ArticleOpen Access http://dx.doi.org/10.26855/acc.2026.09.013
Data-driven Quality Control Technologies for the Direct Recycling Process of Lithium Iron Phosphate Batteries
Boyu Cao
Pratt School of Engineering, Duke University, Durham, NC 27708, USA.
*Corresponding author: Boyu Cao
Published: August 31, 2026
Abstract
Spent lithium iron phosphate cathodes rarely enter a direct recycling line with uniform degradation. Lithium loss, FePO4 accumulation, particle damage, residual carbon, and fluorine-containing impurities vary from batch to batch, so fixed relithiation recipes may produce inconsistent outcomes when feedstock conditions and process histories differ. A quality data chain connects feedstock diagnosis, electrochemical relithiation, slurry homogenization, atmosphere-controlled thermal repair, and product release. The Li/Fe molar ratio, phase composition, residual carbon, fluorine content, and particle-size distribution are treated as critical quality attributes, while current, cumulative charge, reaction time, temperature history, and oxygen concentration describe the evolving process state. Elemental balance and phase constraints are coupled with machine-learning models for soft sensing, anomaly detection, and parameter correction. Retrospective validation uses published LFP regeneration datasets to compare experimentally selected settings with ANN-guided conditions. The ANN refined the experimentally identified regeneration temperature from 700 to 675 °C, achieving an R2 of 0.999, an MAE of 0.158, and an RMSE of 0.209. In a separate LFP case, ANN-guided settings increased specific discharge capacity by 2.5%, improved overall capacity by 6.2%, and extended the point at which capacity declined to 80% from 1091 to 1147 cycles. These results indicate that data-guided parameter selection can narrow operating windows and support traceable quality decisions under variable feedstock conditions.
Keyword
Lithium iron phosphate; direct recycling; data-driven quality control; electrochemical relithiation; process monitoring
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doi:10.1002/ente.202502551
Copyright
© 2026 by the author(s).
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) license, which permits non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited and is not modified or adapted.
https://creativecommons.org/licenses/by-nc-nd/4.0/
How to cite this paper
Data-driven Quality Control Technologies for the Direct Recycling Process of Lithium Iron Phosphate Batteries
How to cite this paper: Boyu Cao. (2026) Data-driven Quality Control Technologies for the Direct Recycling Process of Lithium Iron Phosphate Batteries. Advances in Computer and Communication, 7(3), 176-180.
DOI: http://dx.doi.org/10.26855/acc.2026.09.013