Estimating the state of health of lithium-ion batteries based on a probability density function

Abstract

Accurate estimation of the state of health (SOH) of lithium-ion batteries is important to ensure safe operation. Although SOH models that extract health features (HFs) from incremental capacity (IC) curves have proven to be effective methods of evaluating battery SOH, the use of smooth IC curves that rely heavily on complicated algorithms reduces the reliability of SOH assessment to a certain extent. In this paper, a probability density function (PDF) method is integrated with Gaussian process regression (GPR) and used to build highly optimised SOH evaluation models for three different types of batteries. The PDF peak position and regional charging time are extracted from charging voltage data in the form of HFs using the PDF method. The proposed SOH estimation model shows good performance when these two HFs are both adopted. Our SOH estimation models for both cells and modules show good robustness for LiCoO2 (LCO), LiNi0.8Co0.15Al0.05O2 (NCA) and lithium iron phosphate (LFP) batteries.