Accurate prediction of the remaining useful life of lithium-ion batteries is a critical enabler for the safety

reliability

and energy efficiency of modern electric vehicles. However

the nonlinear

multiscale

and condition-dependent nature of battery degradation presents formidable challenges for conventional prognostic models. This work proposes a high-performance hybrid prognostic architecture that synergistically integrates (i) multiresolution feature extraction via the discrete wavelet transform

(ii) long-range temporal dependency modeling through an encoder–decoder transformer network with multihead self-attention

and (iii) nonlinear residual correction using XGBoost. To ensure globally optimal hyperparameter configuration and robust convergence

the full pipeline is optimized using the chaotic billiards optimizer

complemented by local refinement with the Adam optimizer. Experimental evaluations conducted on benchmark battery aging datasets from the National Aeronautics and Space Administration and the Center for Advanced Life Cycle Engineering demonstrate that the proposed framework substantially outperforms state-of-the-art deep learning and ensemble baselines

including recurrent neural networks

convolutional–recurrent hybrids

transformer-based models

and gradient-boosted decision trees. The proposed approach achieves performance improvements exceeding 15%; in both mean absolute error and root mean square error

with an average prediction accuracy characterized by a mean absolute error below 0.020

a root mean square error below 0.032

and a coefficient of determination exceeding 0.98. Ablation analyses further confirm the complementary contributions of multiscale signal decomposition

attention-based temporal modeling

residual learning

and chaotic meta-heuristic optimization. Despite its hybrid structure

the framework remains computationally efficient

converging within a limited number of training epochs and enabling real-time inference (∼0.038 s per prediction window) with a lightweight model size of 2.14 million parameters

highlighting its suitability for embedded battery management systems. Overall

the proposed framework establishes a robust and interpretable foundation for next-generation battery prognostics

enabling intelligent predictive maintenance

enhanced safety

and energy-aware management in electric mobility systems.