State-of-Health Estimation of Lithium-ion Batteries by Fusing an Open-Circuit-Voltage Model and Incremental Capacity Analysis

The state of health (SOH) is a vital parameter enabling the reliability and life diagnostic of lithium-ion batteries (LIBs). A novel fusion-based SOH estimator is proposed in this study, which combines an open-circuit-voltage (OCV) model and the incremental capacity analysis (ICA). Specifically, a novel OCV model is developed to extract the OCV curve and the associated features-of-interest (FOI), from the measured terminal voltage during constant-current (CC) charge. With the determined OCV model, the disturbance-free incremental capacity (IC) curves can be derived, which enables the extraction of a set of IC morphological FOIs. The extracted model FOI and IC morphological FOIs are further fused for SOH estimation through an artificial neural network. Long-term degradation data obtained from different battery chemistries are used for validation. Results suggest that the proposed fusion-based method manifests itself with high estimation accuracy and high robustness.