Battery State-of-Health Estimation Based on Incremental Capacity Analysis Method: Synthesizing From Cell-Level Test to Real-World Application

The incremental capacity analysis (ICA) method is widely used in battery state-of-health (SOH) estimation due to its high prediction accuracy and aging mechanism implications. However, realizing precise SOH metering for real-world electric vehicles (EVs) is still challenging, if not impossible, and comprehensive and large-scale laboratory tests necessitated are usually time-consuming and labor-intensive. This article proposes an enabling SOH estimation scheme based on the ICA method for real-world EVs. This is realized by combining an equivalent IC-value calculation for battery packs with cell-level battery tests while taking cell inconsistency into consideration. The effectiveness of the proposed method is verified using the datasets collected from both well-controlled laboratory tests and daily operating EVs. The results show that battery cells within a batter pack generally experience similar degradation routes, which means insignificant cell inconsistency development with aging, and the proposed method can realize an accurate pack-level SOH estimation both for laboratory battery packs and real-world EVs. By applying the proposed method, the root mean square errors (RMSEs) of battery SOH prediction for laboratory modules, packs, and an electric taxi are 0.00955, 0.02457, and 0.0204, respectively. This study presents a verified framework of applying the ICA-based method to realize pack-level battery SOH estimation based on cell-level tests.

Automated summary

This article proposes an enabling SOH estimation scheme based on the ICA method for real-world EVs. The effectiveness of the proposed method is verified using the datasets collected from both well-controlled laboratory tests and daily operating EVs. The results show that the proposed method can realize an accurate pack-level SOH estimation both for laboratory battery packs and real-world EVs.