Identifiability Study of Lithium-Ion Battery Capacity Fade Using Degradation Mode Sensitivity for a Minimally and Intuitively Parametrized Electrode-Specific Cell Open-Circuit Voltage Model
Jing Lin, Edwin Khoo

TL;DR
This study investigates the identifiability of capacity fade in lithium-ion batteries using a re-parametrized electrode-specific OCV model, emphasizing physical interpretability and sensitivity analysis to distinguish degradation modes.
Contribution
The paper introduces a re-parametrization of the OCV model based on symmetric ratios, and derives sensitivity gradients to improve understanding of capacity fade mechanisms.
Findings
Sensitivity gradients reveal four regimes of capacity limiting factors.
Electrode differential voltage fractions are key to parameter influence.
Application demonstrates degradation mode identifiability from OCV data.
Abstract
When two electrode open-circuit potentials form a full-cell OCV (open-circuit voltage) model, cell-level SOH (state of health) parameters related to LLI (loss of lithium inventory) and LAM (loss of active materials) naturally appear. Such models have been used to interpret experimental OCV measurements and infer these SOH parameters associated with capacity fade. In this work, we first re-parametrize a popular OCV model formulation by the N/P (negative-to-positive) ratio and Li/P (lithium-to-positive) ratio, which have more symmetric and intuitive physical meaning, and are also pristine-condition-agnostic and cutoff-voltage-independent. We then study the modal identifiability of capacity fade by mathematically deriving the gradients of electrode slippage and cell OCV with respect to these SOH parameters, where the electrode differential voltage fractions, which characterize each…
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Taxonomy
TopicsAdvanced Battery Technologies Research · Advancements in Battery Materials · Advanced Battery Materials and Technologies
