DITING: A Weak Degradation Listener for Battery Lifetime Early Prediction

Battery lifetime early prediction is crucial for safety assessment and decision planning. However, early-stage degradation signals are extremely weak and difficult to distinguish from noise. Existing methods primarily rely on denoising or signal decomposition, which risks losing critical degradation cues. In nature, most organisms exhibit binaural effect, exploiting differences between left and right auditory inputs to enhance perceptual reliability. Inspired by this, we propose DITING, a weak degradation listener for battery lifetime early prediction. DITING first employs optimal-transport-based selective matching to extract a robust health template from initial cycles for degradation representation. To manifest degradation signals from noise, we further design a tri-coupled degradation manifestation mechanism. By exploiting the randomness of noise, matched responses under symmetric coupling suppress stochastic fluctuations. Conversely, cumulative deviations driven by degradation propagate through the coupling to form stable bilateral discrepancies. This design effectively amplifies weak cues in the early stage for lifetime prediction. Experiments on multiple datasets demonstrate that DITING achieves state-of-the-art performance and provides more reliable early support for full-lifecycle battery management. The code is available at https://anonymous.4open.science/r/Batt_DITING.