Abstract

The prognostics and health management of lithium-ion batteries is extremely important for the working performance and cost of energy storage systems. Accurately forecasting battery state of health (SOH) and remaining useful life (RUL) plays an important role in ensuring reliable system operation and minimizing maintenance costs. This paper is, thus, concerned with online short-term SOH estimation and long-term RUL prediction using Brownian motion (BM) based degradation model and particle filtering (PF). The proposed model tackles the capacity degradation as the traveling distance of a Brownian particle in a given time interval. Then, the PF is used to estimate the drift parameter of the BM. This framework leads an accurate short-term SOH estimation result and gives a clear explanation for long-term RUL prediction because the first hitting time of BM follows the inverse Gaussian distribution. The predictive capability and effectiveness of the method are demonstrated by degradation datasets from different types of lithium-ion batteries. Through comparisons with other methods, the experimental results show the superiority of the proposed method in battery health prognosis and it can provide accurate and robust SOH and RUL forecasting.