Modelling Li-ion batteries with distinct chemistries using advanced system identification based data driven techniques
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Date
2022-08
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G.B. Pant University of Agriculture and Technology, Pantnagar, District Udham Singh Nagar, Uttarakhand. PIN - 263145
Abstract
Recently Li-ion batteries have emerged as a potential candidate for reliable energy storage. This had been on the account of their high energy density and power as compared to their counterparts in addition to long life and improved charge-discharge cycles. Li-ion batteries are available in chemistries such as NCA, NMC, LFP, LCO, etc. Li-ion batteries being critical to EVs and other applications involving energy storage requirements need to be modelled accurately and precisely. This is essential for proper energy management by calculating the SOC, SOH, and RUL of the battery. Accurate models are necessary to be incorporated within the BMS handling the optimal regulation of the energy output from battery including the efficient charging. In this thesis work three different chemistries of Li-ion battery have been chosen for modelling using advanced data driven method called system identification. The selected chemistries of Li-ion batteries are NCA, NMC, and LFP. For each chemistry a linear Transfer function model and a nonlinear model Hamm- Wiener model has been realized using system identification approach also sometimes
called experimental based modeling technique. A set of linear and nonlinear models are created using system identification technique. The mathematical metrics of the fit between the reference and simulated signals is used to choose the optimal and best model. In comparison to the linear model the nonlinear model was obtained to be more accurate for all the batteries. The accuracies achieved by the nonlinear model for NCA, NMC, and LFP are 94.15%, 92.23%, and 90.78% respectively. Hence, it can be concluded that the Hammwiener model being the best can be used effectively for representing a Li-ion battery in different applications using BMS.