A new method for identifying the parameters of the Li-ion battery PNGV model

The article delves into modeling the Li-ion cell and the prerequisites for identifying system parameters. Initially, we construct a model that encapsulates the nonlinearity of our cell, employing a PNGV model encompassing dynamic parameters like state of charge (SOC), open-circuit voltage OCV, C_b, R₀, R₁ and C₁. Subsequent to this, pertinent battery discharge tests are conducted to discern the model parameters.

Commencing with establishing the relationship between state of charge (SOC) and the variable open-circuit voltage (OCV) using the least squares method, we progressed. Following determining thise relationship, cell discharge tests were employed to ascertain the values of R₀, R₁ and C₁. Once the various parameters of the PNGV Model are identified, validation of this established battery model is performed via experimental discharge tests. The resulting error indicates a mean absolute error (MAE) of 0.0176V for modeling, alongside a root mean square error (RMSE) of 0.0232V. These outcomes underscore the remarkable accuracy of our cell model.