@article {pub6482,
	title = {Benchmarking half-cell model fitting approaches for lithium-ion battery degradation diagnostics},
	author = {Tingkai Li AND Yifan Zhang AND Benjamin Nowacki AND Sina Navidi AND Thomas Schmitt AND Shan Hu AND Chao Hu},
	year = {2026},
	month = {April},
	abstract = {Accurate degradation diagnostics for lithium-ion batteries{\textemdash}specifically quantifying loss of active material on the positive and negative electrodes and loss of lithium inventory{\textemdash}enables component-level insights to improve design, control, and maintenance strategies. Among the few non-intrusive techniques, empirical half-cell model fitting to full-cell cycling data offers a promising path but often suffers from inconsistent fitting performance. To address this limitation, this work introduces a multi-objective optimization framework that enhances half-cell model fitting performance by jointly optimizing four degradation-aware objectives, which also serve as the goodness-of-fit metrics. Benchmarking studies on two datasets with distinct cell chemistries (LFP/Gr and LCO/Gr) and degradation pathways (fabricated capacity imbalance vs. cycling-induced aging) demonstrate improved alignment of phase changes and electrochemical-related signatures on differential voltage (dV/dQ) curves
compared to conventional single-objective fitting. Built upon the successful benchmarking results, we present best practices and practical guidelines for implementing the proposed framework, including recommendations on half-cell fabrication and model formulation for broader application in degradation diagnostics.},
	publisher = {Elsevier},
	journal = {eTransportation},
	volume = {29}
}