An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty

Accurate prediction of the remaining useful life (RUL) of lithium-ion battery is of great significance for the reliability of electronic equipment. In the conventional approaches, there are notable challenges in the RUL prediction for a single battery lacking historical data. To predict the battery's RUL under the condition of unlabeled small sample data and to describe the uncertainty of the parameter estimation in the degradation model, a novel adaptive approach based on Kalman filter and expectation maximum with Rauch-Tung-Striebel (KF-EM-RTS) is proposed to predict the battery's RUL. Specifically, without RUL labels and offline training, an online KF adaptive-update model based on the Wiener process is proposed for a single battery, in which the uncertainty of parameter estimation is described. Furthermore, the unknown model parameters can be adaptively estimated using EM-RTS to overcome the constraints of strong Markov characteristics, the convergence of which is proved. The real-world battery dataset provided by NASA Ames research center is applied to verify the proposed RUL prediction approach. Experimental results show that the proposed approach outperforms the existing conventional data-driven approaches for predicting the battery's RUL.

Automated summary

This study proposes an adaptive approach based on Kalman filter and expectation maximum to accurately predict the remaining useful life (RUL) of a single lithium-ion battery without historical data and describe the uncertainty of parameter estimation. Experimental results demonstrate that this method outperforms existing conventional data-driven approaches.