Intelligent prediction of the remaining useful life of lithium-ion batteries based on a CGHF-MDH-Mamba model

Abstract

Lithium-ion batteries are core components of renewable generation and energy-storage systems and are widely deployed in PV/wind grid scheduling and e-mobility. Accurate remaining useful life (RUL) prediction is essential for operational stability and cost control. This paper proposes a battery life prediction approach that integrates channel-grouping half-convolution (CGHF) and a monotonic decreasing head (MDH) within a Mamba-based sequence modeling framework. CGHF reduces computational redundancy while strengthening multi-scale temporal representations; the selective state-space module of Mamba efficiently captures long-range dependencies; MDH imposes an explicit “non-increasing capacity” constraint at the decoder to enhance robustness and interpretability. Experiments on the National Aeronautics and Space Administration (NASA) Randomized Battery Usage Dataset and the Tongji University (TJU) Commercial Lithium-Ion Battery Cycling Dataset demonstrate superior RUL accuracy, achieving minimum capacity-prediction mean absolute errors (MAEs) of 0.0081 and 0.0009 Ah, respectively, outperforming strong baselines under the same settings. The method improves accuracy while maintaining fast inference, suggesting potential applicability to online health monitoring and maintenance planning, subject to further validation under more diverse operating conditions.

Keywords

Lithium-ion batteries / remaining useful life / Mamba / channel-grouping half-convolution / deep learning

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