Conventional state of health (SOH) estimation often requires capacity measurement from battery's full charge or discharge profile between fully charged state and cut-off state. Incremental capacity analysis can improve estimation efficiency by extracting features to estimate SOH or recalibrate state of charge estimation without using full profile. While direct numerical derivatives often do not show smooth result due to measurement noise, this paper utilizes robust cubic smoothing spline method on producing incremental capacity curve, which is superior over typical filters that require tuning on window size usually by trial&error because smoothing parameters in the proposed method can be determined by cross validation. Comparison through simulated data shows that the proposed method maintains good fidelity on data and feature of interest with low RMSE values under derivative form. This paper also proposes a peak height ratio feature for SOH estimation. While a linear relationship is noted between SOH and peak height ratio, estimation of SOH from peak height ratio is demonstrated using linear regression. A more generalized version of SOH estimation method is also demonstrated using multiple linear regression with covariates of both peak height ratio and the height of peak associated with "last phase-transition of Li ions intercalation during charging."
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Renewable Energy, Sustainability and the Environment
- Surfaces, Coatings and Films
- Materials Chemistry