Uncertainty-Aware Early Battery Life Prediction with Composite-Kernel Gaussian Process Regression and Conformalized Adaptive Intervals

Accurate remaining useful life (RUL) prediction of lithium-ion batteries is critical for the safe and reliable operation of battery management systems. While point prediction methods have been extensively studied, principled uncertainty quantification (UQ) remains underexplored, particularly in the early-cycle regime where degradation signals are subtle and training data are scarce. This paper presents a systematic evaluation of ten UQ configurations for early-cycle battery RUL prediction using the MIT-Stanford dataset of 124 cells. A composite-kernel Gaussian process regression model combining a radial basis function and a white noise kernel, denoted GPR-CK(RBF + W), is used as the core predictor. We compare Bayesian native UQ, split conformal prediction, jackknife+, bootstrap resampling, and a Conformalized Adaptive Intervals (CAI) method across a Primary test set and a distribution-shifted Secondary test set collected one year later. On the Primary test set, the composite-kernel GPR variants achieve full (100%) prediction-interval coverage at the 95% nominal level, while the proposed CAI calibration yields the sharpest coverage-valid intervals. Under a one-year distribution shift, GPR-CK(RBF + W) empirically retains 97.5% coverage, which we report as empirical robustness rather than a guaranteed coverage level. A leave-one-out calibration factor (δ = 1.39 with the white noise kernel versus δ = 3.43 without it) isolates explicit noise modeling as the decisive factor for calibration. Feature dimensionality analysis further reveals a three-phase sensitivity pattern, identifying three features as the optimal operating point balancing predictive accuracy and UQ quality.