Battery Acoustic Analysis Using Resonance Features and Reference Models
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Solution Overview
Problem
Current acoustic signal based analysis techniques for batteries face challenges in effectively processing large volumes of data to derive meaningful insights on state of health and charge, production quality, and other characteristics due to the variability in battery chemistry and geometry.
Innovation Solution
The use of acoustic signal based analysis techniques, including electrochemical-acoustic signal interrogation (EASI), acoustic resonance spectroscopy (ARS), resonant ultrasound spectroscopy (RUS), and nonlinear acoustic resonance spectroscopy (NARS), combined with machine learning and reference models, to process acoustic response signal data and determine characteristics such as state of charge, state of health, and construction quality of batteries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If acoustic signal based analysis techniques are applied to inspect batteries over a long duration of time, then high-resolution waveforms and detailed battery characteristics can be obtained, but large quantities of raw data are generated that are difficult to process and analyze
Solution Approach 1:
The patent extracts only the most relevant features from the large volumes of acoustic signal data using machine learning algorithms. Instead of processing all raw data, the system identifies and extracts key characteristics such as resonance frequencies, amplitude patterns, and temporal features that are most indicative of battery state of health and charge levels, thereby simplifying the processing burden while maintaining analytical accuracy.
Solution Approach 2:
The patent transforms the acoustic signal data from its original complex waveform form into simplified parameter representations through feature extraction. By converting time-domain signals into frequency-domain characteristics and other meaningful parameters, the system reduces data dimensionality and complexity while preserving the essential information needed for battery diagnostics.
2Ease of operation
If acoustic signal based analysis is used to determine battery characteristics, then non-invasive inspection is achieved, but the variability in battery chemistry and geometry makes it challenging to derive meaningful insights from the data
Solution Approach 1:
The patent implements preliminary action by training machine learning models in advance using datasets that encompass the full range of battery chemistries and geometries. These pre-trained models learn to account for variations in battery design and chemistry, enabling them to accurately interpret acoustic signals from diverse battery types without requiring real-time adjustments or recalibration for each specific battery variant.
Solution Approach 2:
The patent creates a universal analysis system that can handle multiple battery types, chemistries, and geometries through a single machine learning framework. The system is designed to be multi-functional, accommodating different battery form factors and chemical compositions by learning common acoustic patterns across diverse battery types, thereby enabling meaningful insights extraction without requiring type-specific analysis procedures.
3Loss of information
If large volumes of acoustic signal data are collected for battery analysis, then comprehensive information on state of health and charge is obtained, but the data requires significant storage, transmission, and processing resources
Solution Approach 1:
The patent extracts only the essential information from the comprehensive acoustic signal datasets, isolating the specific features that directly correlate with battery state of health and charge characteristics. By filtering out redundant and irrelevant data elements, the system retains the critical information needed for accurate battery assessment while dramatically reducing the overall data volume that requires storage and processing.
Solution Approach 2:
The patent segments the large volumes of acoustic signal data into manageable feature sets that can be processed independently. By dividing the comprehensive dataset into distinct feature categories (such as frequency domain features, time domain features, and spectral characteristics), the system enables efficient processing and storage of battery information in modular units that are easier to manage and analyze.
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 allows for the generation of actionable insights and dynamic updating of reference models, enabling efficient analysis of battery performance and quality, even under varying conditions, by correlating acoustic signal features with battery characteristics.
Implementation Method 1
acoustic resonance spectroscopy (ARS), resonant ultrasound spectroscopy (RUS), nonlinear acoustic resonance spectroscopy (NARS)
Implementation Method 2
recording response vibration signals to the one or more acoustic excitation 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.