Physics-based model informed smooth particle filter for remaining useful life prediction of lithium-ion battery

Abstract

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is essential for battery management systems (BMS) as rapid capacity declines and failure can impact equipment operation and pose safety hazards. However, battery aging is a complex electrochemical process influenced by various factors such as cycle time, temperature, and loading conditions. To address this, a physics-informed smooth particle filter (SPF) framework for RUL prediction is proposed in this work, which estimates parameters of a single particle model (SPM) of LiBs by extracting three main degradation mechanisms: active material loss in positive and negative electrodes and loss of lithium inventory. Unlike traditional prognostic frameworks, this approach utilizes the SPM to estimate degradation parameters directly from voltage and capacity data, enabling more accurate quantification of degradation mechanisms and prediction of capacity fade trends. The estimated capacity is then used to develop an RUL predictor based on an SPF, which produces more accurate RUL predictions compared to conventional capacity-based methods. The proposed framework achieves a best-case RUL prediction of 2402 cycles at the prediction starting point of 2000 cycles, with a minimum relative error of around 0.089% compared to approximately 0.8% for the traditional framework. Additionally, the proposed framework is demonstrated to be dependable and robust, even when dealing with LiBs data containing Gaussian white noise and dynamic discharging profiles.