Lithium Battery Acoustic Warning for Early Thermal Runaway
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Solution Overview
Problem
Existing methods for detecting thermal runaway in lithium-ion batteries, such as using temperature and voltage signals, suffer from significant lag and often fail to provide early warnings, while sound-based methods are ineffective due to the faint nature of early-stage sounds in pouch batteries.
Innovation Solution
A dual-level thermal runaway warning system utilizing a sparrow search algorithm-optimized eXtreme Gradient Boosting (SSA-XGBoost) algorithm for identifying abnormal sound signals, combining outlier detection with multi-domain feature extraction to provide early warnings through level-1 and level-2 alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If temperature and voltage signals are used for thermal runaway detection, then the detection method is simple and reliable, but the detection has obvious lag and cannot detect early-stage thermal runaway
Solution Approach 1:
The patent replaces traditional temperature and voltage detection methods with acoustic signal detection. By using microphones to capture sound waves generated during thermal runaway, the system can detect early-stage anomalies before temperature and voltage changes become apparent, thereby reducing detection time lag while maintaining reliability through advanced signal processing algorithms.
Solution Approach 2:
The patent introduces a new detection dimension by utilizing acoustic signals instead of relying solely on temperature and voltage parameters. This dimensional shift allows the system to detect thermal runaway events at an earlier stage, as acoustic anomalies occur before significant thermal or electrical changes, thus resolving the time lag issue.
2Loss of time
If sound signal detection is used for early-stage thermal runaway, then early detection capability is improved, but the sound signal is extremely faint and difficult to perceive
Solution Approach 1:
The patent combines multiple signal processing techniques including wavelet transform, autocorrelation analysis, and energy detection to enhance the detection of faint acoustic signals. By merging these methods, the system can extract meaningful information from weak early-stage sounds while filtering out background noise, thus improving measurement precision without sacrificing early detection capability.
Solution Approach 2:
The patent introduces signal processing algorithms as intermediaries between the faint acoustic signals and the detection system. These algorithms amplify and clarify the weak early-stage sounds, making them detectable and analyzable, thereby resolving the precision issue while maintaining early detection advantages.
3Device complexity
If a single-level thermal runaway warning system is used, then the system complexity is low, but the identification accuracy of weak abnormal sound signals in early stage is insufficient
Solution Approach 1:
The patent divides the thermal runaway detection process into multiple stages with different detection strategies. Level-1 warning uses simple acoustic anomaly detection for early screening, while Level-2 warning employs more sophisticated analysis for confirmed threats. This segmentation allows the system to maintain low complexity for normal operation while achieving high accuracy when needed.
Solution Approach 2:
The patent implements a dynamic warning system that adapts its complexity based on the detected threat level. The system starts with simple monitoring and progressively engages more complex analysis only when anomalies are detected, thereby maintaining low average complexity while ensuring high identification accuracy for weak abnormal signals.
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
Enhances the accuracy and timeliness of thermal runaway detection by identifying faint early-stage sounds, allowing for rapid safety measures like power cutoff and battery replacement.
Implementation Method 1
obtaining a battery sound signal sequence of a to-be-detected lithium battery; performing outlier identification on the battery sound signal sequence
Data Source
AI summary
The present invention provides a dual-level thermal runaway warning method of a lithium battery based on a sound signal, comprising: obtaining a battery sound signal sequence; performing outlier identification on the battery sound signal sequence, and providing a level-1 thermal runaway warning when an abnormal data point exists; extracting a time-frequency domain feature of the abnormal data point, and identifying a presence of a thermal runaway expansion sound through a sparrow search algorithm-optimized eXtreme Gradient Boosting (SSA-XGBoost) algorithm, to providing a level-2 thermal runaway warning. In the SSA-XGBoost algorithm, optimal parameter adjustment is performed on a number of iterations, a learning rate, and a decision tree depth of the XGBoost algorithm through the SSA. A dual-level thermal runaway warning strategy is adopted to perform grading identification on general anomalies or deep anomalies, thereby effectively improving identification accuracy of a weak abnormal sound signal in an early stage.