Battery Acoustic Analysis Using Reference Models for SoH Detection
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Solution Overview
Problem
Current acoustic signal-based analysis techniques face challenges in effectively processing large volumes of data to derive meaningful insights on the state of health and charge of batteries, particularly due to the variability in battery chemistry and geometry, making it difficult to obtain actionable results in battery system design, production, and use.
Innovation Solution
The method involves obtaining acoustic response signal data from battery cells by transmitting acoustic excitation signals and recording response vibrations, using reference models generated from previous data to determine characteristics such as state of charge, state of health, and construction quality, and updating these models using machine learning techniques to improve analysis accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If acoustic signal based techniques are used to inspect batteries over long duration to obtain high-resolution waveforms, then measurement precision is improved, but data processing complexity increases
Solution Approach 1:
The patent extracts and isolates specific acoustic features (resonant frequencies, damping ratios, mode shapes) from the complex waveform data. By focusing on these key extracted features rather than processing the entire high-resolution waveform, the system maintains measurement precision while significantly reducing data processing complexity.
Solution Approach 2:
The patent replaces complex mechanical signal processing with computational methods. Instead of using traditional digital signal processing techniques that require extensive computational resources, the system uses machine learning models trained on acoustic features to predict battery state, substituting mechanical processing complexity with more efficient computational approaches.
2Adaptability or versatility
If acoustic signal based techniques are applied to batteries with varying chemistry and geometry, then adaptability is improved, but measurement precision deteriorates
Solution Approach 1:
The patent segments the battery analysis into distinct acoustic modes (resonant frequencies, damping ratios, mode shapes) that can be independently analyzed. This segmentation allows the system to adapt to different battery chemistries and geometries by focusing on mode-specific characteristics rather than requiring a universal analysis approach, thereby maintaining precision across diverse battery types.
Solution Approach 2:
The patent utilizes parameter changes in acoustic resonance characteristics (resonant frequencies, damping ratios) that naturally occur with different battery chemistries and geometries. By training machine learning models on these varying parameters across diverse battery types, the system achieves both adaptability to different battery configurations and maintained measurement precision through learned parameter relationships.
3Loss of information
If large quantities of acoustic data are collected for battery analysis, then information completeness is improved, but loss of time in processing increases
Solution Approach 1:
The patent performs preliminary action by pre-processing acoustic data to extract key features (resonant frequencies, damping ratios, mode shapes) before main analysis. This preliminary feature extraction reduces the dimensionality of the data while preserving essential information, enabling faster subsequent processing without significant loss of diagnostic capability.
Solution Approach 2:
The patent creates simplified copies of the complex acoustic data by generating feature representations (resonant frequencies, damping ratios) that capture the essential diagnostic information. These feature copies serve as surrogate data that can be processed much faster than the original high-resolution waveforms while maintaining the ability to derive meaningful battery state information.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the generation of actionable insights and improves the accuracy of battery analysis by effectively processing acoustic signal data, providing detailed information on battery performance and lifecycle, thereby enhancing battery design, production, and usage.
Implementation Method 1
acoustic signal based techniques such as electrochemical-acoustic signal interrogation (EASI), acoustic resonance spectroscopy (ARS), resonant ultrasound spectroscopy (RUS)
Implementation Method 2
transmitting one or more acoustic excitation signals into at least the portion of the battery cell and recording response vibration signals
Data Source
AI summary
Systems and methods for acoustic signal based analysis, include obtaining acoustic response signal data of at least a portion of a battery cell, the acoustic response signal data comprising waveforms generated by transmitting one or more acoustic excitation signals into at least the portion of the battery cell and recording response vibration signals to the one or more acoustic excitation signals. One or more metrics are determined from at least the acoustic response signal data, the one or more metrics being determined based on correlation of the one or more metrics to one or more characteristics of battery cells and a reference model is generated from the one or more metrics. A test battery can be evaluated using the reference model. Actionable insights or recommendations can be generated based on the evaluation. The reference model can also be updated based on the evaluation.