Multistage Early Warning of Sodium-Ion Battery Thermal Runaway Using Multidimensional Signal Analysis and Redundancy Optimization

This paper proposes an early warning method for thermal runaway in sodium-ion batteries (SIBs) based on multidimensional signal analysis and redundancy optimization. By analyzing signals such as voltage, temperature, strain, and gas concentrations, Principal Component Analysis (PCA) is employed to evaluate the contribution of each signal and reduce data redundancy, while correlation analysis further refines the signal set by eliminating overlapping information. The optimized signals enable a stage-specific warning framework, which identifies distinct phases of thermal runaway progression with high precision. Experimental results validate the effectiveness of the proposed method, showcasing its potential for real-time monitoring and enhanced safety management of sodium-ion battery systems in critical applications.