LFP Battery Thermal Runaway Warning Using Ultrasonic-Stress Fusion
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Solution Overview
Problem
Existing detection technologies for lithium iron phosphate batteries struggle to accurately and timely detect thermal runaway due to interference from multiple factors, leading to false or missed warnings, as they rely on single parameter monitoring that fails to capture the interaction and causality of various factors contributing to thermal runaway.
Innovation Solution
An early warning apparatus and method combining ultrasonic crack detection and stress sensor monitoring, utilizing an ultrasonic monitoring module and stress monitoring module, with a Kalman filtering model to integrate ultrasonic and stress parameters for comprehensive evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single parameter monitoring (ultrasonic or stress) is used, then device complexity is reduced, but measurement precision and reliability of thermal runaway detection deteriorate due to interference from multiple factors
Solution Approach 1:
The patent combines ultrasonic detection technology and stress sensor monitoring into an integrated multi-parameter coupling analysis system. The ultrasonic module detects internal cracks and structural changes, while the stress monitoring module captures external force bearing conditions and structural strain. By merging these complementary detection methods, the system achieves comprehensive monitoring of battery internal and external states, significantly improving measurement precision and reliability of thermal runaway detection while resolving the limitation of single parameter monitoring.
2Measurement precision
If multi-parameter coupling analysis is implemented, then measurement precision and timeliness of early warning are improved, but device complexity increases due to integration of multiple monitoring modules
Solution Approach 1:
The patent divides the monitoring system into functionally independent modules: an ultrasonic detection module for internal structure monitoring, a stress monitoring module for external force and structural strain monitoring, and a data processing module for coupling analysis. Each module performs a specific function, allowing for independent optimization, maintenance, and calibration. This segmentation reduces the complexity of the overall system while enabling comprehensive multi-parameter monitoring and improving early warning accuracy through coordinated operation of specialized components.
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 warnings by providing a comprehensive evaluation parameter, enabling automatic early warning and risk assessment through integrated parameter analysis.
Implementation Method 1
By detecting internal cracks and defects of the battery by using the ultrasonic technology, real-time information of a health condition of an internal structure of the battery can be provided.
Implementation Method 2
By monitoring stress variation of an outer surface of the battery by using the strain sensor technology, an external force bearing condition and structural strain of the battery can be reflected.
Implementation Method 3
a power amplifier electrically connected with the ultrasonic signal generator and the ultrasonic transmitting end; and the power amplifier being capable of amplifying the ultrasonic waves and transmitting the ultrasonic waves through the ultrasonic transmitting end.
Data Source
AI summary
The present invention relates to an early warning apparatus for thermal runaway of a lithium iron phosphate battery, and belongs to the technical field of batteries. The early warning apparatus includes an ultrasonic monitoring module, a stress monitoring module, and an upper computer. The early warning method includes: S1: collecting ultrasonic parameters and stress parameters of the lithium iron phosphate battery; S2: inputting historical data of the collected ultrasonic parameters and stress parameters to a Kalman filtering model to obtain optimal estimation at the time k; S3: computing a comprehensive evaluation parameter J; and S4: judging a warning level according to the comprehensive evaluation parameter. By using the early warning apparatus and method, the ultrasonic parameters can be coupled with the stress parameters so that automatic early warning for the thermal runaway of the lithium iron phosphate battery is achieved.