Differential voltage analysis for battery manufacturing process control - Weng, Andrew; Siegel, Jason B.; Stefanopoulou, Anna (Frontiers in Energy Research, 2023)
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. Difficulties stem from inconsistent guidelines for data collection and a lack of standardized methods for collecting and analyzing voltage-based data. In this work, we discuss a method for collecting and analyzing full cell near-equilibrium voltage curves for end-of-line manufacturing process control. The method builds off of existing literature on differential voltage, or dV/dQ, analysis, but expands the method formalism through the lens of reproducibility, interpretability, and automation. Our model revisions introduce several new derived metrics relevant to manufacturing, including lithium consumed during formation and the practical negative-to-positive ratio, which complements already-existing \rev{cell} metrics such as positive and negative electrode capacities. To facilitate more reproducible research, we discuss the "inaccessible lithium problem" and its implications on how the differential voltage analysis outputs are interpreted. We summarize experimental and analysis considerations for reproducible data reporting. We finally discuss how these voltage-based electrochemical metrics may be implemented in a factory context for online process control.