Battery Health Prediction

This paper introduces PIRC, a lightweight physics-informed reservoir computing framework that embeds degradation priors into a closed-form ridge regression readout for battery state-of-health prediction, validated on the NASA PCoE dataset and chaotic benchmark systems.

Received the MECC 2026 DSCD & AACC Student Travel Award to present our paper on Lightweight Physics-Informed Reservoir Computing (PIRC) for Battery Health Prediction at MECC 2026 in Phoenix, Arizona.

Presented PIRC framework for accurate and energy-efficient State of Health (SOH) prediction of lithium-ion batteries using physics-informed reservoir computing at ACC 2026.