Thermal runaway(TR)is a critical issue hindering the large-scale application of lithium-ion batteries(LIBs).Understanding the thermal safety behavior of LIBs at the cell and module level under different state of charg...Thermal runaway(TR)is a critical issue hindering the large-scale application of lithium-ion batteries(LIBs).Understanding the thermal safety behavior of LIBs at the cell and module level under different state of charges(SOCs)has significant implications for reinforcing the thermal safety design of the lithium-ion battery module.This study first investigates the thermal safety boundary(TSB)correspondence at the cells and modules level under the guidance of a newly proposed concept,safe electric quantity boundary(SEQB).A reasonable thermal runaway propagation(TRP)judgment indicator,peak heat transfer power(PHTP),is proposed to predict whether TRP occurs.Moreover,a validated 3D model is used to quantitatively clarify the TSB at different SOCs from the perspective of PHTP,TR trigger temperature,SOC,and the full cycle life.Besides,three different TRP transfer modes are discovered.The interconversion relationship of three different TRP modes is investigated from the perspective of PHTP.This paper explores the TSB of LIBs under different SOCs at both cell and module levels for the first time,which has great significance in guiding the thermal safety design of battery systems.展开更多
Recent experimental evidence of the charge-6e condensed phase in Kagome superconductors has generated significant interest.This study investigates the unconventional superconductivity in the Kagome superconductor CsV_...Recent experimental evidence of the charge-6e condensed phase in Kagome superconductors has generated significant interest.This study investigates the unconventional superconductivity in the Kagome superconductor CsV_(3)Sb_(5),focusing on the emergence of charge-6e superconductivity(SC)at temperatures higher than the conventional charge-2e SC state.By modeling the phase coherence of the SC order parameter using a frustrated antiferromagnetic XY model on an emergent Kagome lattice,the condensation of fractional vortices with 1/3 vorticity stabilizes the phase coherence in exp(i3θ),resulting in a charge-6e SC state.Using a tensor network approach tailored for frustrated spin systems,a Berezinskii-Kosterlitz-Thouless transition is identified at T_(c)/J≃0.075,where the unbinding of 1/3 fractional vortex-antivortex pairs transforms the system from the charge-6e SC phase to the normal phase.Below T_(c),the 1/3 fractional vortex correlations exhibit a power-law decay,whereas the integer vortex correlations decay exponentially,reflecting the dominance of charge-6e SC in the absence of charge-2e SC.The results provide a theoretical understanding of charge-6e SC in two-dimensional Kagome superconductors,emphasizing the interaction between fractional vortices,frustration,and topology in stabilizing the exotic SC phase.展开更多
Aim To research and develop a battery management system(BMS)with the state of charge(SOC)indicator for electric vehicles (EVs).Methods On the basis of analyzing the electro-chemical characteristics of lead-acid. batte...Aim To research and develop a battery management system(BMS)with the state of charge(SOC)indicator for electric vehicles (EVs).Methods On the basis of analyzing the electro-chemical characteristics of lead-acid. battery, the state of charge indicator for lead-acid battery was developed by means of an algorithm based on combination of ampere-hour, Peukert's equation and open-voltage method with the compensation of temperature,aging,self- discharging,etc..Results The BMS based on this method can attain an accurate surplus capa- city whose error is less than 5% in static experiments.It is proved by experiments that the BMS is reliable and can give the driver an accurate surplus capacity,precisely monitor the individual battery modules as the same time,even detect and warn the problems early,and so on. Conclusion A BMS can make the energy of the storage batteries used efficiently, develop the batteries cycle life,and increase the driving distance of EVs.展开更多
A fuzzy model was established to estimate the state of charge（SOC） of a lithium-ion battery for electric vehicles.The robust Gustafson-Kessel（GK） clustering algorithm based on clustering validity indices was appli...A fuzzy model was established to estimate the state of charge（SOC） of a lithium-ion battery for electric vehicles.The robust Gustafson-Kessel（GK） clustering algorithm based on clustering validity indices was applied to identify the structure and antecedent parameters of the model.The least squares algorithm was utilized to determine the consequent parameters.Validation results show that this model can provide accurate SOC estimation for the lithium-ion battery and satisfy the requirement for practical electric vehicle applications.展开更多
The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial...The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.展开更多
In the field of energy storage,it is very important to predict the state of charge and the state of health of lithium-ion batteries.In this paper,we review the current widely used equivalent circuit and electrochemica...In the field of energy storage,it is very important to predict the state of charge and the state of health of lithium-ion batteries.In this paper,we review the current widely used equivalent circuit and electrochemical models for battery state predictions.The review demonstrates that machine learning and deep learning approaches can be used to construct fast and accurate data-driven models for the prediction of battery performance.The details,advantages,and limitations of these approaches are presented,compared,and summarized.Finally,future key challenges and opportunities are discussed.展开更多
Lithium-ion batteries are widely used in electric vehicles and electronics, and their thermal safety receives widespread attention from consumers. In our study, thermal runaway testing was conducted on the thermal sta...Lithium-ion batteries are widely used in electric vehicles and electronics, and their thermal safety receives widespread attention from consumers. In our study, thermal runaway testing was conducted on the thermal stability of commercial lithium-ion batteries, and the internal structure of the battery was analyzed with an in-depth focus on the key factors of the thermal runaway. Through the study of the structure and thermal stability of the cathode, anode, and separator, the results showed that the phase transition reaction of the separator was the key factor affecting the thermal runaway of the battery for the condition of a low state of charge.展开更多
The technology deployed for lithium-ion battery state of charge(SOC)estimation is an important part of the design of electric vehicle battery management systems.Accurate SOC estimation can forestall excessive charging...The technology deployed for lithium-ion battery state of charge(SOC)estimation is an important part of the design of electric vehicle battery management systems.Accurate SOC estimation can forestall excessive charging and discharging of lithium-ion batteries,thereby improving discharge efficiency and extending cycle life.In this study,the key lithium-ion battery SOC estimation technologies are summarized.First,the research status of lithium-ion battery modeling is introduced.Second,the main technologies and difficulties in model parameter identification for lithium-ion batteries are discussed.Third,the development status and advantages and disadvantages of SOC estimation methods are summarized.Finally,the current research problems and prospects for development trends are summarized.展开更多
We propose a simple scheme to generate x-type four-charge entangled states by using SQUID-based charge qubits capacitively coupled to a transmission line resonator （TLR）. The coupling between the superconducting qub...We propose a simple scheme to generate x-type four-charge entangled states by using SQUID-based charge qubits capacitively coupled to a transmission line resonator （TLR）. The coupling between the superconducting qubit and the TLR can be effectively controlled by properly adjusting the control parameters of the charge qubit. The experimental feasibility of our scheme is also shown.展开更多
Using electric vehicles(EVs)for transportation is considered as a necessary component for managing sustainable development and environmental issues.The present concerns regarding the environment,such as rapid fossil f...Using electric vehicles(EVs)for transportation is considered as a necessary component for managing sustainable development and environmental issues.The present concerns regarding the environment,such as rapid fossil fuel depletion,increases in air pollution,accelerating energy demands,global warming,and climate change,have paved the way for the electrification of the transport sector.EVs can address all of the aforementioned issues.Portable power supplies have become the lifeline of the EV world,especially lithium-ion(Li-ion)batteries.Li-ion batteries have attracted considerable attention in the EV industry,owing to their high energy density,power density,lifespan,nominal voltage,and cost.One major issue with such batteries concerns providing a quick and accurate estimation of a battery’s state and health;therefore,accurate determinations of the battery’S performance and health,as well as an accurate prediction of its life,are necessary to ensure reliability and efficiency.This study conducts a review of the technological briefs of EVs and their types,as well as the corresponding battery characteristics.Various aspects of recent research and developments in Li-ion battery prognostics and health monitoring are summarized,along with the techniques,algorithms,and models used for current/voltage estimations,state-of-charge(SoC)estimations,capacity estimations,and remaining-useful-life predictions.展开更多
The estimation of state of charge(SOC)using deep neural networks(DNN)generally requires a considerable number of labelled samples for training,which refer to the current and voltage pieces with knowing their correspon...The estimation of state of charge(SOC)using deep neural networks(DNN)generally requires a considerable number of labelled samples for training,which refer to the current and voltage pieces with knowing their corresponding SOCs.However,the collection of labelled samples is costly and time-consuming.In contrast,the unlabelled training samples,which consist of the current and voltage data with unknown SOCs,are easy to obtain.In view of this,this paper proposes an improved DNN for SOC estimation by effectively using both a pool of unlabelled samples and a limited number of labelled samples.Besides the traditional supervised network,the proposed method uses an input reconstruction network to reformulate the time dependency features of the voltage and current.In this way,the developed network can extract useful information from the unlabelled samples.The proposed method is validated under different drive cycles and temperature conditions.The results reveal that the SOC estimation accuracy of the DNN trained with both labelled and unlabelled samples outperforms that of only using a limited number of labelled samples.In addition,when the dataset with reduced number of labelled samples to some extent is used to test the developed network,it is found that the proposed method performs well and is robust in producing the model outputs with the required accuracy when the unlabelled samples are involved in the model training.Furthermore,the proposed method is evaluated with different recurrent neural networks(RNNs)applied to the input reconstruction module.The results indicate that the proposed method is feasible for various RNN algorithms,and it could be flexibly applied to other conditions as required.展开更多
The structural features and three-dimensional nature of the charge density wave （CDW） state of the layered chalcogenide 1T-TaSe2-xTex （0≤x≤2.0） are characterized by Cs-corrected transmission electron microscopy ...The structural features and three-dimensional nature of the charge density wave （CDW） state of the layered chalcogenide 1T-TaSe2-xTex （0≤x≤2.0） are characterized by Cs-corrected transmission electron microscopy measurements. Notable changes of both average structure and the CDW state arising from Te substitution for Se are clearly demonstrated in samples with x〉0.3. The commensurate CDW state characterized by the known star-of-David clustering in the 1T-TaSe2 crystal becomes visibly unstable with Te substitution and vanishes when x=0.3. The 1T-TaSe2-xTex （0.3≤x≤1.3） samples generally adopt a remarkable incommensurate CDW state with monoclinic distortion, which could be fundamentally in correlation with the strong qq-dependent electron-phonon coupling-induced period-lattice-distortion as identified in TaTe22. Systematic analysis demonstrates that the occurrence of superconductivity is related to the suppression of the commensurate CDW phase and the presence of discommensuration is an evident structural feature observed in the superconducting samples.展开更多
In order to improve the accuracy of the battery state of charge（SOC） estimation, in this paper we take a lithiumion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, ...In order to improve the accuracy of the battery state of charge（SOC） estimation, in this paper we take a lithiumion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate.Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded.展开更多
It is widely accepted that the variation of ambient temperature has great influence on the battery model parameters and state-of-charge(SOC) estimation, and the accurate SOC estimation is a significant issue for devel...It is widely accepted that the variation of ambient temperature has great influence on the battery model parameters and state-of-charge(SOC) estimation, and the accurate SOC estimation is a significant issue for developing the battery management system in electric vehicles. To address this problem, in this paper we propose an enhanced equivalent circuit model(ECM) considering the influence of different ambient temperatures on the open-circuit voltage for a lithium-ion battery. Based on this model, the exponential-function fitting method is adopted to identify the battery parameters according to the test data collected from the experimental platform. And then, the extended Kalman filter(EKF) algorithm is employed to estimate the battery SOC of this battery ECM. The performance of the proposed ECM is verified by using the test profiles of hybrid pulse power characterization(HPPC) and the standard US06 driving cycles(US06) at various ambient temperatures, and by comparing with the common ECM with a second-order resistance capacitor. The simulation and experimental results show that the enhanced battery ECM can improve the battery SOC estimation accuracy under different operating conditions.展开更多
Stripper gas and terminal potential play a key role for the charge state distribution in a tandem pelletron accelerator. The knowledge of this distribution is important for experiments performed on tandem accelerators...Stripper gas and terminal potential play a key role for the charge state distribution in a tandem pelletron accelerator. The knowledge of this distribution is important for experiments performed on tandem accelerators. The charge state distribution of B, C, Si, Ni, Cu and Au beams is measured by using Ar as stripper gas, and terminal potential is varied from 0.3 to 3.0 MV on 5UDH-2 tandem pelletron accelerator installed at the National Centre for Physics, Islamabad. The individual charge state is measured after the switching magnet at 15° in high-energy portion. It is observed that the higher charge states are stable in the range of lower and middle atomic masses of periodic table, whereas higher atomic mass(Au) shows beam current instability in higher charge states. For carbon,the charge distribution at 1.7 MV terminal potential by varying stripper gas pressure is also studied, which resulted in decreased overall transmission with good current value for higher charge states.展开更多
Fe K-shell ionization cross sections induced by 2.4-6.0 MeV Xe^20＋ are measured and compared with different binary- encounter-approximation （BEA） models. The results indicate that the BEA model corrected both by th...Fe K-shell ionization cross sections induced by 2.4-6.0 MeV Xe^20＋ are measured and compared with different binary- encounter-approximation （BEA） models. The results indicate that the BEA model corrected both by the Coulomb repulsion and by the effective nuclear charge （Zeff） agrees well with the experimental data. Comparison of Fe K-shell X-ray emission induced by 5 MeV xenon ions with different initial charge states （20＋, 22＋, 26＋, 30＋） verifies the applicability of the effective nuclear charge （Zeff） correction for the BEA model. It is found that Zeff correction is reasonable to describe direct ionization induced by xenon ions with no initial M-shell vacancies. However, when the M shell is opened, the Zeff corrected BEA model is unable to explain the inner-shell ionization, and the electron transfer by molecular-orbital promotion should be considered.展开更多
基金supported by the National Natural Science Foundation of China(No.U20A20310 and No.52176199)sponsored by the Program of Shanghai Academic/Technology Research Leader(No.22XD1423800)。
文摘Thermal runaway(TR)is a critical issue hindering the large-scale application of lithium-ion batteries(LIBs).Understanding the thermal safety behavior of LIBs at the cell and module level under different state of charges(SOCs)has significant implications for reinforcing the thermal safety design of the lithium-ion battery module.This study first investigates the thermal safety boundary(TSB)correspondence at the cells and modules level under the guidance of a newly proposed concept,safe electric quantity boundary(SEQB).A reasonable thermal runaway propagation(TRP)judgment indicator,peak heat transfer power(PHTP),is proposed to predict whether TRP occurs.Moreover,a validated 3D model is used to quantitatively clarify the TSB at different SOCs from the perspective of PHTP,TR trigger temperature,SOC,and the full cycle life.Besides,three different TRP transfer modes are discovered.The interconversion relationship of three different TRP modes is investigated from the perspective of PHTP.This paper explores the TSB of LIBs under different SOCs at both cell and module levels for the first time,which has great significance in guiding the thermal safety design of battery systems.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFA1406400)。
文摘Recent experimental evidence of the charge-6e condensed phase in Kagome superconductors has generated significant interest.This study investigates the unconventional superconductivity in the Kagome superconductor CsV_(3)Sb_(5),focusing on the emergence of charge-6e superconductivity(SC)at temperatures higher than the conventional charge-2e SC state.By modeling the phase coherence of the SC order parameter using a frustrated antiferromagnetic XY model on an emergent Kagome lattice,the condensation of fractional vortices with 1/3 vorticity stabilizes the phase coherence in exp(i3θ),resulting in a charge-6e SC state.Using a tensor network approach tailored for frustrated spin systems,a Berezinskii-Kosterlitz-Thouless transition is identified at T_(c)/J≃0.075,where the unbinding of 1/3 fractional vortex-antivortex pairs transforms the system from the charge-6e SC phase to the normal phase.Below T_(c),the 1/3 fractional vortex correlations exhibit a power-law decay,whereas the integer vortex correlations decay exponentially,reflecting the dominance of charge-6e SC in the absence of charge-2e SC.The results provide a theoretical understanding of charge-6e SC in two-dimensional Kagome superconductors,emphasizing the interaction between fractional vortices,frustration,and topology in stabilizing the exotic SC phase.
文摘Aim To research and develop a battery management system(BMS)with the state of charge(SOC)indicator for electric vehicles (EVs).Methods On the basis of analyzing the electro-chemical characteristics of lead-acid. battery, the state of charge indicator for lead-acid battery was developed by means of an algorithm based on combination of ampere-hour, Peukert's equation and open-voltage method with the compensation of temperature,aging,self- discharging,etc..Results The BMS based on this method can attain an accurate surplus capa- city whose error is less than 5% in static experiments.It is proved by experiments that the BMS is reliable and can give the driver an accurate surplus capacity,precisely monitor the individual battery modules as the same time,even detect and warn the problems early,and so on. Conclusion A BMS can make the energy of the storage batteries used efficiently, develop the batteries cycle life,and increase the driving distance of EVs.
基金Sponsored by the National High Technology Research and Development Program of China("863"Program)(2003AA501800)
文摘A fuzzy model was established to estimate the state of charge（SOC） of a lithium-ion battery for electric vehicles.The robust Gustafson-Kessel（GK） clustering algorithm based on clustering validity indices was applied to identify the structure and antecedent parameters of the model.The least squares algorithm was utilized to determine the consequent parameters.Validation results show that this model can provide accurate SOC estimation for the lithium-ion battery and satisfy the requirement for practical electric vehicle applications.
基金Project supported by the National High Technology Research and Development Program of China (Grant No. 2011AA110303)the Beijing Municipal Science & Technology Project,China (Grant No. Z111100064311001)
文摘The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.
基金funding support from the Department of Science and Technology of Guangdong Province(2019A050510043)the Department of Science and Technology of Zhuhai City(ZH22017001200059PWC)+1 种基金the National Natural Science Foundation of China(2210050123)the China Postdoctoral Science Foundation(2021TQ0161 and 2021M691709)。
文摘In the field of energy storage,it is very important to predict the state of charge and the state of health of lithium-ion batteries.In this paper,we review the current widely used equivalent circuit and electrochemical models for battery state predictions.The review demonstrates that machine learning and deep learning approaches can be used to construct fast and accurate data-driven models for the prediction of battery performance.The details,advantages,and limitations of these approaches are presented,compared,and summarized.Finally,future key challenges and opportunities are discussed.
基金financial supports from National Key R&D Program of China (2018YFC1902200)the key technologies R&D program of Tianjin (18YFZCGX00240)key R&D Program of China Automotive Technology and Research Center Co., Ltd. (18200116)。
文摘Lithium-ion batteries are widely used in electric vehicles and electronics, and their thermal safety receives widespread attention from consumers. In our study, thermal runaway testing was conducted on the thermal stability of commercial lithium-ion batteries, and the internal structure of the battery was analyzed with an in-depth focus on the key factors of the thermal runaway. Through the study of the structure and thermal stability of the cathode, anode, and separator, the results showed that the phase transition reaction of the separator was the key factor affecting the thermal runaway of the battery for the condition of a low state of charge.
基金supported by research on value model and technology application of patent operation of science and technology project(52094020000U)National Natural Science Foundation of China(52177193).
文摘The technology deployed for lithium-ion battery state of charge(SOC)estimation is an important part of the design of electric vehicle battery management systems.Accurate SOC estimation can forestall excessive charging and discharging of lithium-ion batteries,thereby improving discharge efficiency and extending cycle life.In this study,the key lithium-ion battery SOC estimation technologies are summarized.First,the research status of lithium-ion battery modeling is introduced.Second,the main technologies and difficulties in model parameter identification for lithium-ion batteries are discussed.Third,the development status and advantages and disadvantages of SOC estimation methods are summarized.Finally,the current research problems and prospects for development trends are summarized.
基金supported by the National Natural Science Foundations of China (Grant Nos. 10947017/A05 and 11074190)the Science Foundation of the Key Laboratory of Novel Thin Film Solar Cells, China (Grant No. KF200912)the Graduates' Innovative Scientific Research Project of Zhejiang Province, China (Grant No. 2011831)
文摘We propose a simple scheme to generate x-type four-charge entangled states by using SQUID-based charge qubits capacitively coupled to a transmission line resonator （TLR）. The coupling between the superconducting qubit and the TLR can be effectively controlled by properly adjusting the control parameters of the charge qubit. The experimental feasibility of our scheme is also shown.
基金by Department of Science and Technology,New Delhi(Indo-Norway consortium)project entitled“Integrated Renewable Resources and Storage Operation and Management”program.
文摘Using electric vehicles(EVs)for transportation is considered as a necessary component for managing sustainable development and environmental issues.The present concerns regarding the environment,such as rapid fossil fuel depletion,increases in air pollution,accelerating energy demands,global warming,and climate change,have paved the way for the electrification of the transport sector.EVs can address all of the aforementioned issues.Portable power supplies have become the lifeline of the EV world,especially lithium-ion(Li-ion)batteries.Li-ion batteries have attracted considerable attention in the EV industry,owing to their high energy density,power density,lifespan,nominal voltage,and cost.One major issue with such batteries concerns providing a quick and accurate estimation of a battery’s state and health;therefore,accurate determinations of the battery’S performance and health,as well as an accurate prediction of its life,are necessary to ensure reliability and efficiency.This study conducts a review of the technological briefs of EVs and their types,as well as the corresponding battery characteristics.Various aspects of recent research and developments in Li-ion battery prognostics and health monitoring are summarized,along with the techniques,algorithms,and models used for current/voltage estimations,state-of-charge(SoC)estimations,capacity estimations,and remaining-useful-life predictions.
基金the financial support from the China Scholarship Council(CSC)(No.202207550010)。
文摘The estimation of state of charge(SOC)using deep neural networks(DNN)generally requires a considerable number of labelled samples for training,which refer to the current and voltage pieces with knowing their corresponding SOCs.However,the collection of labelled samples is costly and time-consuming.In contrast,the unlabelled training samples,which consist of the current and voltage data with unknown SOCs,are easy to obtain.In view of this,this paper proposes an improved DNN for SOC estimation by effectively using both a pool of unlabelled samples and a limited number of labelled samples.Besides the traditional supervised network,the proposed method uses an input reconstruction network to reformulate the time dependency features of the voltage and current.In this way,the developed network can extract useful information from the unlabelled samples.The proposed method is validated under different drive cycles and temperature conditions.The results reveal that the SOC estimation accuracy of the DNN trained with both labelled and unlabelled samples outperforms that of only using a limited number of labelled samples.In addition,when the dataset with reduced number of labelled samples to some extent is used to test the developed network,it is found that the proposed method performs well and is robust in producing the model outputs with the required accuracy when the unlabelled samples are involved in the model training.Furthermore,the proposed method is evaluated with different recurrent neural networks(RNNs)applied to the input reconstruction module.The results indicate that the proposed method is feasible for various RNN algorithms,and it could be flexibly applied to other conditions as required.
基金Supported by the National Basic Research Program of China under Grant Nos 2015CB921300 and 2012CB821404the National Key Research and Development Program of China under Grant Nos 2016YFA0300300 and 2016YFA0300404+1 种基金the National Natural Science Foundation of China under Grant Nos 11474323,11604372,11274368,91221102,11190022,11674326 and 91422303the Strategic Priority Research Program(B)of the Chinese Academy of Sciences under Grant No XDB07020000
文摘The structural features and three-dimensional nature of the charge density wave （CDW） state of the layered chalcogenide 1T-TaSe2-xTex （0≤x≤2.0） are characterized by Cs-corrected transmission electron microscopy measurements. Notable changes of both average structure and the CDW state arising from Te substitution for Se are clearly demonstrated in samples with x〉0.3. The commensurate CDW state characterized by the known star-of-David clustering in the 1T-TaSe2 crystal becomes visibly unstable with Te substitution and vanishes when x=0.3. The 1T-TaSe2-xTex （0.3≤x≤1.3） samples generally adopt a remarkable incommensurate CDW state with monoclinic distortion, which could be fundamentally in correlation with the strong qq-dependent electron-phonon coupling-induced period-lattice-distortion as identified in TaTe22. Systematic analysis demonstrates that the occurrence of superconductivity is related to the suppression of the commensurate CDW phase and the presence of discommensuration is an evident structural feature observed in the superconducting samples.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61004048 and 61201010)
文摘In order to improve the accuracy of the battery state of charge（SOC） estimation, in this paper we take a lithiumion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate.Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded.
基金Project supported by the National Natural Science Foundation of China(Grant No.51675423)
文摘It is widely accepted that the variation of ambient temperature has great influence on the battery model parameters and state-of-charge(SOC) estimation, and the accurate SOC estimation is a significant issue for developing the battery management system in electric vehicles. To address this problem, in this paper we propose an enhanced equivalent circuit model(ECM) considering the influence of different ambient temperatures on the open-circuit voltage for a lithium-ion battery. Based on this model, the exponential-function fitting method is adopted to identify the battery parameters according to the test data collected from the experimental platform. And then, the extended Kalman filter(EKF) algorithm is employed to estimate the battery SOC of this battery ECM. The performance of the proposed ECM is verified by using the test profiles of hybrid pulse power characterization(HPPC) and the standard US06 driving cycles(US06) at various ambient temperatures, and by comparing with the common ECM with a second-order resistance capacitor. The simulation and experimental results show that the enhanced battery ECM can improve the battery SOC estimation accuracy under different operating conditions.
文摘Stripper gas and terminal potential play a key role for the charge state distribution in a tandem pelletron accelerator. The knowledge of this distribution is important for experiments performed on tandem accelerators. The charge state distribution of B, C, Si, Ni, Cu and Au beams is measured by using Ar as stripper gas, and terminal potential is varied from 0.3 to 3.0 MV on 5UDH-2 tandem pelletron accelerator installed at the National Centre for Physics, Islamabad. The individual charge state is measured after the switching magnet at 15° in high-energy portion. It is observed that the higher charge states are stable in the range of lower and middle atomic masses of periodic table, whereas higher atomic mass(Au) shows beam current instability in higher charge states. For carbon,the charge distribution at 1.7 MV terminal potential by varying stripper gas pressure is also studied, which resulted in decreased overall transmission with good current value for higher charge states.
基金Project supported by the National Basic Research Program of China(Grant No.2010CB832902)the National Natural Science Foundation of China(Grant Nos.11275241,11205225,11105192,and 11275238)
文摘Fe K-shell ionization cross sections induced by 2.4-6.0 MeV Xe^20＋ are measured and compared with different binary- encounter-approximation （BEA） models. The results indicate that the BEA model corrected both by the Coulomb repulsion and by the effective nuclear charge （Zeff） agrees well with the experimental data. Comparison of Fe K-shell X-ray emission induced by 5 MeV xenon ions with different initial charge states （20＋, 22＋, 26＋, 30＋） verifies the applicability of the effective nuclear charge （Zeff） correction for the BEA model. It is found that Zeff correction is reasonable to describe direct ionization induced by xenon ions with no initial M-shell vacancies. However, when the M shell is opened, the Zeff corrected BEA model is unable to explain the inner-shell ionization, and the electron transfer by molecular-orbital promotion should be considered.
