A model for state-of-health estimation of lithium ion batteries based on charging profiles. (15th June 2019) - Bian, Xiaolei; Liu, Longcheng; Yan, Jinying
Abstract: Using an equivalent circuit model to characterize the constant-current part of a charging/discharging profile, a model is developed to estimate the state-of-health of lithium ion batteries. The model is an incremental capacity analysis-based model, which applies a capacity model to define the dependence of the state of charge on the open circuit voltage as the battery ages. It can be learning-free, with the parameters subject to certain constraints, and is able to give efficient and reliable estimates of the state-of-health for various lithium ion batteries at any aging status. When applied to a fresh LiFePO4 cell, the state-of-health estimated by this model (learning-unrequired or learning-required) shows a close correspondence to the available measured data, with an absolute difference of 0.31% or 0.12% at most, even for significant temperature fluctuation. In addition, NASA battery datasets are employed to demonstrate the versatility and applicability of the model to different chemistries and cell designs. Highlights: An ICA-based model is proposed to estimate SOH of LIBs. The ICA-based model can be learning-required or learning-unrequired. This model can give reliable estimates of SOH for various LIBs at any aging status. The accuracy of the model is validated by experimental results of LFP and NCA batteries.