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This study presents a Fisher-inspired feature selection (FIFS) framework and a sparrow search algorithm optimized graph neural network (SSA-GNN) for improving the state of health (SOH) estimation of lithium-ion batteries. The FIFS framework combines physical insights with data-driven models to enhance feature selection accuracy, while the SSA-GNN model optimizes hyperparameters for better estimation performance. Experimental results demonstrate significant reductions in mean absolute error and root mean squ

Author

Carlos

Language

EN