Bin Wang, Lifeng Zhang
Abstract Accurate state-of-health (SOH) estimation is crucial for ensuring the safe and reliable operation of lithium-ion batteries (LIBs). However, conventional SOH estimation methods mainly rely on a small number of highly correlated features or isolated model architectures, which often fail to capture the multi-dimensional degradation characteristics and complex nonlinear behaviors of LIBs. Therefore, this paper proposes a multi-source feature fusion-based chaotic evolution-optimized gated parallel echo state network-Transformer (CEO-GPNFormer) for battery SOH estimation. Multi-dimensional indirect health indicators are first selected using dual correlation criteria. CEO-optimized variational mode decomposition is then applied to extract capacity-mode features, which are effectively fused with the selected indicators to construct high-quality inputs. These fused features are subsequently fed into the CEO-GPNFormer model to complete SOH estimation. Validation on two datasets and six benchmark models shows that the proposed approach achieves marked improvements in both within-dataset cross-validation and single-battery prediction tasks, yielding mean absolute error values below 0.007 and average root mean squared error values below 0.01 across both datasets. These results verify the superior accuracy, robustness, and generalization capability of the proposed method.