Abstract: The present disclosure provides a non-invasive and acoustic signal-based approach for examining a quality of SEI formation for any given battery cell and providing an objective assessment thereof. In one example, the objective assessment may be provided as a score that may be referred to as a Solid Electrolyte Interphase (SEI) score for a given battery cell. In one aspect, a method includes transmitting acoustic signals through a battery cell via one or more first transducers, receiving response signals in response to the acoustic signals at one or more second transducers, determining a score indicative of quality of SEI formation in the battery cell based on analyzing the response signals, and outputting the score.

Abstract: 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.

Abstract: Systems, techniques, and computer-implemented processes for cycle life performance determination of batteries using non-invasive acoustic solutions. In one aspect, a battery inspection system includes a plurality of transducers, and a controller communicatively coupled to the plurality of transducers. The controller is configured to send one or more commands to a first subset of the plurality of transducers for transmitting acoustic signals through a battery cell, receive, from a second subset of the plurality of transducers, response signals in response to the acoustic signals transmitted through the battery cell, and determine a cycle life performance score for the battery cell based on at least the response signals, the score indicating an estimated number of charge-discharge cycles that the battery cell goes through prior to reaching a threshold retention capacity.

Abstract: One or more aspects of the present disclosure are directed to a synchronized actuation system for holding samples (e.g., battery cells) in place such that ultrasonic sensors can make physical contacts with the samples to transmit and receive acoustic signals across the samples for purposes of making ultrasound measurements of the battery sample. The movement of sensors on both sides of a sample can be synchronized (as opposed to being handled via independent actuators) to improve signal repeatability.

Abstract: Provided is an approach for fast and efficient analysis of acoustic signals to detect anomalies in battery cells and using the results of such analysis for intelligent selection of a subset of battery cells for in-depth quality control analysis. In one aspect, a system includes a first acoustic scanner configured to scan a battery cell; determine if the battery cell is potentially anomalous or not; and send the battery cell for a high-resolution scan if the battery cell is determined to be potentially anomalous.

Abstract: The present disclosure is directed to similarity-based defect detection in battery cells without retraining utilized neural networks to identify new and unknown defects. In one aspect, a method includes receiving, as first input into a trained neural network, at least one first signal representative of acoustic measurements of at least one reference battery cell; receiving, as second input into the trained neural network, a second signal representative of acoustic measurements of an under-the-test battery cell; performing, using the trained neural network, an analysis of the at least one first signal and the second signal to determine if the second signal has a threshold similarity to the at least one first signal; and generating, as an output of the trained neural network, a defect identification for the under-the-test battery cell, based on whether the second has the threshold similarity to the at least one first signal or not.

Abstract: Systems and methods for analyzing physical characteristics of a battery include arrangements of two or more transducers coupled to the battery. A control module controls one or more of the two or more transducers to transmit acoustic signals through at least a portion of the battery, and one or more of the two or more transducers to receive response acoustic signals. Distribution of physical properties of the battery is determined based at least on the transmitted acoustic signals and the response acoustic signals.

Abstract: Systems, techniques, and computer-implemented processes for calibration of transducers used for acoustic signal-based analysis are disclosed. In one aspect, a calibration method includes capturing a plurality of signals for a plurality of channels, each of the plurality of channels being associated with a pair of transducers formed of a transmitting transducer and a receiving transducer; and for each of the plurality of channels, determining a corresponding peak intensity for a corresponding signal of the plurality of signals; normalizing the corresponding signal based at least on the corresponding peak intensity to yield a corresponding normalized signal; and applying a time-shift to the normalized signal.

Abstract: 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.

Abstract: Systems and techniques for measuring process characteristics including electrolyte distribution in a battery cell. A non-destructive method for analyzing a battery cell includes determining acoustic features at two or more locations of the battery cell, the acoustic features based on one or more of acoustic signals travelling through at least one or more portions of the battery cell during one or more points in time or responses to the acoustic signals obtained during one or more points in time, wherein the one or more points in time correspond to one or more stages of electrolyte distribution in the battery cell. One or more characteristics of the battery cell are determined based on the acoustic features at the two or more locations of the battery cell.