Application of Chaotic Time Series Analysis and Relaxation Voltage for Diagnosing Aging of Lithium-Ion Batteries

Lithium-ion battery aging exhibits nonlinear chaotic dynamics influenced by multiple factors. This study introduces a novel diagnostic framework integrating chaos theory with relaxation voltage analysis to characterize battery aging. First, the Lyapunov exponent is computed to validate chaotic behavior in voltage time series. The time delay parameter is then optimized using the correlation integral from the C–C (correlation integral–correlation dimension) method. Finally, the Grassberger–Procaccia (G–P) algorithm estimates the correlation dimension to distinguish healthy and aged batteries. Results reveal distinct chaotic characteristics between the two states, providing both a reliable health state indicator and early degradation warning. This approach enhances predictive accuracy for preventing unexpected failures, improving safety and reliability in drone applications. By merging chaos theory with relaxation voltage analysis, the study offers a new perspective on lithium-ion battery diagnostics.