An Optimized Design Methodology for Fast Electrochemical Impedance Spectroscopy-Based Battery State of Health Estimation

This paper presents a novel methodology for designing Electrochemical Impedance Spectroscopy-based State of Health estimation systems, which aims to optimize both the accuracy and the measurement time. The proposed approach integrates feature selection to identify the most informative impedance features, reducing the number of frequencies measured without compromising the SoH estimation accuracy. A Genetic Algorithm (GA) is employed to select the optimal subset of features, balancing the trade-off between improved prediction performance and reduced measurement time. By adjusting frequency selection, the approach can optimize the system to meet application-specific constraints, such as desired accuracy levels and measurement time constraints. The proposed methodology was validated using a publicly available dataset of lithium-ion batteries. The results demonstrated the effectiveness of the proposed approach, achieving up to a 90% reduction in measurement times with only a minimal reduction in estimation accuracy.