Estimating internal battery health parameters using high-precision cycling

Accurate and high-resolution characterization of internal battery health parameters, including loss of active material (LAM) and loss of lithium inventory (LLI), are critical for advancing lithium-ion battery technology. In this work, we demonstrate how high-precision cycling measurements can improve the estimation of key degradation parameters, both in terms of precision and temporal resolution. To achieve this, we employ a precision measurement setup featuring continuous calibration, enhanced 24-bit data acquisition, and stringent temperature control. Our approach integrates a blend-model-based estimation, leveraging half-cell data and differential voltage analysis (DVA) optimization. The resulting estimates of degradation parameters show extremely low estimation uncertainty and the highest possible cycle-to-cycle temporal resolution yielding detailed insight into aging mechanisms. This provides immediate benefits for cell design optimization and benchmarking. In the long term, this level of detail could set the stage for developing and calibrating advanced aging models, accelerating iterative improvements in battery materials, configurations, and chemistries. • High-precision cycling significantly reduces measurement noise and uncertainty. • Enhanced temporal resolution enables precise, cycle-by-cycle battery aging analysis. • Estimation of Loss of Active Material, Loss of Lithium Inventory. • Blend-model based estimation of the silicon/graphite blend ratio in the anode. • Enables rapid diagnostics and targeted optimization of lithium-ion battery design.