Developing precise and fast methods for short circuit detection is crucial for preventing or mitigating the risk of safety issues of lithium-ion batteries(LIBs).In this paper,we developed a Convolutional Neural Networ...Developing precise and fast methods for short circuit detection is crucial for preventing or mitigating the risk of safety issues of lithium-ion batteries(LIBs).In this paper,we developed a Convolutional Neural Networks(CNN)based model that can quickly and precisely predict the short circuit resistance of LIB cells during various working conditions.Cycling tests of cells with an external short circuit(ESC)are produced to obtain the database and generate the training/testing samples.The samples are sequences of voltage,current,charging capacity,charging energy,total charging capacity,total charging energy with a length of 120 s and frequency of 1 Hz,and their corresponding short circuit resistances.A big database with~6×10^(5)samples are generated,covering various short circuit resistances(47~470Ω),current loading modes(Constant current-constant voltage(CC-CV)and drive cycle),and electrochemical states(cycle numbers from 1 to 300).Results show that the average relative absolute error of five random sample splits is 6.75%±2.8%.Further parametric analysis indicates the accuracy estimation benefits from the appropriate model setups:the optimized input sequence length(~120 s),feature selection(at least one total capacity-related variable),and rational model design,using multiple layers with different kernel sizes.This work highlights the capabilities of machine learning algorithms and data-driven methodologies in real-time safety risk prediction for batteries.展开更多
Energy absorption performance has been a long-pursued research topic in designing desired materials and structures subject to external dynamic loading.Inspired by natural bio-structures,herein,we develop both numerica...Energy absorption performance has been a long-pursued research topic in designing desired materials and structures subject to external dynamic loading.Inspired by natural bio-structures,herein,we develop both numerical and theoretical models to analyze the energy absorption behaviors of Weaire,Floret,and Kagome-shaped thin-walled structures.We demonstrate that these bio-inspired structures possess superior energy absorption capabilities compared to the traditional thin-walled structures,with the specific energy absorption about 44%higher than the traditional honeycomb.The developed mechanical model captures the fundamental characteristics of the bio-inspired honeycomb,and the mean crushing force in all three structures is accurately predicted.Results indicate that although the basic energy absorption and deformation mode remain the same,varied geometry design and the corresponding material distribution can further boost the energy absorption of the structure,providing a much broader design space for the next-generation impact energy absorption structures and systems.展开更多
基金supported by the U.S.Department of Energy’s Office on Energy Efficiency and Renewable Energy(EERE)under the Advanced Manufacturing Office,award number DE-EE0009111。
文摘Developing precise and fast methods for short circuit detection is crucial for preventing or mitigating the risk of safety issues of lithium-ion batteries(LIBs).In this paper,we developed a Convolutional Neural Networks(CNN)based model that can quickly and precisely predict the short circuit resistance of LIB cells during various working conditions.Cycling tests of cells with an external short circuit(ESC)are produced to obtain the database and generate the training/testing samples.The samples are sequences of voltage,current,charging capacity,charging energy,total charging capacity,total charging energy with a length of 120 s and frequency of 1 Hz,and their corresponding short circuit resistances.A big database with~6×10^(5)samples are generated,covering various short circuit resistances(47~470Ω),current loading modes(Constant current-constant voltage(CC-CV)and drive cycle),and electrochemical states(cycle numbers from 1 to 300).Results show that the average relative absolute error of five random sample splits is 6.75%±2.8%.Further parametric analysis indicates the accuracy estimation benefits from the appropriate model setups:the optimized input sequence length(~120 s),feature selection(at least one total capacity-related variable),and rational model design,using multiple layers with different kernel sizes.This work highlights the capabilities of machine learning algorithms and data-driven methodologies in real-time safety risk prediction for batteries.
文摘Energy absorption performance has been a long-pursued research topic in designing desired materials and structures subject to external dynamic loading.Inspired by natural bio-structures,herein,we develop both numerical and theoretical models to analyze the energy absorption behaviors of Weaire,Floret,and Kagome-shaped thin-walled structures.We demonstrate that these bio-inspired structures possess superior energy absorption capabilities compared to the traditional thin-walled structures,with the specific energy absorption about 44%higher than the traditional honeycomb.The developed mechanical model captures the fundamental characteristics of the bio-inspired honeycomb,and the mean crushing force in all three structures is accurately predicted.Results indicate that although the basic energy absorption and deformation mode remain the same,varied geometry design and the corresponding material distribution can further boost the energy absorption of the structure,providing a much broader design space for the next-generation impact energy absorption structures and systems.
