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

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IEEE Transactions on Transportation Electrification


Batteries, Battery charge measurement, battery management systems, Computational modeling, Data models, Discharges (electric), Li-ion batteries, OCV characterization, OCV modeling, OCV-SOC characterization, OCV-SOC modeling, State of charge, state of charge estimation, Uncertainty


This paper is the third part of a series of papers 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 paper 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, 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 paper establish a relationship between the C-Rate and the uncertainty of the OCV-SOC model. Further, it was observed that the magnitude of cell-to-cell variations varied with the battery SOC and that it was not significantly affected by the C-Rate at which the experimental data was collected. The analysis in this paper also found that widely used polynomial modeling approaches for OCV-SOC curve modeling, incur significant errors. The approaches and results presented in this paper 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. Further, quantified uncertainty model parameters can accurately characterize the uncertainty in various BMS functionalities, such as SOC and state of health estimation. The insights presented in this paper can be useful to collect more accurate data for training machine learning models in SOC estimation.