Localized feature selection augmented dual-stream fusion network for state of health estimation of lithium-ion batteries

Abstract

Lithium-ion batteries are essential for renewable energy storage, necessitating efficient battery management systems (BMS) for optimal performance and longevity. Accurate estimation of the state of health (SOH) is crucial for BMS safety, yet current machine learning-based SOH estimation relying on global aging features often overlooks localized degradation patterns. In this study, we introduce a novel SOH estimation pipeline that integrates voltage-range-specific segmentation with a multi-stage, cross-validation-driven localized feature-selection framework and a feature-augmented dual-stream fusion network. Our methodology partitions full-range voltage into localized intervals to construct a degradation-sensitive feature library, from which 4 optimal features are identified from a set of 336 candidates. These selected features are combined with raw voltage signals via a dual-stream architecture that employs a dynamic gating mechanism to recalibrate feature contributions during training. Cross-validation-based evaluation on datasets encompassing different chemistries and charge/discharge protocols demonstrate that our approach can achieve lower average root-mean-square-error (Oxford dataset: 0.7201%, Massachusetts Institute of Technology (MIT) dataset: 0.7184%) compared to baseline models. An in-depth analysis of the physical significance of the screened features improves the interpretability of the features. This work underscores the significant potential of leveraging localized feature enhancement in SOH estimation by systematically integrating degradation-sensitive features, thereby offering precise estimation.