Prediction of remaining useful life for lithium-ion battery using recurrence plot analysis
During the long-term cycling of lithium-ion batteries(LIBs),performance degradation is inevitable,which directly impacts the stable operation of the battery system.To address this,this paper proposes a novel for predicting the remaining useful life of LIBs based on multi-scale features derived from recurrence plots.This approach aims to overcome the limitations of extracting key degradation features from one-dimensional(1D)signals,such as voltage data.By leveraging the rich spatiotemporal degradation features captured in recurrence plots,we first develop a deep learning architecture for effective multi-scale feature extraction.This architecture detects temporal variations within the same voltage region across multiple cycles and examines the spatial evolution of recurrence plots between adjacent voltage regions using variable-sized receptive fields.This approach enables the extraction of deep multi-scale features,which are then mapped to RUL modeling.Comprehensive evaluation experiments were performed to systematically validate the predictive effectiveness of the proposed method.The results suggest that by using a limited number of charge process recurrence plots as input,the model achieves rapid convergence and accurate predictions.Additionally,in cross-rate prediction scenarios,the proposed method improves performance metrics,with absolute error(MAE)and root mean squared error(RMSE)reduced by approximately 7-fold and 5.7-fold,respectively,at a 2C rate compared to shallow indicators.Finally,comparative experiments with 1D sequence inputs further validate the effectiveness of predicting the RUL of LIBs using multiscale features of recurrence plots.This approach achieves performance improvements of approximately 50% and 43% in various evaluation metrics,while requiring relatively minimal time-series voltage sampling data for imaging.
lithium-ion batteriesremaining useful life predictionrecurrence plotmulti-scale feature extraction
国家科技期刊平台
NSTL
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