Predicting the impact of formation protocols on battery lifetime immediately after manufacturing
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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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Mar 2022.
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Citing papers per year: 2023: 2; 2024: 1; 2025: 4; 2026: 2.
9 papers.
- 2023Predicting Battery Lifetime Under Varying Usage Conditions from Early Aging Datacited by 7
- 2025BatteryLife: A Comprehensive Dataset and Benchmark for Battery Life Predictioncited by 4
- 2024Systematic Feature Design for Cycle Life Prediction of Lithium-Ion Batteries During Formationcited by 3
- 2025Diagnostic-free onboard battery health assessmentcited by 1
- 2023Health diagnosis and recuperation of aged Li-ion batteries with data analytics and equivalent circuit modelingcited by 0
- 2025Discovery Learning accelerates battery design evaluationcited by 0
- 2025Pretrained battery transformer (PBT): A foundation model for battery life predictioncited by 0
- 2026BatteryMFormer: Multi-level Learning for Battery Degradation Trajectory Forecastingcited by 0
- 2026CORD: Learning Reusable Degradation Representations Across Heterogeneous Physical Systemscited by 0
- 2026CORD: Learning Reusable Degradation Representations Across Heterogeneous Physical Systems30 Sep 2026
- 2026BatteryMFormer: Multi-level Learning for Battery Degradation Trajectory Forecasting26 May 2026
- 2025Pretrained battery transformer (PBT): A foundation model for battery life prediction18 Dec 2025
- 2025Discovery Learning accelerates battery design evaluation9 Aug 2025
- 2025Diagnostic-free onboard battery health assessment10 Mar 2025
- 2025BatteryLife: A Comprehensive Dataset and Benchmark for Battery Life Prediction26 Feb 2025
- 2024Systematic Feature Design for Cycle Life Prediction of Lithium-Ion Batteries During Formation9 Oct 2024
- 2023Health diagnosis and recuperation of aged Li-ion batteries with data analytics and equivalent circuit modeling21 Sep 2023
- 2023Predicting Battery Lifetime Under Varying Usage Conditions from Early Aging Data17 Jul 2023