PSA-DMCF-SVAE: A Lithium Battery SOH Prediction Framework with ProbSparse Self-Attention and Deep Multidimensional Features

Accurate state of health (SOH) evaluation is essential for lithium-ion battery safety. Conventional prediction models suffer high computation overhead and insufficient multidimensional feature fusion, leading to poor generalization across working conditions. This work develops the PSA-DMCF-SVAE framework. Ten health factors are extracted from cycling data, and four high-correlation indicators are selected via Pearson screening. The method combines stacked ensemble learning and sparse variational autoencoder (SVAE) for multi-scale aging feature extraction, with an embedded ProbSparse self-attention module to adaptively weight base learners and cut computational complexity. Experiments on NASA battery data reveal over 40% lower prediction error and 42% less feature redundancy than classic stacking architectures. Cross-domain validation on an MIT fast-charging dataset confirms the model’s zero-shot generalization under high-rate, noisy discharge conditions, offering a practical solution for battery management systems.