# Predicting the impact of formation protocols on battery lifetime immediately after manufacturing

arXiv: https://arxiv.org/abs/2203.14158 · HTML: https://mlsanity.com/paper/2203.14158/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2022.

## Built on by (most cited first, 9 of 9)

- [Predicting Battery Lifetime Under Varying Usage Conditions from Early Aging Data](https://mlsanity.com/paper/2307.08382/) (2023)
- [BatteryLife: A Comprehensive Dataset and Benchmark for Battery Life Prediction](https://mlsanity.com/paper/2502.18807/) (2025)
- [Systematic Feature Design for Cycle Life Prediction of Lithium-Ion Batteries During Formation](https://mlsanity.com/paper/2410.07458/) (2024)
- [Diagnostic-free onboard battery health assessment](https://mlsanity.com/paper/2503.07383/) (2025)
- [Health diagnosis and recuperation of aged Li-ion batteries with data analytics and equivalent circuit modeling](https://mlsanity.com/paper/2310.03750/) (2023)
- [Discovery Learning accelerates battery design evaluation](https://mlsanity.com/paper/2508.06985/) (2025)
- [Pretrained battery transformer (PBT): A foundation model for battery life prediction](https://mlsanity.com/paper/2512.16334/) (2025)
- [BatteryMFormer: Multi-level Learning for Battery Degradation Trajectory Forecasting](https://mlsanity.com/paper/2605.27044/) (2026)
- [CORD: Learning Reusable Degradation Representations Across Heterogeneous Physical Systems](https://mlsanity.com/paper/2609.39784/) (2026)

## Abstract (arXiv)

Increasing the speed of battery formation can significantly lower lithium-ion battery manufacturing costs. However, adopting faster formation protocols in practical manufacturing settings is challenging due to a lack of inexpensive, rapid diagnostic signals that can inform possible impacts to long-term battery lifetime. This work identifies the cell resistance measured at low states of charge as an early-life diagnostic feature for screening new formation protocols. We show that this signal correlates to cycle life and improves the accuracy of data-driven battery lifetime prediction models. The signal is obtainable at the end of the manufacturing line, takes seconds to acquire, and does not require specialized test equipment. We explore a physical connection between this resistance signal and the quantity of lithium consumed during formation, suggesting that the signal may be broadly applicable for evaluating any manufacturing process change that could impact the total lithium consumed during formation.