# Differential voltage analysis for battery manufacturing process control

arXiv: https://arxiv.org/abs/2303.07088 · HTML: https://mlsanity.com/paper/2303.07088/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2023.

## Built on by (most cited first, 3 of 3)

- [Systematic Feature Design for Cycle Life Prediction of Lithium-Ion Batteries During Formation](https://mlsanity.com/paper/2410.07458/) (2024)
- [Conformalized Transfer Learning for Li-ion Battery State of Health Forecasting under Manufacturing and Usage Variability](https://mlsanity.com/paper/2603.24475/) (2026)
- [Bridging battery design and health assessment through virtual sensing and physics-informed learning](https://mlsanity.com/paper/2607.16864/) (2026)

## Abstract (arXiv)

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.