Lithium-ion battery life prediction based on hybrid scale health factors with LSTM-Transformer model
To enhance lithium-ion battery remaining useful life (RUL) prediction accuracy,we propose an integrated model using hybrid scale health factors.We address challenges of noisy data,limited quantity,and incomplete capture of nonlinear characteristics.Firstly,we use singular value decomposition (SVD) to process capacitance signals,optimizing variational mode decomposition (VMD) for denoising and reconstructing the direct health factor,SR.We introduce an amplitude-phase perturbation (APP) data augmentation method to generate artificially labeled data,ESR,based on changes in SR data distribution.Combined with three indirect health factors,selected using GRA algorithm,we establish a comprehensive mixed-scale life characteristic information.Additionally,we improve Transformer model's decoder structure with LSTM and optimize key hyperparameters using Optuna framework.Experimental results on NASA data show RMSE within 2.39％ and MAE within 1.59％,with improved stability and narrower 95％ confidence intervals compared to RNN,LSTM,Transformer,and existing models.
lithium-ion batteryhybrid scale feature extractionLSTM-Transformer modelAPP data augmentationOptuna framework
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