DCML-CSAR: A deep cascaded framework with dual-coupled memory learning and orthogonal feature extraction via recursive parameter transfer for SOH-RUL assessment

Abstract

Accurate state of health (SOH) and remaining useful life (RUL) predictions are essential for battery health assessment, early fault detection, and ensuring system safety. However, existing methods struggle to effectively capture multiscale spatiotemporal characteristics, recognize intricate degradation patterns, and achieve synergy between SOH and RUL tasks due to independent architectures and limited information inheritance. To address these challenges, we propose a novel cascaded SOH-RUL assessment framework that integrates recursive hyperparameter transfer to enable deep coupling between SOH and RUL predictions. The framework employs a Triple-Orthogonal-Plane CNN to map battery data onto three orthogonal hyperplanes, extracting and fusing temporal-spatial features via an attention-based adaptive weighting mechanism. Additionally, a Dual-Coupled Memory-Learning LSTM with a novel gating interaction mechanism enhances temporal feature modeling by coupling forget and input gates and introducing peephole connections. Extensive experiments on multiple datasets, including NASA, Oxford, and CALCE, under diverse degradation scenarios, demonstrate significant improvements in prediction accuracy, robustness, and generalization. This framework offers a promising solution for advancing battery health management and system reliability.