A model for state-of-health estimation of lithium ion batteries based on charging profiles. (15th June 2019)

• Record Type: Journal Article
• Authors: Bian, Xiaolei; Liu, Longcheng; Yan, Jinying
• Is Part Of: Energy. Volume 177(2019)
• Page Start: 57
• Page End: 65
• Publication Date: 2019-06-15
• Subjects: Lithium ion battery -- State of health -- Equivalent circuit model -- Incremental capacity analysis; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.04.070
• Languages: English
• ISSNs: 0360-5442

Abstract

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.