DITING: A Weak Degradation Listener for Battery Lifetime Early Prediction

Battery lifetime early prediction is crucial for safety assessment and decision planning, yet early-stage degradation signals are extremely weak and difficult to distinguish from stochastic noise. Existing methods primarily rely on denoising or signal decomposition, which may lose critical degradation cues. In nature, most organisms exhibit the binaural effect, exploiting discrepancies 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. We first employ optimal-transport-based selective matching to extract a robust health template from initial cycles, and further design a tri-coupled degradation manifestation mechanism to distinguish degradation signals from noise. By exploiting the randomness of noise, matched responses under symmetric coupling suppress stochastic fluctuations, while degradation-driven cumulative deviations propagate through the coupling process to form stable bilateral discrepancies, thereby amplifying weak early-stage cues for lifetime prediction. Experiments on various datasets demonstrate that DITING achieves state-of-the-art performance and provides more reliable early support for full-lifecycle battery management.