A data‐driven approach with error compensation and uncertainty quantification for remaining useful life prediction of lithium‐ion battery

To accurately predict the remaining useful life (RUL) of lithium-ion battery, this paper presents a novel machine-learning method using gated recurrent unit (GRU) with error compensation (EC). Moreover, the dropout Monte Carlo technology is selected for reliable RUL prediction with uncertainty quantification. First, the equal charging voltage time is established and the variational mode decomposition is introduced to reduce the influence of capacity regeneration phenomenon. Then, the phase space reconstruction with C-C technology is adopted to obtain the optimal input sequence, and the GRU with EC is established for RUL prediction. Finally, the probability distribution and 95% confidence interval are obtained based on dropout Monte Carlo technology. Compared with the existing methods, the proposed method can not only obtain a higher accuracy with a mean absolute error below 1.55% but also achieve reliable RUL prediction with probability distribution.