Online Diffusion Monitoring for Optimized Charge Control in Lithium-ion Batteries

Lithium-ion batteries are complex nonlinear electrochemical systems that deliver power to an ever growing list of devices, from smartphones to vehicles. The required performance and useful lifetime heavily depend on internal ion diffusion mechanisms. Expensive and time-consuming experiments are typically used to estimate diffusion coefficients. After presenting the traditional GITT and EIS methods for estimating diffusion coefficients, this work proposes an alternative way based on signal processing. Specifically, we apply the Continuous Wavelet Transform (CWT) to pulsed current and voltage discharge to extract the diffusion-related resistance, which is inversely proportional to the diffusion coefficient. The method is applied to a publicly available dataset of commercial lithium-ion cells aged under different cycling conditions. The results confirm that the extracted values capture the trends in lithium-ion transport. This alternative approach enables a practical pathway toward real-time diffusion monitoring, with potential application to adaptive charging strategies and to onboard battery management systems.