Performance Analysis of Empirical Open-Circuit Voltage Modeling in Lithium-Ion Batteries—Part 3: Experimental Results

This article is the third part of a series of articles about empirical approaches to open-circuit voltage (OCV) modeling of lithium-ion batteries. The first part of the series proposed models to quantify various sources of uncertainties in the OCV models; the second part of the series presented systematic data collection approaches to compute the uncertainties in the OCV to state of charge (SOC) models. This article uses data collected from 28 OCV characterization experiments, performed according to the data collection plan presented in the second part, to compute and analyze three OCV uncertainty metrics: cell-to-cell variations, current-rate (C-Rate) error, and curve-fitting error. The computed metrics showed that a lower C-Rate resulted in smaller errors in the OCV-SOC model and vice versa. The results reported in this article establish a relationship between the C-Rate and the uncertainty of the OCV-SOC model. Furthermore, it was observed that the magnitude of cell-to-cell variations varied with the battery SOC, and it was not significantly affected by the C-Rate at which the experimental data were collected. The analysis in this article also found that widely used polynomial modeling approaches for OCV-SOC curve modeling incur significant errors. The approaches and results presented in this article can be useful to battery researchers for quantifying the tradeoff between the time taken to complete the OCV characterization test and the corresponding uncertainty in the OCV-SOC modeling. Furthermore, quantified uncertainty model parameters can accurately characterize the propagation of uncertainty in various battery management system (BMS) functionalities, such as SOC and state of health estimation. Finally, the insights presented in this article can be useful to collect more accurate data for training machine learning models in SOC estimation.