Accurately predicting the knee point cycle number,marking the onset of accelerated capacity degradation,is the first critical signal for managing lithium-ion battery life cycles in electric vehicles and stationary ene...Accurately predicting the knee point cycle number,marking the onset of accelerated capacity degradation,is the first critical signal for managing lithium-ion battery life cycles in electric vehicles and stationary energy storage systems.However,the inherent electrochemo-mechanical-thermal complexities of battery aging present significant challenges for physics-based models and machine-learning models,often leading to reduced predictive accuracy.Our study developed a comprehensive dataset comprising 20 lithium nickel manganese cobalt oxide(NCM)/graphite cells(0.5-1 C)from our lab and 162 commercial lithium iron phosphate(LFP)/graphite cells(3-6 C)from the public database,with knee point observed between 100 and 1000 cycles.We proposed a new strategy to extract novel features with strong physical context from early-cycle voltage curves,enabling precise knee point predictions across the chemistries without the need for extensive cycling histories.Our model achieved a mean absolute percentage error(MAPE)of 7%for knee point prediction using five selected features.Remarkably,the model yielded 8%MAPE with only one single feature across the initial 200 cycles,and 10%MAPE when applying five features across the initial 50 cycles,spanning different battery chemistries.This work highlights the potential of integrating multi-chemistry datasets with data-driven modeling to forecast aging patterns across diverse battery chemistries,advancing battery longevity and reliability.展开更多
The interfacial debonding between the active layer and the current collector has been recognized as a critical mechanism for battery fading,and thus has attracted great efforts focused on the related analyses.However,...The interfacial debonding between the active layer and the current collector has been recognized as a critical mechanism for battery fading,and thus has attracted great efforts focused on the related analyses.However,much still remains to be studied regarding practical methods for suppressing electrode debonding,especially from the perspective of mechanics.In this paper,a pre-strain strategy of current collectors to alleviate electrode debonding is proposed.An analytical model for a symmetric electrode with a deformable and limited-thickness current collector is developed to analyze the debonding behavior involving both a pre-strain of the current collector and an eigen-strain of the active layers.The results reveal that the well-designed pre-strain can significantly delay the debonding onset(by up to 100%)and considerably reduce the debonding size.The critical values of the pre-strain are identified,and the pre-strain design principles are also provided.Based on these findings,this work sheds light on the mechanical design to suppress electrode degradation.展开更多
基金supported by Startup funding from the University of Delaware。
文摘Accurately predicting the knee point cycle number,marking the onset of accelerated capacity degradation,is the first critical signal for managing lithium-ion battery life cycles in electric vehicles and stationary energy storage systems.However,the inherent electrochemo-mechanical-thermal complexities of battery aging present significant challenges for physics-based models and machine-learning models,often leading to reduced predictive accuracy.Our study developed a comprehensive dataset comprising 20 lithium nickel manganese cobalt oxide(NCM)/graphite cells(0.5-1 C)from our lab and 162 commercial lithium iron phosphate(LFP)/graphite cells(3-6 C)from the public database,with knee point observed between 100 and 1000 cycles.We proposed a new strategy to extract novel features with strong physical context from early-cycle voltage curves,enabling precise knee point predictions across the chemistries without the need for extensive cycling histories.Our model achieved a mean absolute percentage error(MAPE)of 7%for knee point prediction using five selected features.Remarkably,the model yielded 8%MAPE with only one single feature across the initial 200 cycles,and 10%MAPE when applying five features across the initial 50 cycles,spanning different battery chemistries.This work highlights the potential of integrating multi-chemistry datasets with data-driven modeling to forecast aging patterns across diverse battery chemistries,advancing battery longevity and reliability.
基金Project supported by the National Natural Science Foundation of China(Nos.12072183,11872236,12172205)the Key Research Project of Zhejiang Laboratory of China(No.2021PE0AC02)。
文摘The interfacial debonding between the active layer and the current collector has been recognized as a critical mechanism for battery fading,and thus has attracted great efforts focused on the related analyses.However,much still remains to be studied regarding practical methods for suppressing electrode debonding,especially from the perspective of mechanics.In this paper,a pre-strain strategy of current collectors to alleviate electrode debonding is proposed.An analytical model for a symmetric electrode with a deformable and limited-thickness current collector is developed to analyze the debonding behavior involving both a pre-strain of the current collector and an eigen-strain of the active layers.The results reveal that the well-designed pre-strain can significantly delay the debonding onset(by up to 100%)and considerably reduce the debonding size.The critical values of the pre-strain are identified,and the pre-strain design principles are also provided.Based on these findings,this work sheds light on the mechanical design to suppress electrode degradation.
