Enhanced remaining useful life prediction of lithium-ion battery based on a dual attention hybrid data-driven method

Electric vehicles (EVs) and wheelchairs rely heavily on lithium-ion batteries (LIBs) for safe, reliable, and uninterrupted mobility. Accurate Remaining Useful Life (RUL) prediction of EV and wheelchair batteries is paramount for averting unexpected failures, extending service longevity, and devising proactive maintenance protocols. Although bidirectional gated recurrent unit (BiGRU) networks are proficient at modeling temporal dependencies, their exclusive dependence on sequential input constrains prediction precision. To circumvent this issue, this study introduces an encoder-decoder architecture that synergistically integrates dual attention mechanisms with BiGRU. Within the encoder, an attention-augmented BiGRU module selectively emphasizes critical aging features. An additional attention mechanism dynamically refines the input sequence to ensure no essential information is attenuated during encoding. The decoder, structured as a BiGRU network, subsequently delineates precise capacity degradation trajectories. Comprehensive evaluations conducted on the NASA and CALCE datasets substantiate the model efficacy. On the NASA dataset, the proposed framework yields absolute errors of 1, 4, 0, and 2 for cells B0005, B0006, B0007, and B0018, respectively. Comparably minimal error margins are observed on the CALCE dataset. Further comparative analyses reveal that the proposed approach surpasses existing benchmarks in predictive accuracy, robustness, and generalizability. This high-fidelity RUL prediction model holds substantial potential for advancing battery management systems in EVs and wheelchairs, reinforcing operational reliability, and elevating safety and end-user experience.