Gel polymer electrolytes(GPEs)have attracted extensive attention in lithium-ion batteries due to their high security and excellent electrochemical performance.However,their inferior Li-ion transference number,low room...Gel polymer electrolytes(GPEs)have attracted extensive attention in lithium-ion batteries due to their high security and excellent electrochemical performance.However,their inferior Li-ion transference number,low room-temperature ionic conductivity,and poor long cycle stability raise challenges in practical applications.Herein,a flexible poly(vinylidene fluoride-cohexafluoropropylene)-butanedinitrile(PVDF-HFP-SN)-based GPE(PSGPE)is synthesized successfully by a general immersion precipitation method.The resultant PSGPEs have numerous connecting pores to ensure sufficient space for liquid electrolytes.Moreover,the reduced crystallinity of PVDF-HFP and the high polarity of SN can reduce the energy barrier of Li-ions shuttling between pores.The synergistic effect possesses a high ionic conductivity of 1.35 mS·cm^(-1)at room temperature with a high Li-ion transference number of 0.69.The PVDF-HFP-SN-based GPE is applied in a LiFePO_(4)/graphite battery,which can realize stable cycling performance for 350 cycles and good rate performance at room temperature.These results demonstrate that the novel PSGPE possesses advantage in simplified production process,which can improve the practicability of gel polymer lithium-ion batteries.展开更多
In data-driven fault diagnosis for turbo-generator sets,the fault samples are usually expensive to obtain,and inevitably with noise,which will both lead to an unsatisfying identification performance of diagnosis model...In data-driven fault diagnosis for turbo-generator sets,the fault samples are usually expensive to obtain,and inevitably with noise,which will both lead to an unsatisfying identification performance of diagnosis models.To address these issues,this paper proposes a fault diagnosis model for turbo-generator sets based on Weighted Extension Neural Network(W-ENN).WENN is a novel neural network which has three types of connection weights and an improved correlation function.The performance of the proposed model is validated against Extension Neural Network(ENN),Support Vector Machine(SVM),Relevance Vector Machine(RVM)and Extreme Learning Machine(ELM)based models.The results indicate that,on noisy small sample sets,the proposed model is superior to the other models in terms of higher identification accuracy with fewer samples and strong noise-tolerant ability.The findings of this study may serve as a powerful fault diagnosis model for turbo-generator sets on noisy small sample sets.展开更多
基金This work was funded by Huaneng Clean Energy Research Institute Found Project(No.TE-22-CERI01).
文摘Gel polymer electrolytes(GPEs)have attracted extensive attention in lithium-ion batteries due to their high security and excellent electrochemical performance.However,their inferior Li-ion transference number,low room-temperature ionic conductivity,and poor long cycle stability raise challenges in practical applications.Herein,a flexible poly(vinylidene fluoride-cohexafluoropropylene)-butanedinitrile(PVDF-HFP-SN)-based GPE(PSGPE)is synthesized successfully by a general immersion precipitation method.The resultant PSGPEs have numerous connecting pores to ensure sufficient space for liquid electrolytes.Moreover,the reduced crystallinity of PVDF-HFP and the high polarity of SN can reduce the energy barrier of Li-ions shuttling between pores.The synergistic effect possesses a high ionic conductivity of 1.35 mS·cm^(-1)at room temperature with a high Li-ion transference number of 0.69.The PVDF-HFP-SN-based GPE is applied in a LiFePO_(4)/graphite battery,which can realize stable cycling performance for 350 cycles and good rate performance at room temperature.These results demonstrate that the novel PSGPE possesses advantage in simplified production process,which can improve the practicability of gel polymer lithium-ion batteries.
基金the National Natural Science Foundation of China(No.51775272,No.51005114)The Fundamental Research Funds for the Central Universities,China(No.NS2014050)。
文摘In data-driven fault diagnosis for turbo-generator sets,the fault samples are usually expensive to obtain,and inevitably with noise,which will both lead to an unsatisfying identification performance of diagnosis models.To address these issues,this paper proposes a fault diagnosis model for turbo-generator sets based on Weighted Extension Neural Network(W-ENN).WENN is a novel neural network which has three types of connection weights and an improved correlation function.The performance of the proposed model is validated against Extension Neural Network(ENN),Support Vector Machine(SVM),Relevance Vector Machine(RVM)and Extreme Learning Machine(ELM)based models.The results indicate that,on noisy small sample sets,the proposed model is superior to the other models in terms of higher identification accuracy with fewer samples and strong noise-tolerant ability.The findings of this study may serve as a powerful fault diagnosis model for turbo-generator sets on noisy small sample sets.
