Remaining Useful Lifetime Prediction of Lithium-Ion Batteries Based on Fragment Data and Trend Identification

Existing methods for predicting lithium-ion battery remaining useful lifetime (RUL) rely on complete capacity degradation data or extensive historical profiles. However, such sufficient conditions are usually unavailable in practical battery usage. To cope with this issue, a framework for RUL estimation with fragment data is proposed. The framework utilizes a small amount of prior knowledge as benchmark data to create an empirical model-based predictive method for estimating RUL by fragment historical data during nonlinear degradation stage. A more specified parameter initialization is obtained by trend identification of the fragment. Particle filter (PF) algorithm is utilized for model parameter update with proposed improved resampling strategy. RUL predictions using two different datasets demonstrate the effectiveness of the proposed method. An error margin of less than ten cycles in RUL predictions is consistently achieved in CS2 dataset when employing fragments ranging from 50 to 60 cycles. And an error margin of around 20 cycles is achieved in CX2 dataset by fragments ranging from 60 to 80 cycles. The proposed method renders a more precise and stable predictive result with high confident level.