  Voltage-based battery metrics are ubiquitous and essential in battery
manufacturing diagnostics. They enable electrochemical "fingerprinting" of
batteries at the end of the manufacturing line and are naturally scalable,
since voltage data is already collected as part of the formation process which
is the last step in battery manufacturing. Yet, despite their prevalence,
interpretations of voltage-based metrics are often ambiguous and require expert
judgment. In this work, we present a method for collecting and analyzing full
cell near-equilibrium voltage curves for end-of-line manufacturing process
control. The method builds on existing literature on differential voltage
analysis (DVA or dV/dQ) by expanding the method formalism through the lens of
reproducibility, interpretability, and automation. Our model revisions
introduce several new derived metrics relevant to manufacturing process
control, including lithium consumed during formation and the practical
negative-to-positive ratio, which complement standard metrics such as positive
and negative electrode capacities. To facilitate method reproducibility, we
reformulate the model to account for the "inaccessible lithium problem" which
quantifies the numerical differences between modeled versus true values for
electrode capacities and stoichiometries. We finally outline key data
collection considerations, including C-rate and charging direction for both
full cell and half cell datasets, which may impact method reproducibility. This
work highlights the opportunities for leveraging voltage-based electrochemical
metrics for online battery manufacturing process control.
