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CN-121978535-A - Power battery degradation mechanism analysis and health state estimation method

CN121978535ACN 121978535 ACN121978535 ACN 121978535ACN-121978535-A

Abstract

The invention discloses a power battery decay mechanism analysis and health state estimation method, which particularly relates to the technical field of battery management, and comprises the following steps of S1, synchronously collecting ultrasonic scanning, distributed temperature sensing and electrochemical impedance spectrum signals of a battery, S2, extracting and fusing multiple physical field characteristics, constructing an acoustic-thermal-electric coupling characteristic spectrum, S3, decoupling and quantifying an internal decay mechanism based on the characteristic spectrum, S4, finally combining a mechanism model and a data driving model, and cooperatively estimating the health state of the battery through a self-adaptive fusion algorithm. The invention realizes the on-line, nondestructive and quantitative analysis of key decay mechanisms such as lithium precipitation, cracks and the like, remarkably improves the accuracy of health state estimation and early warning capability, and provides a complete solution for accurate management of batteries.

Inventors

  • MO MINGLI

Assignees

  • 重庆开放大学重庆工商职业学院

Dates

Publication Date
20260505
Application Date
20260202

Claims (10)

  1. 1. A power battery degradation mechanism analysis and health state estimation method is characterized by comprising the following steps: s1, synchronously acquiring multi-physical-field in-situ sensing signals of a battery in the running process, wherein the signals at least comprise an actively excited ultrasonic scanning signal, a distributed temperature sensing signal and an electrochemical impedance spectrum signal; s2, extracting characteristic parameters capable of representing the internal mechanical structure state, the thermal behavior state and the electrochemical reaction dynamics state of the battery from an ultrasonic scanning signal, a distributed temperature sensing signal and an electrochemical impedance spectrum signal respectively, and fusing and constructing a time-space associated sound-heat-electricity coupling characteristic spectrum; S3, decoupling and quantitatively analyzing a decay mechanism in the battery based on the acoustic-thermal-electric coupling characteristic spectrum to obtain at least one quantification index of the decay mechanism; S4, utilizing quantitative indexes of a decay mechanism, combining a mechanism model and a data driving model, and cooperatively estimating the health state of the battery through an information fusion algorithm.
  2. 2. The method for analyzing and estimating a health state of a power battery according to claim 1, wherein in the step S1, the synchronous acquisition is specifically implemented by triggering a cooperative measurement period in a standing phase of the battery or a voltage platform phase of constant current charging, and controlling an ultrasonic excitation unit, an impedance excitation unit and a temperature acquisition unit to perform millisecond-level time synchronous signal acquisition.
  3. 3. The method for analyzing the degradation mechanism and estimating the health of a power battery according to claim 1, wherein extracting the characteristic parameters from the ultrasonic scan signal in step S2 comprises processing the received ultrasonic echo signal to extract an acoustic characteristic vector related to a microstructure change inside the battery, wherein the acoustic characteristic vector at least comprises one or more of a propagation sound velocity of an acoustic wave passing through each layer of the battery, a signal energy attenuation coefficient, a spectral amplitude of a specific frequency band, or a scattering intensity.
  4. 4. The method for power cell degradation mechanism analysis and state of health estimation according to claim 1, wherein extracting the characteristic parameters from the distributed temperature sensing signal in step S2 comprises calculating a thermal characteristic vector based on temperature sensor readings disposed at a plurality of locations on the surface of the battery, wherein the thermal characteristic vector comprises at least one or more of a temperature field spatial gradient, a maximum temperature rise rate, and a temperature distribution non-uniformity index.
  5. 5. The method of claim 1, wherein extracting the characteristic parameters from the electrochemical impedance spectrum signal in step S2 comprises analyzing the measured impedance spectrum to extract an electrochemical eigenvector, wherein the electrochemical eigenvector comprises at least one or more of an ohmic internal resistance, a charge transfer resistance, a Warburg diffusion coefficient, and a phase angle at one or more characteristic frequency points.
  6. 6. The method for analyzing power battery degradation mechanism and estimating health state according to claim 1, wherein in step S2, the acoustic-thermal-electric coupling characteristic spectrum is constructed by splicing and aligning acoustic characteristic vectors, thermal characteristic vectors and electrochemical characteristic vectors associated with spatial positions under the same time stamp to form a multidimensional characteristic matrix serving as a coupling characteristic spectrum representing a battery comprehensive state at the moment.
  7. 7. The method for analyzing and estimating a state of health of a power battery according to claim 1, wherein the step S3 comprises: s3.1, establishing a standard characteristic spectrum template library corresponding to different dominant decay mechanisms through experiments in advance; And S3.2, carrying out matching decomposition on the on-line constructed acoustic-thermal-electric coupling characteristic spectrum and a standard characteristic spectrum template library, and calculating to obtain contribution coefficients of each decay mechanism template to the current battery state, wherein the contribution coefficients are quantization indexes of the decay mechanism.
  8. 8. The method of claim 7, wherein the dominant degradation mechanism comprises at least two of solid electrolyte interfacial film growth, metal lithium precipitation, active material loss, electrode material particle crack growth, and wherein the quantitative indicator comprises a lithium evolution index for characterizing lithium evolution severity and/or a crack index for characterizing structural crack growth.
  9. 9. The method for analyzing and estimating a state of health of a power battery according to claim 1, wherein the step S4 comprises: S4.1, establishing a battery electrochemical-mechanical coupling decay model which comprises a decay mechanism quantization index as a state variable, and updating the model by utilizing the quantization index to obtain a first health state estimated value and a first uncertainty based on the mechanism model; S4.2, taking the acoustic-thermal-electric coupling characteristic spectrum or the main component after dimension reduction as input, and obtaining a second health state estimated value and a second uncertainty through a data driving model; And S4.3, based on the first uncertainty and the second uncertainty, adopting a self-adaptive weighted fusion algorithm to fuse the first health state estimated value and the second health state estimated value, and outputting a final battery health state estimated value and a confidence interval.
  10. 10. The method for analyzing and estimating a health state of a power battery according to claim 9, wherein in step S4.3, the adaptive weighted fusion algorithm is a fusion framework based on Kalman filtering or Bayesian estimation, and the weights of the fusion framework are dynamically adjusted according to the covariance of the estimated error of each model under the current working condition.

Description

Power battery degradation mechanism analysis and health state estimation method Technical Field The invention relates to the technical field of battery management, in particular to a power battery degradation mechanism analysis and health state estimation method. Background Along with the rapid development of electric automobiles and large-scale energy storage industry, the long-term operation reliability and safety of a power battery become a core concern, and the battery can generate complex physical and chemical changes in the process of recycling, so that the performance of the battery can be irreversibly degenerated, therefore, the accurate evaluation of the battery health state is a key for realizing efficient battery management, and the deep analysis of the internal degeneration mechanism is a fundamental basis for realizing accurate evaluation, early warning and life prediction. Currently, widely studied battery state of health estimation methods can be categorized into three main categories, data-driven methods, model-driven methods, and feature-related methods, which generally rely on macroscopically measurable signals (e.g., voltage, current, surface temperature) external to the battery, and the internal state is inferred by indirect processing or fitting of these signals. However, the fundamental limitation of the common existence of the method is that the method lacks an effective means capable of directly sensing the internal multi-physical field state on line, without damage and with spatial resolution under the actual operation condition of the battery, so that the prior art is difficult to distinguish and quantitatively analyze various coupling degradation mechanisms such as lithium precipitation, electrode active material loss, particle crack extension, SEI film growth and the like, and further the problems of insufficient early warning capability, poor physical interpretation, weak extrapolation under the variable working condition and the like of the health state estimation result are caused. In view of the above, the present invention provides a method for analyzing the degradation mechanism and estimating the health status of a power battery to solve the above-mentioned shortcomings in the prior art. Disclosure of Invention In order to overcome the above-mentioned drawbacks of the prior art, the present invention provides a method for analyzing a degradation mechanism and estimating a health state of a power battery, so as to solve the above-mentioned problems in the prior art. In order to achieve the purpose, the invention provides the following technical scheme that the power battery degradation mechanism analysis and health state estimation method specifically comprises the following steps: s1, synchronously acquiring multi-physical-field in-situ sensing signals of a battery in the running process, wherein the signals at least comprise an actively excited ultrasonic scanning signal, a distributed temperature sensing signal and an electrochemical impedance spectrum signal; s2, extracting characteristic parameters capable of representing the internal mechanical structure state, the thermal behavior state and the electrochemical reaction dynamics state of the battery from an ultrasonic scanning signal, a distributed temperature sensing signal and an electrochemical impedance spectrum signal respectively, and fusing and constructing a time-space associated sound-heat-electricity coupling characteristic spectrum; S3, decoupling and quantitatively analyzing a decay mechanism in the battery based on the acoustic-thermal-electric coupling characteristic spectrum to obtain at least one quantification index of the decay mechanism; S4, utilizing quantitative indexes of a decay mechanism, combining a mechanism model and a data driving model, and cooperatively estimating the health state of the battery through an information fusion algorithm. Preferably, in step S1, the synchronous acquisition is specifically that a cooperative measurement period is triggered in a standing stage of the battery or a voltage platform stage of constant current charging, and the ultrasonic excitation unit, the impedance excitation unit and the temperature acquisition unit are controlled to perform millisecond-level time synchronous signal acquisition. Preferably, in step S2, extracting the characteristic parameters from the ultrasonic scanning signals comprises processing the received ultrasonic echo signals, and extracting acoustic characteristic vectors related to the microstructure changes inside the battery, wherein the acoustic characteristic vectors at least comprise one or more of sound velocity of sound wave propagation, signal energy attenuation coefficient, spectral amplitude of a specific frequency band or scattering intensity of sound waves passing through each layer of the battery. Preferably, in step S2, extracting the characteristic parameter from the distributed temperature sensing signal comprises c