtained, including voltage, current and temperature data at different time scales. The obtained lithium battery time series synchronous acquisition parameters are processed using MATLAB software. According to the determined calendar-microcirculation aging distribution weight, the data at different time scales are time-aligned. The specific method is to use the long-term data as the benchmark and adjust the time axis of the medium-term and short-term data according to the aging weight. For example, if the calendar aging weight is 0.7 and the microcycle aging weight is 0.3, then, when time-aligned, the time axis of the medium-term data will be 70% closer to the long-term data, and the time axis of the short-term data will be 30% closer. After time alignment, the aligned data is organized into a table, the lithium battery synchronized degradation data table, containing the time-aligned voltage, current, and temperature data. Feature extraction is performed on the resulting lithium battery synchronized degradation data table in MATLAB. Principal component analysis (PCA) is used to extract degradation eigenvectors. The data table is normalized so that each feature has a mean of 0 and a standard deviation of 1. The data covariance matrix is ​​calculated and eigendecomposed to obtain the principal components and their corresponding eigenvalues. The principal component with a cumulative contribution rate of 95% is selected as the degradation eigenvector. For example, if the cumulative contribution rate of the first three principal components after PCA analysis is 95%, these three principal components are extracted as degradation eigenvectors. These eigenvectors effectively reflect the key characteristics of battery degradation, resulting in lithium battery degradation characteristic data. A lithium battery state-space model is established using MATLAB's System Identification Toolbox. The obtained lithium battery degradation characteristic data is used as input data, and the appropriate model structure (such as ARX, ARMAX or state-space model) is selected through the model identification function in the toolbox. For example, select the state-space model structure and set the order of the model to 3 (according to the number of eigenvectors). The toolbox will automatically fit the model parameters according to the input data to obtain the lithium battery state-space model. This model can describe the dynamic changes in the battery state. In MATLAB, the state covariance of the established lithium battery state-space model is solved. Use the lyap function to solve the continuous-time Lyapunov equation to obtain the state covariance matrix.Specifically, the system matrix (A matrix) and the noise covariance matrix (Q matrix) of the state-space model are input into the lyap function to calculate the state covariance matrix P. For example, assuming the system matrix A is a 3×3 matrix and the noise covariance matrix Q is the identity matrix, the state covariance matrix P is calculated using the lyap(A,Q) function. The state covariance matrix P reflects the uncertainty of the battery state. The obtained lithium battery state covariance data is covariance-corrected using MATLAB. The state covariance matrix P is adjusted to the actual measurement data through the design of a Kalman filter. Specifically, the Kalman function is used to calculate the Kalman gain K and the updated state covariance matrix P based on the measurement noise covariance matrix R and the process noise covariance matrix Q. For example, assuming the measurement noise covariance matrix R is a 3×3 diagonal matrix with diagonal elements of 0.1, 0.2, and 0.3, the Kalman gain K and the updated state covariance matrix P are calculated using the kalman(A,C,Q,R) function. The updated state covariance matrix P and Kalman gain K constitute the Kalman filter parameter set. Based on this Kalman filter parameter set, the lithium battery state-space model is reconstructed using MATLAB's ss function. The updated state covariance matrix P and Kalman gain K are input as model parameters to obtain a corrected state-space model. This model allows the lithium battery state-of-health transition equation to be derived, describing how the battery's state of health changes over time. For example, assume that the system matrix A, input matrix B, output matrix C, and direct transfer matrix D are: If C=1 0 0 and D=0, the state space model is constructed using the ss(A, B, C, D) function, and the lithium battery health state transition equation is obtained. For the detailed implementation process of step S48, please refer to the sub-steps of step S48.

[0162] It is particularly important that step S48 further includes the following steps:

[0163] Step S481: performing variational Bayesian inference based on the lithium battery health state transition equation to obtain variational Bayesian prior distribution parameters;

[0164] Step S482: performing variational inference on the energy storage lithium battery system based on the variational Bayesian prior distribution parameters and the lithium battery health state transition equation to obtain health state posterior distribution approximate data;

[0165] Step S483: performing Markov chain Monte Carlo sampling on the health state posterior distribution approximate data to obtain health state posterior distribution data;

[0166] Step S484: performing feature fusion based on the health status posterior distribution data and the positive electrode interface side reaction parameter set to obtain a comprehensive lithium battery health feature vector;

[0167] Step S485: performing feature importance assessment based on the comprehensive lithium battery health feature vector and the capacity drop inflection point prediction data to obtain lithium battery feature weight distribution data;

[0168] Step S486: performing principal component dimensionality reduction on the comprehensive lithium battery health feature vector according to the lithium battery feature weight distribution data to obtain reduced-dimensional lithium battery feature covariance data;

[0169] Step S487: Perform principal component projection transformation on the dimension-reduced lithium battery feature covariance data to obtain the lithium battery feature principal component score; collect key health indicators of the energy storage lithium battery system based on the lithium battery feature principal component score to obtain the lithium battery key health indicators.

[0170] In this embodiment, MATLAB software is used for variational Bayesian reasoning. According to the obtained lithium battery health state transition equation, the state variables and observation variables of the system are defined. Assume that the state variable is the health state indicator of the battery (such as capacity, internal resistance), and the observation variable is the actual measured battery performance data (such as voltage, current). In MATLAB, the fitdist function is used to perform distribution fitting on the observed data, assuming that the observed data obeys a Gaussian distribution, and the mean and variance of the observed data are obtained. According to the variational Bayesian theory, an appropriate prior distribution (such as a Gaussian distribution) is selected and the prior distribution parameters are initialized. For example, assume that the mean of the prior distribution is 0.5 and the variance is 0.1. These parameters are used as variational Bayesian prior distribution parameters. In MATLAB, the variational Bayesian reasoning method is used to infer the health state of the energy storage lithium battery system. Based on the obtained variational Bayesian prior distribution parameters and the health state transition equation, a variational Bayesian model is constructed. The parameters of the posterior distribution are updated by an iterative optimization algorithm (such as the coordinate ascent method) until convergence. The specific operation is to use MATLAB's vbem function (variational Bayesian expectation maximization algorithm) for iterative calculation. Assume that the initial iteration error is 0.01 and the maximum number of iterations is 100. After multiple iterations, the approximate data of the posterior distribution of the health state is obtained, including the mean and variance of the posterior distribution. Use MATLAB's statistics and machine learning toolbox for Markov chain Monte Carlo (MCMC) sampling. According to the obtained approximate data of the posterior distribution of the health state, set the initial parameters of MCMC sampling, including the sampling step and the number of samplings. For example, the Metropolis-Hastings algorithm is selected for sampling, and the sampling step is set to 0.05 and the number of samplings is 10,000. Sampling is performed through the mhsample function to obtain the posterior distribution data of the health state. In MATLAB, the obtained posterior distribution data of the health state is feature fused with the positive electrode interface side reaction parameter set. The positive electrode interface side reaction parameter set includes data such as the concentration of side reaction products and the reaction rate constant. By using the feature splicing method, the posterior distribution data (such as mean, variance) and the side reaction parameter set are combined into a comprehensive feature vector. For example, assuming that the mean of the posterior distribution is 0.6, the variance is 0.05, the side reaction product concentration is 0.1, and the reaction rate constant is 0.01, the comprehensive feature vector is [0.6, 0.05, 0.1, 0.01]. The feature importance of the comprehensive lithium battery health feature vector is evaluated using MATLAB's machine learning toolbox. Combined with the obtained capacity drop inflection point prediction data, a feature importance evaluation model is constructed. For example, a random forest algorithm is used to evaluate feature importance. In MATLAB, the Tree Bagger function is used to create a random forest model, and the number of trees is set to 100. The importance score of each feature is obtained through the OOBPredict or Importance property of the model.Assuming the importance scores of the four features in the comprehensive feature vector are 0.4, 0.3, 0.2, and 0.1, respectively, the lithium battery feature weight distribution data is [0.4, 0.3, 0.2, 0.1]. These weights reflect the degree of influence of different features on the battery health status. In MATLAB, principal component analysis (PCA) is used to reduce the dimensionality of the comprehensive lithium battery health feature vector. Based on the obtained feature weight distribution data, the first two features with larger weights are selected for principal component analysis. The comprehensive feature vector is then subjected to dimensionality reduction using the PCA function to obtain the reduced feature covariance data. For example, assuming the comprehensive feature vector is [0.6, 0.05, 0.1, 0.01] and the feature weight distribution is [0.4, 0.3, 0.2, 0.1], the first two features are selected for PCA analysis. In MATLAB, the reduced dimensionality lithium battery feature covariance data is subjected to principal component projection transformation. The output of the PCA function is used to obtain the score of each principal component. For example, assuming the feature covariance data after dimensionality reduction is [0.6, 0.05], the scores after principal component projection transformation are [0.8, 0.2]. These scores reflect the main characteristics of the battery's health status. Based on the principal component scores, the principal component with the highest score is selected as the key health indicator. For example, the first principal component is selected as the key health indicator. Its score of 0.8 indicates the main trend of battery health status, and the key health indicator of lithium batteries is finally obtained.

[0171] Preferably, step S5 includes the following steps:

[0172] Step S51: performing support vector machine classifier parameter configuration on the key health indicators of the lithium battery to obtain the SVM classifier parameter configuration;

[0173] Step S52: constructing training samples for key health indicators of lithium batteries according to the SVM classifier parameter configuration to obtain an SVM classification training data set;

[0174] Step S53: performing SVM model training based on the SVM classification training data set to obtain an SVM classifier model; performing cross-validation evaluation on the SVM classifier model to obtain SVM classifier performance evaluation data;

[0175] Step S54: classifying the health status of the energy storage lithium battery system according to the SVM classifier performance evaluation data and the key health indicators of the lithium battery to obtain health status grade classification data;

[0176] Step S55: performing historical data statistics on the energy storage lithium battery system based on the health status classification data to obtain health threshold adjustment benchmark data;

[0177] Step S56: performing adaptive threshold calculation on the energy storage lithium battery system according to the health threshold adjustment benchmark data to obtain the lithium battery health dynamic threshold parameter;

[0178] Step S57: setting warning rules for the energy storage lithium battery system based on the lithium battery health dynamic threshold parameters and health status classification data to obtain lithium battery health warning rules;

[0179] Step S58: Based on the lithium battery health warning rules and the capacity drop inflection point prediction data, the remaining life of the energy storage lithium battery system is evaluated to obtain the lithium battery corrected life prediction data.

[0180] In this embodiment, the machine learning toolbox of MATLAB is used to configure the parameters of the support vector machine (SVM) classifier. According to the obtained key health indicators of the lithium battery, a suitable kernel function is selected. For example, the radial basis function (RBF) is selected as the kernel function because it is suitable for nonlinear classification problems. In MATLAB, the fitcsvm function is used to configure the SVM classifier. Set the parameters of the kernel function, such as the penalty parameter C and the width σ of the kernel function. For example, set C=1, σ=0.5. The selection of these parameters is based on the preliminary analysis and experience of the battery health status data. After completing the configuration, record the SVM classifier parameter configuration. Based on the configured SVM classifier parameters, MATLAB is used to construct training samples for the key health indicators of the lithium battery. Battery samples with known health states are selected from historical data, including three states: normal, mildly aged, and severely aged. Assume that there are 100 samples for each state, and each sample contains two key health indicators (such as principal component scores). These sample data are divided into training sets and test sets, of which the training set accounts for 70% and the test set accounts for 30%. In MATLAB, the cvpartition function is used to partition the data. After the division is completed, the SVM classification training data set is obtained. In MATLAB, the obtained SVM classification training data set is used for model training. The SVM model is trained according to the configured parameters (such as C=1, σ=0.5) through the fitcsvm function. After the training is completed, the SVM classifier model is obtained. In order to evaluate the performance of the model, the cross-validation method is used. In MATLAB, the crossval function is used to perform 10-fold cross-validation on the model. By calculating the accuracy, recall rate and F1 score of the cross-validation, the SVM classifier performance evaluation data is obtained. For example, assuming that the average accuracy of the cross-validation is 90%, the recall rate is 85%, and the F1 score is 87%. Based on the obtained SVM classifier performance evaluation data and the key health indicators of the lithium battery, the health status of the energy storage lithium battery system is classified using MATLAB. The key health indicators in the test set are input into the trained SVM classifier model, and the model will output the health status category of each sample (such as normal, mild aging, and severe aging). According to the output results of the model, the health status level of the energy storage lithium battery system is divided. For example, suppose that in a test set, 30 samples are classified as normal, 20 samples are classified as mild aging, and 10 samples are classified as severe aging. These classification results constitute the health status classification data. In MATLAB, historical data statistics are performed on the obtained health status classification data. The distribution of each health status (normal, mild aging, severe aging) in the historical data is calculated. For example, the normal state samples account for 60% of the total samples, the mild aging state accounts for 30%, and the severe aging state accounts for 10%. The mean and standard deviation of the key health indicators for each state are calculated.For example, the mean of the key health indicators in the normal state is 0.8, with a standard deviation of 0.1; the mean of the mild aging state is 0.6, with a standard deviation of 0.2; and the mean of the severe aging state is 0.4, with a standard deviation of 0.3. These statistical results serve as benchmark data for adjusting health thresholds. Based on this baseline data for health threshold adjustment, MATLAB is used to perform adaptive threshold calculations for the energy storage lithium battery system. Dynamic thresholds are calculated based on the mean and standard deviation of the key health indicators for each health state. For example, assuming the threshold for the normal state is the mean minus 1 standard deviation (0.8 - 0.1 = 0.7), the threshold for the mild aging state is 0.6 - 0.2 = 0.4, and the threshold for the severe aging state is 0.4 - 0.3 = 0.1. These thresholds can be dynamically adjusted based on the distribution of historical data to adapt to changes in different health states. The resulting dynamic health threshold parameters for the lithium battery are [0.7, 0.4, 0.1]. In MATLAB, based on the obtained dynamic health threshold parameters and health state classification data, lithium battery health warning rules are set. For example, when the key health indicator falls below 0.7, a mild aging warning is issued; when it falls below 0.4, a severe aging warning is issued; and when it falls below 0.1, an urgent replacement warning is issued. These warning rules can be adjusted in real time based on dynamic threshold parameters to adapt to changes in battery health. The resulting lithium battery health warning rules are: Mild aging warning: Key health indicator < 0.7; Severe aging warning: Key health indicator < 0.4; Urgent replacement warning: Key health indicator < 0.1. For the detailed implementation process of step S58, please refer to the sub-steps of step S58.

[0181] It is particularly important that step S58 further includes the following steps:

[0182] Step S581: Initialize the particle filter algorithm parameters for the lithium battery health warning rule to obtain particle filter configuration data;

[0183] Step S582: Initialize particles according to the particle filter configuration data and the lithium battery health warning rules to obtain an initial lithium battery particle set;

[0184] Step S583: performing particle state prediction based on the initial lithium battery particle set to obtain predicted lithium battery particle state data;

[0185] Step S584: updating the weights and resampling the predicted lithium battery particle state data to obtain posterior lithium battery particle distribution data;

[0186] Step S585: performing a probability prediction of the remaining life of the energy storage lithium battery system based on the a posteriori lithium battery particle distribution data to obtain lithium battery life prediction trajectory data;

[0187] Step S586: performing coupled aging correction based on the lithium battery life prediction trajectory data and the capacity sudden drop inflection point prediction data to obtain the lithium battery corrected life prediction data.

[0188] In this embodiment, MATLAB software is used to initialize the parameters of the particle filter algorithm. According to the set lithium battery health warning rules, the basic parameters of the particle filter algorithm are determined. The total number of particles is set to 1000, and these particles represent different values ​​of the battery health state. The state distribution of the initialized particles is assumed to obey the Gaussian distribution, with a mean of 0.8 (close to the healthy state) and a standard deviation of 0.1. The resampling threshold of the particle filter is set to 0.5, and the resampling operation is triggered when the number of valid particles is lower than the threshold. After these parameters are configured, the initial configuration data of the particle filter is obtained. Based on the obtained particle filter configuration data, MATLAB is used to initialize the particles. According to the initialized particle state distribution (Gaussian distribution, mean 0.8, standard deviation 0.1), 1000 particles are randomly generated, each particle represents a battery health state. The initial weights of these particles are set to be equal, that is, the weight of each particle is 1 / 1000. These particles and their weights are organized into a set, namely the initial lithium battery particle set. This set contains the initial estimate of the battery health state. In MATLAB, the state prediction of the obtained initial lithium battery particle set is performed. Based on a dynamic model of the battery's state of health (for example, assuming the state of health degrades at a certain rate), the state of each particle is updated. Assuming a state of health degradation rate of 0.01 per month and a prediction period of one month, the new state of each particle is its initial state minus 0.01. For example, a particle with an initial state of 0.82 will have a predicted state of 0.81. After all particle states are updated, the predicted lithium battery particle state data is obtained. In MATLAB, the weights of the obtained predicted lithium battery particle state data are updated. Based on new measurement data (for example, new health indicator measurements), the likelihood function is used to update the weight of each particle. Assuming the measurement value is 0.8 and the measurement error follows a Gaussian distribution with a standard deviation of 0.05, the likelihood value of each particle is calculated based on the Gaussian distribution and its weight is updated. After the weight update is completed, the number of valid particles is checked to see if it is below the resampling threshold (0.5). If so, resampling is performed, retaining particles with high weights and discarding particles with low weights. The weights are redistributed to obtain the posterior lithium battery particle distribution data. In MATLAB, based on the obtained posterior lithium battery particle distribution data, a probabilistic prediction of the remaining life of the energy storage lithium battery system is performed. The remaining life is calculated for each particle, assuming that the battery life ends when the health status drops to 0.2. The remaining life of each particle is predicted based on the particle health status and degradation rate. For example, a particle with a health status of 0.81 has an estimated remaining life of 60 months ((0.81 - 0.2) / 0.01). The remaining life of all particles is statistically analyzed to obtain a probability distribution of the remaining life, ultimately generating the lithium battery life prediction trajectory data.In MATLAB, coupled aging correction is performed by combining the obtained lithium battery life prediction trajectory data with the capacity drop inflection point prediction data. Assuming the capacity drop inflection point is predicted to occur at month 50, at which point the battery capacity will rapidly decline. Based on this information, the life prediction trajectory is adjusted to make the predicted life expectancy more conservative before the drop inflection point. For example, for particles with a predicted life expectancy exceeding 50 months, their remaining life expectancy is proportionally shortened. After completing the correction, the corrected lithium battery life prediction data is obtained.

[0189] Preferably, the present invention further provides a health assessment system for an energy storage lithium battery system, which is used to perform the health assessment method for an energy storage lithium battery system as described above. The health assessment system for an energy storage lithium battery system includes:

[0190] The impedance analysis module is used to collect the wide-frequency complex impedance response data of the energy storage lithium battery system in the floating charge state, and perform frequency domain segmentation to obtain the lithium battery impedance characteristic data; based on the lithium battery impedance characteristic data, the calendar-microcycle aging distribution weight is determined;

[0191] The heat flow monitoring module is used to perform microcalorimetry on the energy storage lithium battery system to obtain real-time battery heat flow time series data; perform DSC testing on the energy storage lithium battery system based on the real-time battery heat flow time series data to obtain the DSC heat flow curve; and predict the evolution of side reaction products of the energy storage lithium battery system based on the DSC heat flow curve to obtain the positive electrode interface side reaction parameter set;

[0192] The lithium deposition quantification module is used to configure the potential step relaxation test equipment of the energy storage lithium battery system, and quantify the lithium deposition degree of the energy storage lithium battery system based on the potential step relaxation test equipment to obtain the lithium deposition degree data of the lithium battery; based on the lithium deposition degree data of the lithium battery, the lithium battery capacity attenuation is predicted to obtain the capacity sudden drop inflection point prediction data;

[0193] The health assessment module is used to construct a lithium battery health state transition equation based on the calendar-microcycle aging distribution weight; perform a lithium battery health state assessment based on the lithium battery health state transition equation to obtain key health indicators of the lithium battery;

[0194] The life prediction module is used to evaluate the remaining life of the energy storage lithium battery system based on the key health indicators of the lithium battery and the capacity drop inflection point prediction data, and obtain the corrected life prediction data of the lithium battery.

[0195] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0196] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily 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 is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A health assessment method for an energy storage lithium battery system, characterized in that: The following steps are involved: Step S1: collecting wide-frequency domain complex impedance response data of the energy storage lithium battery system in a floating charge state, and performing frequency domain segmentation to obtain lithium battery impedance characteristic data; Determine calendar-microcycle aging distribution weights based on lithium battery impedance characteristic data; Step S2: performing microcalorimetry on the energy storage lithium battery system to obtain real-time battery heat flow time series data; performing DSC testing on the energy storage lithium battery system based on the real-time battery heat flow time series data to obtain a DSC heat flow curve; predicting the evolution of side reaction products of the energy storage lithium battery system based on the DSC heat flow curve to obtain a positive electrode interface side reaction parameter set; Step S3: configuring a potential step relaxation test device for the energy storage lithium battery system, and quantifying the degree of lithium deposition of the energy storage lithium battery system based on the potential step relaxation test device to obtain lithium deposition degree data of the lithium battery; Based on the lithium battery lithium deposition degree data, the lithium battery capacity attenuation is predicted to obtain the capacity sudden drop inflection point prediction data; Step S4: constructing a lithium battery health state transfer equation based on the calendar-microcycle aging distribution weight; The health status of lithium batteries is evaluated based on the health status transfer equation of lithium batteries to obtain the key health indicators of lithium batteries; Step S5: Based on the key health indicators of the lithium battery and the capacity drop inflection point prediction data, the remaining life of the energy storage lithium battery system is evaluated to obtain the corrected life prediction data of the lithium battery.

2. The health assessment method for the energy storage lithium battery system according to claim 1, characterized in that: In step S1, the wide-frequency domain complex impedance response data of the energy storage lithium battery system in the floating charge state is collected and the frequency domain segmentation is performed, including: Configure the multi-channel data acquisition interface for the energy storage lithium battery system to obtain the electrochemical impedance spectroscopy test parameter configuration; Based on the electrochemical impedance spectroscopy test parameter configuration, a wide-frequency domain scan range is set for the energy storage lithium battery system to obtain the wide-frequency domain impedance scan parameters. The wide-frequency domain frequency scan range is set to 0.01Hz-10kHz, using a logarithmic distribution method, with 10 test points set for each frequency octave. The disturbance signal amplitude is set to 5mV, the phase accuracy is ±0.1°, the impedance accuracy is ±0.1%, and the single scan time is controlled within 30 minutes. The impedance spectrum of the energy storage lithium battery system is collected under floating charge state according to the wide-frequency domain impedance scanning parameters to obtain the original complex impedance response data; Perform noise filtering on the original complex impedance response data to obtain lithium battery purification impedance spectrum data; Normalize the lithium battery purification impedance spectrum data to generate a standard lithium battery impedance spectrum data set; The standard lithium battery impedance spectrum dataset is segmented in the frequency domain according to the preset frequency domain segmentation rules to obtain the lithium battery impedance characteristic data. The frequency domain is divided into three intervals: high frequency band, medium frequency band, and low frequency band. Four characteristic parameters of impedance real part, imaginary part, modulus value, and phase angle are extracted from each frequency band to form a 12-dimensional feature vector.

3. The health assessment method for the energy storage lithium battery system according to claim 1, characterized in that: Determining the calendar-microcycle aging distribution weight based on the lithium battery impedance characteristic data in step S1 includes: Select a wavelet basis function according to the lithium battery impedance characteristic data, and obtain wavelet basis function selection parameters; Based on the wavelet basis function selection parameters, the impedance data of each frequency point in the lithium battery impedance characteristic data is subjected to continuous wavelet transform to obtain the lithium battery impedance coefficient set in the time-frequency domain; Calculate the wavelet coefficient energy at each scale of the lithium battery impedance coefficient set in the time-frequency domain to obtain the energy distribution data at each scale; Based on the energy distribution data of each scale, the top 8 main scales with a concentrated energy proportion greater than 5% of the lithium battery impedance coefficient in the time-frequency domain are selected, and the corresponding wavelet coefficient real part, imaginary part, modulus and phase information are extracted to form a time-frequency domain impedance feature vector set; Perform Hilbert-Huang transform on the impedance eigenvector set in the time-frequency domain to obtain the impedance empirical mode decomposition parameters; According to the impedance empirical mode decomposition parameters, each dimension of the impedance eigenvector set in the time-frequency domain is decomposed dimension by dimension to obtain the initial set of eigenmode functions; Perform Hilbert transform on the initial set of intrinsic mode functions to obtain the modal instantaneous frequency and amplitude data; perform frequency characteristic statistics on the modal instantaneous frequency and amplitude data to obtain a multi-band intrinsic mode function set; The aging mechanism weights were calculated based on the multi-band intrinsic mode function set, and the calendar-microcirculation aging distribution weights were obtained.

4. The health assessment method for the energy storage lithium battery system according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: configuring a microcalorimetric device for the energy storage lithium battery system to obtain battery heat flow detection system parameters; Step S22: performing float charge state heat flow monitoring on the energy storage lithium battery system based on the battery heat flow detection system parameters to obtain original battery heat flow time series data; Step S23: performing baseline drift correction on the original battery heat flow time series data to obtain purified battery heat flow time series data; Step S24: identifying the heat flow peak value of the purification battery heat flow time series data to obtain the side reaction heat flow characteristic point; Step S25: pre-configuring DSC test parameters for the energy storage lithium battery system according to the side reaction heat flow characteristic points to obtain a DSC test parameter configuration; Step S26: performing a temperature program on the battery sample of the energy storage lithium battery system according to the DSC test parameter configuration, and recording a relationship curve between heat flow and temperature to obtain a DSC heat flow curve; Step S27: Predicting the evolution of side reaction products of the energy storage lithium battery system based on the DSC heat flow curve to obtain a positive electrode interface side reaction parameter set.

5. The health assessment method for the energy storage lithium battery system according to claim 4, characterized in that: Step S27 includes the following steps: Step S271: performing peak separation and fitting on the DSC heat flow curve to obtain characteristic data of side reaction enthalpy change, wherein the characteristic data of side reaction enthalpy change includes peak temperature, peak intensity, peak width and peak area; Step S272: Repeating DSC testing on battery samples of the energy storage lithium battery system at different temperature points at each peak temperature, and calculating reaction rate constants at different temperatures to obtain battery reaction rate constant parameters; Step S273: preparing the energy storage lithium battery system for X-ray photoelectron spectroscopy testing to obtain XPS test condition parameters; Step S274: Detecting the surface composition of the positive electrode of the energy storage lithium battery system according to the XPS test condition parameters to obtain chemical state spectrum data of the lithium battery; Step S275: performing peak decomposition and quantification of side reaction products on the lithium battery chemical state spectrum data to obtain side reaction product concentration data; Step S276: constructing a side reaction model for the energy storage lithium battery system according to the battery reaction rate constant parameter and the side reaction product concentration data to obtain a lithium battery side reaction model; Step S277: Based on the preset prediction cycle and the lithium battery side reaction model, the side reaction product evolution of the energy storage lithium battery system is predicted to obtain the positive electrode interface degradation trend data; the positive electrode interface degradation trend data is parameter extracted and integrated to obtain the positive electrode interface side reaction parameter set.

6. The health assessment method for the energy storage lithium battery system according to claim 1, characterized in that: In step S3, configuring a potential step relaxation test device for the energy storage lithium battery system and quantifying the degree of lithium deposition of the energy storage lithium battery system based on the potential step relaxation test device includes: Perform potential step relaxation test equipment configuration on the energy storage lithium battery system and obtain electrochemical workstation parameters; Based on the electrochemical workstation parameters, the microcirculation potential of the energy storage lithium battery system is monitored to obtain the original data of the negative electrode potential response; Noise filtering is performed on the raw data of the negative electrode potential response to obtain purified lithium battery potential relaxation data; Perform relaxation curve fitting on the potential relaxation data of purified lithium batteries to obtain potential relaxation characteristic parameters; The lithium deposition criterion is established based on the potential relaxation characteristic parameters, and the critical potential threshold of lithium deposition is obtained; The lithium deposition degree of the energy storage lithium battery system is quantified according to the critical potential threshold of lithium deposition and the potential relaxation data of the purified lithium battery, and the lithium deposition degree data of the lithium battery is obtained.

7. The health assessment method for an energy storage lithium battery system according to claim 1, characterized in that: The step S3 of predicting the capacity attenuation of the lithium battery based on the lithium deposition degree data of the lithium battery includes: Prepare the energy storage lithium battery system for nuclear magnetic resonance spectroscopy testing and obtain NMR test parameter configuration; perform lithium metal quantitative testing on the energy storage lithium battery system based on the NMR test parameter configuration and obtain 7Li NMR spectrum data; Peak decomposition and lithium deposition quantification were performed on the 7Li NMR spectrum data to obtain lithium deposition data of the negative electrode of the lithium battery; Based on the lithium battery negative electrode lithium plating data, the energy storage lithium battery system is configured with a scanning electron microscope to obtain the scanning electron microscope detection parameters; Scan the negative electrode surface morphology of the energy storage lithium battery system according to the scanning electron microscope detection parameters to obtain the lithium deposition morphology characteristic data; A simulation model of the energy storage lithium battery system is constructed to obtain the MD simulation parameter configuration; based on the MD simulation parameter configuration and the positive electrode interface side reaction parameter set, the cross-catalytic effect of the energy storage lithium battery system is carried out to obtain the intermolecular interaction energy data; Based on the intermolecular interaction energy data, the catalytic reaction of the energy storage lithium battery system is simulated to obtain the cross-catalytic reaction activation energy data; Based on the lithium deposition degree data of lithium batteries, the lithium deposition data of lithium batteries' negative electrodes and the activation energy data of cross-catalytic reactions, the coupling effect is evaluated to obtain the positive and negative electrode interface coupling parameters; The capacity attenuation of the energy storage lithium battery system is predicted based on the positive and negative electrode interface coupling parameters, and the capacity drop inflection point prediction data is obtained.

8. The health assessment method for an energy storage lithium battery system according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: synchronously collecting data of the energy storage lithium battery system according to a preset three-level time scale collection system to obtain lithium battery time series synchronous collection parameters; Step S42: performing time alignment of degradation information on lithium battery timing synchronous acquisition parameters according to calendar-microcirculation aging distribution weights to obtain a lithium battery synchronous degradation data table; Step S43: extracting degradation feature vectors from the lithium battery synchronous degradation data table to obtain lithium battery degradation feature data; Step S44: establishing a lithium battery state space model based on the lithium battery degradation characteristic data; Step S45: solving the state covariance of the lithium battery state space model based on the lithium battery degradation characteristic data to obtain lithium battery state covariance data; Step S46: performing covariance correction on the lithium battery state covariance data to obtain a Kalman filter parameter set; Step S47: performing lithium battery state space modeling according to the Kalman filter parameter set to obtain a lithium battery health state transition equation; Step S48: Evaluate the health status of the lithium battery based on the lithium battery health status transfer equation to obtain key health indicators of the lithium battery.

9. The health assessment method for an energy storage lithium battery system according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: performing support vector machine classifier parameter configuration on the key health indicators of the lithium battery to obtain the SVM classifier parameter configuration; Step S52: constructing training samples for key health indicators of lithium batteries according to the SVM classifier parameter configuration to obtain an SVM classification training data set; Step S53: performing SVM model training based on the SVM classification training data set to obtain an SVM classifier model; performing cross-validation evaluation on the SVM classifier model to obtain SVM classifier performance evaluation data; Step S54: classifying the health status of the energy storage lithium battery system according to the SVM classifier performance evaluation data and the key health indicators of the lithium battery to obtain health status grade classification data; Step S55: performing historical data statistics on the energy storage lithium battery system based on the health status classification data to obtain health threshold adjustment benchmark data; Step S56: performing adaptive threshold calculation on the energy storage lithium battery system according to the health threshold adjustment benchmark data to obtain the lithium battery health dynamic threshold parameter; Step S57: setting warning rules for the energy storage lithium battery system based on the lithium battery health dynamic threshold parameters and the health status classification data to obtain lithium battery health warning rules; Step S58: Based on the lithium battery health warning rules and the capacity drop inflection point prediction data, the remaining life of the energy storage lithium battery system is evaluated to obtain the lithium battery corrected life prediction data.

10. A health assessment system for an energy storage lithium battery system, characterized in that: For executing the health assessment method of the energy storage lithium battery system according to claim 1, the health assessment system of the energy storage lithium battery system comprises: The impedance analysis module is used to collect the wide-frequency complex impedance response data of the energy storage lithium battery system in the floating charge state, and perform frequency domain segmentation to obtain the lithium battery impedance characteristic data; based on the lithium battery impedance characteristic data, the calendar-microcycle aging distribution weight is determined; The heat flow monitoring module is used to perform microcalorimetry on the energy storage lithium battery system to obtain real-time battery heat flow time series data; perform DSC testing on the energy storage lithium battery system based on the real-time battery heat flow time series data to obtain the DSC heat flow curve; and predict the evolution of side reaction products of the energy storage lithium battery system based on the DSC heat flow curve to obtain the positive electrode interface side reaction parameter set; The lithium deposition quantification module is used to configure the potential step relaxation test equipment of the energy storage lithium battery system, and quantify the lithium deposition degree of the energy storage lithium battery system based on the potential step relaxation test equipment to obtain the lithium deposition degree data of the lithium battery; based on the lithium deposition degree data of the lithium battery, the lithium battery capacity attenuation is predicted to obtain the capacity sudden drop inflection point prediction data; The health assessment module is used to construct a lithium battery health state transition equation based on the calendar-microcycle aging distribution weight; perform a lithium battery health state assessment based on the lithium battery health state transition equation to obtain key health indicators of the lithium battery; The life prediction module is used to evaluate the remaining life of the energy storage lithium battery system based on the key health indicators of the lithium battery and the capacity drop inflection point prediction data, and obtain the corrected life prediction data of the lithium battery.

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