Battery management systems(BMSs) play a vital role in ensuring efficient and reliable operations of lithium-ion batteries.The main function of the BMSs is to estimate battery states and diagnose battery health using b...Battery management systems(BMSs) play a vital role in ensuring efficient and reliable operations of lithium-ion batteries.The main function of the BMSs is to estimate battery states and diagnose battery health using battery open-circuit voltage(OCV).However,acquiring the complete OCV data online can be a challenging endeavor due to the time-consuming measurement process or the need for specific operating conditions required by OCV estimation models.In addressing these concerns,this study introduces a deep neural network-combined framework for accurate and robust OCV estimation,utilizing partial daily charging data.We incorporate a generative deep learning model to extract aging-related features from data and generate high-fidelity OCV curves.Correlation analysis is employed to identify the optimal partial charging data,optimizing the OCV estimation precision while preserving exceptional flexibility.The validation results,using data from nickel-cobalt-magnesium(NCM) batteries,illustrate the accurate estimation of the complete OCV-capacity curve,with an average root mean square errors(RMSE) of less than 3 mAh.Achieving this level of precision for OCV estimation requires only around 50 s collection of partial charging data.Further validations on diverse battery types operating under various conditions confirm the effectiveness of our proposed method.Additional cases of precise health diagnosis based on OCV highlight the significance of conducting online OCV estimation.Our method provides a flexible approach to achieve complete OCV estimation and holds promise for generalization to other tasks in BMSs.展开更多
Solid non-conjugated polymers have long been regarded as insulators due to deficiency of delocalizedπelectrons along the molecular chain framework.Up to date,origin of insulating polymer regulated charge transfer has...Solid non-conjugated polymers have long been regarded as insulators due to deficiency of delocalizedπelectrons along the molecular chain framework.Up to date,origin of insulating polymer regulated charge transfer has not yet been uncovered.In this work,we unleash the root origin of charge transport capability of insulating polymer in photocatalysis.We ascertain that insulating polymer plays crucial roles in fine tuning of electronic structure of transition metal chalcogenides(TMCs),which mainly include altering surface electron density of TMCs for accelerating charge transport kinetics,triggering the generation of defect over TMCs for prolonging carrier lifetime,and acting as hole-trapping mediator for retarding charge recombination.These synergistic roles contribute to the charge transfer of insulating polymer.Our work opens a new vista of utilizing solid insulating polymers for maneuvering charge transfer toward solar energy conversion.展开更多
Battery remaining charging time(RCT)prediction can facilitate charging management and alleviate mileage anxiety for electric vehicles(EVs).Also,it is of great significance to improve EV users’experience.However,the R...Battery remaining charging time(RCT)prediction can facilitate charging management and alleviate mileage anxiety for electric vehicles(EVs).Also,it is of great significance to improve EV users’experience.However,the RCT for a lithiumion battery pack in EVs changes with temperature and other battery parameters.This study proposes an electrothermal model-based method to accurately predict battery RCT.Firstly,a characteristic battery cell is adopted to represent the battery pack,thus an equivalent circuit model(ECM)of the characteristic battery cell is established to describe the electrical behaviors of a battery pack.Secondly,an equivalent thermal model(ETM)of the battery pack is developed by considering the influence of ambient temperature,thermal management,and battery connectors in the battery pack to calculate the temperature which is then fed back to the ECM to realize electrothermal coupling.Finally,the RCT prediction method is proposed based on the electrothermal model and validated in the wide temperature range from-20℃to 45℃.The experimental results show that the prediction error of the RCT in the whole temperature range is less than 1.5%.展开更多
The rapid advancement and broad application of machine learning(ML)have driven a groundbreaking revolution in computational biology.One of the most cutting-edge and important applications of ML is its integration with...The rapid advancement and broad application of machine learning(ML)have driven a groundbreaking revolution in computational biology.One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improve the sampling efficiency of the vast conformational space of large biomolecules.This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape.We first highlight the recent development of ML-aided enhanced sampling methods,including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential,or facilitate the exploration of the unsampled region of the energy landscape.Further,we review the development of autoencoder based methods that combine molecular simulations and deep learning to expand the search for protein conformations.Lastly,we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights.Collectively,this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.展开更多
Machine learning-based methods have emerged as a promising solution to accurate battery capacity estimation for battery management systems.However,they are generally developed in a supervised manner which requires a c...Machine learning-based methods have emerged as a promising solution to accurate battery capacity estimation for battery management systems.However,they are generally developed in a supervised manner which requires a considerable number of input features and corresponding capacities,leading to prohibitive costs and efforts for data collection.In response to this issue,this study proposes a convolutional neural network(CNN)based method to perform end-to-end capacity estimation by taking only raw impedance spectra as input.More importantly,an input reconstruction module is devised to effectively exploit impedance spectra without corresponding capacities in the training process,thereby significantly alleviating the cost of collecting training data.Two large battery degradation datasets encompassing over 4700 impedance spectra are developed to validate the proposed method.The results show that accurate capacity estimation can be achieved when substantial training samples with measured capacities are given.However,the estimation performance of supervised machine learning algorithms sharply deteriorates when fewer samples with measured capacities are available.In this case,the proposed method outperforms supervised benchmarks and can reduce the root mean square error by up to 50.66%.A further validation under different current rates and states of charge confirms the effectiveness of the proposed method.Our method provides a flexible approach to take advantage of unlabelled samples for developing data-driven models and is promising to be generalised to other battery management tasks.展开更多
The development of a battery management algorithm is highly dependent on high-quality battery operation data,especially the data in extreme conditions such as low temperatures.The data in faults are also essential for...The development of a battery management algorithm is highly dependent on high-quality battery operation data,especially the data in extreme conditions such as low temperatures.The data in faults are also essential for failure and safety management research.This study developed a battery big data platform to realize vehicle operation,energy interaction and data management.First,we developed an electric vehicle with vehicle navigation and position detection and designed an environmental cabin that allows the vehicle to operate autonomously.Second,charging and heating systems based on wireless energy transfer were developed and equipped on the vehicle to investigate optimal charging and heating methods of the batteries in the vehicle.Third,the data transmission network was designed,a real-time monitoring interface was developed,and the self-developed battery management system was used to measure,collect,upload,and store battery operation data in real time.Finally,experimental validation was performed on the platform.Results demonstrate the efficiency and reliability of the platform.Battery state of charge estimation is used as an example to illustrate the availability of battery operation data.展开更多
SnSe has attracted extensive attention due to its ultralow thermal conductivity and excellent thermoelectric properties.In this work,pressure-induced thermoelectric properties of Pnma SnSe are investigated via first-p...SnSe has attracted extensive attention due to its ultralow thermal conductivity and excellent thermoelectric properties.In this work,pressure-induced thermoelectric properties of Pnma SnSe are investigated via first-principles calculations.We uncover distinct energy isosurfaces topology transition of conduction band by applying pressure.The newly created conduction band valley caused by pressure has a distinct anisotropic shape compared to the old one.Inducing pressure can greatly enhance the anisotropy of electronic transport properties of the n-type Pnma SnSe.Furthermore,the lattice thermal conductivity also exhibits anisotropic behavior under pressure due to a special collaged phonon mode.The pressure-induced lattice thermal conductivity along the a-axis shows a slower growth trend than that along the b-axis and c-axis.The optimal ZT value of the n-type Pnma SnSe along the a-axis can reach 1.64 at room temperature.These results would be helpful for designing the Pnma SnSe-based materials for the potential thermoelectric and valleytronic applications.展开更多
Covalent organic framework(COF)film with highly exposed active sites is considered as the promising flexible selfsupported electrode for in-plane microsupercapacitor(MSC).Superlattice configuration assembled alternate...Covalent organic framework(COF)film with highly exposed active sites is considered as the promising flexible selfsupported electrode for in-plane microsupercapacitor(MSC).Superlattice configuration assembled alternately by different nanofilms based on van der Waals force can integrate the advantages of each isolated layer to exhibit unexpected performances as MSC film electrodes,which may be a novel option to ensure energy output.Herein,a mesoporous free-standing A-COF nanofilm(pore size is 3.9 nm,averaged thickness is 4.1 nm)with imine bond linkage and a microporous B-COF nanofilm(pore size is 1.5 nm,averaged thickness is 9.3 nm)withβ-keto-enamine-linkages are prepared,and for the first time,we assembly the two lattice matching films into sandwich-type superlattices via layer-by-layer transfer,in which ABA–COF superlattice stacking into a“nano-hourglass”steric configuration that can accelerate the dynamic charge transportation/accumulation and promote the sufficient redox reactions to energy storage.The fabricated flexible MSC–ABA–COF exhibits the highest intrinsic CV of 927.9 F cm^(−3) at 10 mV s^(−1) than reported two-dimensional alloy,graphite-like carbon and undoped COF-based MSC devices so far,and shows a bending-resistant energy density of 63.2 mWh cm^(−3) even after high-angle and repeat arbitrary bending from 0 to 180°.This work provides a feasible way to meet the demand for future miniaturization and wearable electronics.展开更多
External short circuit(ESC)of lithium-ion batteries is one of the common and severe electrical failures in electric vehicles.In this study,a novel thermal modelis developed to capture the temperature behavior of batte...External short circuit(ESC)of lithium-ion batteries is one of the common and severe electrical failures in electric vehicles.In this study,a novel thermal modelis developed to capture the temperature behavior of batteries under ESC conditions.Experiments were systematically performed under different battery initial state of charge and ambient temperatures.Based on the experimental results,we employed an extreme learming machine(ELM)-based thermal(ELMT)model to depict battery temperature behavior under ESC,where a lumped-state thermal model was used to replace the activation function of conventional ELMs.To demonstrate the effectiveness of the proposed model,wecompared the ELMT model with a multi-lumped-state thermal(MLT)model parameterized by thegenetic algorithm using the experimental data from various sets of battery cells.It is shown that the ELMT model can achieve higher computa-tional efficiency than the MLT model and better fitting and prediction accuracy,where the average root mean squared error(RMSE)of the fitting is 0.65℃ for the ELMT model and 3.95℃ for the MLT model,and the RMES of the prediction under new data set is 3.97℃ for the ELMT model and 6.11℃ for the MLT model.展开更多
The current research of state of charge(SoC) online estimation of lithium-ion battery(LiB) in electric vehicles(EVs)mainly focuses on adopting or improving of battery models and estimation filters. However, little att...The current research of state of charge(SoC) online estimation of lithium-ion battery(LiB) in electric vehicles(EVs)mainly focuses on adopting or improving of battery models and estimation filters. However, little attention has been paid to the accuracy of various open circuit voltage(OCV) models for correcting the SoC with aid of the ampere-hour counting method. This paper presents a comprehensive comparison study on eighteen OCV models which cover the majority of models used in literature. The low-current OCV tests are conducted on the typical commercial LiFePO/graphite(LFP) and LiNiMnCoO/graphite(NMC) cells to obtain the experimental OCV-SoC curves at different ambient temperature and aging stages. With selected OCV and SoC points from experimental OCV-SoC curves, the parameters of each OCV model are determined by curve fitting toolbox of MATLAB 2013. Then the fitting OCV-SoC curves based on diversified OCV models are also obtained. The indicator of root-mean-square error(RMSE) between the experimental data and fitted data is selected to evaluate the adaptabilities of these OCV models for their main features, advantages,and limitations. The sensitivities of OCV models to ambient temperatures, aging stages, numbers of data points,and SoC regions are studied for both NMC and LFP cells. Furthermore, the influences of these models on SoC estimation are discussed. Through a comprehensive comparison and analysis on OCV models, some recommendations in selecting OCV models for both NMC and LFP cells are given.展开更多
Lithium-ion batteries have always been a focus of research on new energy vehicles,however,their internal reactions are complex,and problems such as battery aging and safety have not been fully understood.In view of th...Lithium-ion batteries have always been a focus of research on new energy vehicles,however,their internal reactions are complex,and problems such as battery aging and safety have not been fully understood.In view of the research and preliminary application of the digital twin in complex systems such as aerospace,we will have the opportunity to use the digital twin to solve the bottleneck of current battery research.Firstly,this paper arranges the development history,basic concepts and key technologies of the digital twin,and summarizes current research methods and challenges in battery modeling,state estimation,remaining useful life prediction,battery safety and control.Furthermore,based on digital twin we describe the solutions for battery digital modeling,real-time state estimation,dynamic charging control,dynamic thermal management,and dynamic equalization control in the intelligent battery management system.We also give development opportunities for digital twin in the battery field.Finally we summarize the development trends and challenges of smart battery management.展开更多
State of charge(SOC)estimation for lithium ion batteries plays a critical role in battery management systems for electric vehicles.Battery fractional order models(FOMs)which come from frequency-domain modelling have p...State of charge(SOC)estimation for lithium ion batteries plays a critical role in battery management systems for electric vehicles.Battery fractional order models(FOMs)which come from frequency-domain modelling have provided a distinct insight into SOC estimation.In this article,we compare five state-of-the-art FOMs in terms of SOC estimation.To this end,firstly,characterisation tests on lithium ion batteries are conducted,and the experimental results are used to identify FOM parameters.Parameter identification results show that increasing the complexity of FOMs cannot always improve accuracy.The model R(RQ)W shows superior identification accuracy than the other four FOMs.Secondly,the SOC estimation based on a fractional order unscented Kalman filter is conducted to compare model accuracy and computational burden under different profiles,memory lengths,ambient temperatures,cells and voltage/current drifts.The evaluation results reveal that the SOC estimation accuracy does not necessarily positively correlate to the complexity of FOMs.Although more complex models can have better robustness against temperature variation,R(RQ),the simplest FOM,can overall provide satisfactory accuracy.Validation results on different cells demonstrate the generalisation ability of FOMs,and R(RQ)outperforms other models.Moreover,R(RQ)shows better robustness against truncation error and can maintain high accuracy even under the occurrence of current or voltage sensor drift.展开更多
Lithium-ion batteries(LIBs)have emerged as the preferred energy storage systems for various types of electric transports,including electric vehicles,electric boats,electric trains,and electric airplanes.The energy man...Lithium-ion batteries(LIBs)have emerged as the preferred energy storage systems for various types of electric transports,including electric vehicles,electric boats,electric trains,and electric airplanes.The energy management of LIBs in electric transports for all-climate and long-life operation requires the accurate estimation of state of charge(SOC)and capacity in real-time.This study proposes a multistage model fusion algorithm to co-estimate SOC and capacity.Firstly,based on the assumption of a normal distribution,the mean and variance of the residual error from the model at different ageing levels are used to calculate the weight for the establishment of a fusion model with stable parameters.Secondly,a differential error gain with forward-looking ability is introduced into a proportional–integral observer(PIO)to accelerate convergence speed.Thirdly,a fusion algorithm is developed by combining a multistage model and proportional–integral–differential observer(PIDO)to co-estimate SOC and capacity under a complex application environment.Fourthly,the convergence and anti-noise performance of the fusion algorithm are discussed.Finally,the hardware-in-the-loop platform is set up to verify the performance of the fusion algorithm.The validation results of different aged LIBs over a wide range of temperature show that the presented fusion algorithm can realize a high-accuracy estimation of SOC and capacity with the relative errors within 2%and 3.3%,respectively.展开更多
The vertical GaN-on-GaN Schottky barrier diode with boron-implanted termination was fabricated and characterized.Compared with the Schottky barrier diode(SBD)without boron-implanted termination,this SBD effectively im...The vertical GaN-on-GaN Schottky barrier diode with boron-implanted termination was fabricated and characterized.Compared with the Schottky barrier diode(SBD)without boron-implanted termination,this SBD effectively improved the breakdown voltage from 189 V to 585 V and significantly reduced the reverse leakage current by 10^5 times.In addition,a high Ion/Ioff ratio of ~10^8 was achieved by the boron-implanted technology.We used Technology Computer Aided Design(TCAD)to analyze reasons for the improved performance of the SBD with boron-implanted termination.The improved performance of diodes may be attributed to that B+could confine free carriers to suppress electron field crowding at the edge of the diode,which could improve the breakdown voltage and suppress the reverse leakage current.展开更多
Nearly single-phase and polycrystalline charge-density-wave compound K0.3MoO3 have been prepared by using a simple method. In this work, K2CO3 and MoOs were used as starting materials and reacted by hot isostatic pres...Nearly single-phase and polycrystalline charge-density-wave compound K0.3MoO3 have been prepared by using a simple method. In this work, K2CO3 and MoOs were used as starting materials and reacted by hot isostatic pressing （HIP） sintering. The product is nearly single phase K0.3MoO3 determined by X-ray powder diffraction （XRD） and energy dispersive spectroscopy （EDS）. Measurement of temperature dependence of resistivity reveals that the transport property of polycrystalline K0.3MoO3 obviously differs from that of single crystal due to the grain boundaries and the anisotropic structure in this kind of compound.展开更多
The precipitation of TiN inclusion during solidification of different carbon content of 0.72%, 0.82% and 0.95% in tire cord steel is thermodynamically studied respectively. The results show that the carbon content has...The precipitation of TiN inclusion during solidification of different carbon content of 0.72%, 0.82% and 0.95% in tire cord steel is thermodynamically studied respectively. The results show that the carbon content has obvious effect on TiN inclusion precipitated in tire cord steel of different strength levels. With the carbon content of tire cord steel increasing, the temperature before solidifying reduced gradually and the required activity product of titanium and nitrogen for TiN inclusion precipitation also declined gradually. With the same condition of initial Ti and N content in liquid steel, the size of TiN inclusion precipitated in tire cord steel of higher carbon content is bigger than that of lower carbon content. In order to control the harmful effects on processability of TiN inclusion precipitated in hypereutectoid tire cord steel of the ultra high strength level, the measures of smelting process must be taken to further reduce the titanium and nitrogen content in liquid steel.展开更多
TixAl1-xN films have been prepared by RF reactive magnetron sputtering. X-ray diffraction results showed that TixAl1-xN thin films in this study were hexagonal wurtzite structure with the Ti content up to 0.18. X-ray ...TixAl1-xN films have been prepared by RF reactive magnetron sputtering. X-ray diffraction results showed that TixAl1-xN thin films in this study were hexagonal wurtzite structure with the Ti content up to 0.18. X-ray photoelectron spectrocopy studies provided that the Nls core-electron spectrum of TixAl1-xN thin film brodend with increasing Ti content, and the difference of the chemical shifts for Ti2p3/2 line between TiN and TixAl1-xN th77pj in film was 0.7 eV.展开更多
BACKGROUND Sarcopenia is an age-related decline in skeletal muscle mass,which depends on an assessment of muscle strength and muscle mass.It has been reported that the prevalence of sarcopenia in non-hospitalized elde...BACKGROUND Sarcopenia is an age-related decline in skeletal muscle mass,which depends on an assessment of muscle strength and muscle mass.It has been reported that the prevalence of sarcopenia in non-hospitalized elderly people was 9.0%-18.5%in the lowland plains.However,epidemiological investigations of sarcopenia in plateau regions are limited.The city of Xining in Qinghai Province(altitude 2260 m)is the sole point of access to the Qinghai-Tibet plateau.We hypothesized that the diverse ethnicities or dietary habits of the people living in the plateau may influence the prevalence of sarcopenia.AIM To investigate the prevalence and risk factors of sarcopenia in geriatric patients from the Qinghai-Tibet plateau region.METHODS From October to December 2018,150 hospitalized geriatric patients(72.4±5.60 years)from Xining City(altitude 2260 m)in Qinghai Province were recruited.Collected data included demographics,history of fall,nutritional status,self-care ability,depression,handgrip,muscle mass,and 6-m gait speed.Sarcopenia was diagnosed based on the 2014 criteria of the Asian Working Group for Sarcopenia.RESULTS The overall rate of sarcopenia was 20%(8.7 and 11.3%in men and women,respectively).Binary logistic regression analysis indicated that widowhood and a history of falling were associated with sarcopenia,while higher body mass index and beef and mutton consumption were protective.CONCLUSION The prevalence of sarcopenia in hospitalized geriatric patients in the Qinghai-Tibet plateau region was higher than that in the plain region and in non-hospitalized geriatric people(reported elsewhere).Specific cultural features of the region,including ethnicity,brewed tea and ghee consumption,were not significantly associated with sarcopenia.Higher body mass index and consumption of beef and mutton were protective,while patients who were widowed or with a history of falling were at increased risk.展开更多
基金This work was supported by the National Key R&D Program of China(2021YFB2402002)the Beijing Natural Science Foundation(L223013)the Chongqing Automobile Collaborative Innovation Centre(No.2022CDJDX-004).
文摘Battery management systems(BMSs) play a vital role in ensuring efficient and reliable operations of lithium-ion batteries.The main function of the BMSs is to estimate battery states and diagnose battery health using battery open-circuit voltage(OCV).However,acquiring the complete OCV data online can be a challenging endeavor due to the time-consuming measurement process or the need for specific operating conditions required by OCV estimation models.In addressing these concerns,this study introduces a deep neural network-combined framework for accurate and robust OCV estimation,utilizing partial daily charging data.We incorporate a generative deep learning model to extract aging-related features from data and generate high-fidelity OCV curves.Correlation analysis is employed to identify the optimal partial charging data,optimizing the OCV estimation precision while preserving exceptional flexibility.The validation results,using data from nickel-cobalt-magnesium(NCM) batteries,illustrate the accurate estimation of the complete OCV-capacity curve,with an average root mean square errors(RMSE) of less than 3 mAh.Achieving this level of precision for OCV estimation requires only around 50 s collection of partial charging data.Further validations on diverse battery types operating under various conditions confirm the effectiveness of our proposed method.Additional cases of precise health diagnosis based on OCV highlight the significance of conducting online OCV estimation.Our method provides a flexible approach to achieve complete OCV estimation and holds promise for generalization to other tasks in BMSs.
文摘Solid non-conjugated polymers have long been regarded as insulators due to deficiency of delocalizedπelectrons along the molecular chain framework.Up to date,origin of insulating polymer regulated charge transfer has not yet been uncovered.In this work,we unleash the root origin of charge transport capability of insulating polymer in photocatalysis.We ascertain that insulating polymer plays crucial roles in fine tuning of electronic structure of transition metal chalcogenides(TMCs),which mainly include altering surface electron density of TMCs for accelerating charge transport kinetics,triggering the generation of defect over TMCs for prolonging carrier lifetime,and acting as hole-trapping mediator for retarding charge recombination.These synergistic roles contribute to the charge transfer of insulating polymer.Our work opens a new vista of utilizing solid insulating polymers for maneuvering charge transfer toward solar energy conversion.
基金Supported by National Key R&D Program of China(Grant No.2021YFB2402002)Beijing Municipal Natural Science Foundation of China(Grant No.L223013).
文摘Battery remaining charging time(RCT)prediction can facilitate charging management and alleviate mileage anxiety for electric vehicles(EVs).Also,it is of great significance to improve EV users’experience.However,the RCT for a lithiumion battery pack in EVs changes with temperature and other battery parameters.This study proposes an electrothermal model-based method to accurately predict battery RCT.Firstly,a characteristic battery cell is adopted to represent the battery pack,thus an equivalent circuit model(ECM)of the characteristic battery cell is established to describe the electrical behaviors of a battery pack.Secondly,an equivalent thermal model(ETM)of the battery pack is developed by considering the influence of ambient temperature,thermal management,and battery connectors in the battery pack to calculate the temperature which is then fed back to the ECM to realize electrothermal coupling.Finally,the RCT prediction method is proposed based on the electrothermal model and validated in the wide temperature range from-20℃to 45℃.The experimental results show that the prediction error of the RCT in the whole temperature range is less than 1.5%.
基金Project supported by the National Key Research and Development Program of China(Grant No.2023YFF1204402)the National Natural Science Foundation of China(Grant Nos.12074079 and 12374208)+1 种基金the Natural Science Foundation of Shanghai(Grant No.22ZR1406800)the China Postdoctoral Science Foundation(Grant No.2022M720815).
文摘The rapid advancement and broad application of machine learning(ML)have driven a groundbreaking revolution in computational biology.One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improve the sampling efficiency of the vast conformational space of large biomolecules.This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape.We first highlight the recent development of ML-aided enhanced sampling methods,including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential,or facilitate the exploration of the unsampled region of the energy landscape.Further,we review the development of autoencoder based methods that combine molecular simulations and deep learning to expand the search for protein conformations.Lastly,we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights.Collectively,this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.
基金supported by the National Key R&D Program of China(2021YFB2402002)the National Natural Science Foundation of China(51922006 and 51877009)+1 种基金the China Postdoctoral Science Foundation(BX2021035 and 2022M710379)the Beijing Natural Science Foundation(Grant No.L223013)。
文摘Machine learning-based methods have emerged as a promising solution to accurate battery capacity estimation for battery management systems.However,they are generally developed in a supervised manner which requires a considerable number of input features and corresponding capacities,leading to prohibitive costs and efforts for data collection.In response to this issue,this study proposes a convolutional neural network(CNN)based method to perform end-to-end capacity estimation by taking only raw impedance spectra as input.More importantly,an input reconstruction module is devised to effectively exploit impedance spectra without corresponding capacities in the training process,thereby significantly alleviating the cost of collecting training data.Two large battery degradation datasets encompassing over 4700 impedance spectra are developed to validate the proposed method.The results show that accurate capacity estimation can be achieved when substantial training samples with measured capacities are given.However,the estimation performance of supervised machine learning algorithms sharply deteriorates when fewer samples with measured capacities are available.In this case,the proposed method outperforms supervised benchmarks and can reduce the root mean square error by up to 50.66%.A further validation under different current rates and states of charge confirms the effectiveness of the proposed method.Our method provides a flexible approach to take advantage of unlabelled samples for developing data-driven models and is promising to be generalised to other battery management tasks.
基金Supported by National Key R&D Program of China (Grant No.2021YFB2402002)Beijing Natural Science Foundation of China (Grant No.L223013)。
文摘The development of a battery management algorithm is highly dependent on high-quality battery operation data,especially the data in extreme conditions such as low temperatures.The data in faults are also essential for failure and safety management research.This study developed a battery big data platform to realize vehicle operation,energy interaction and data management.First,we developed an electric vehicle with vehicle navigation and position detection and designed an environmental cabin that allows the vehicle to operate autonomously.Second,charging and heating systems based on wireless energy transfer were developed and equipped on the vehicle to investigate optimal charging and heating methods of the batteries in the vehicle.Third,the data transmission network was designed,a real-time monitoring interface was developed,and the self-developed battery management system was used to measure,collect,upload,and store battery operation data in real time.Finally,experimental validation was performed on the platform.Results demonstrate the efficiency and reliability of the platform.Battery state of charge estimation is used as an example to illustrate the availability of battery operation data.
基金support of the project from the National Natural Science Foundation of China(Grant No.91963207,12122408,12074292)National Key R&D Program of China(Grant No.2021YFA0718700)Suzhou Key Industrial Technology Innovation project(Grant No.SYG201921).
文摘SnSe has attracted extensive attention due to its ultralow thermal conductivity and excellent thermoelectric properties.In this work,pressure-induced thermoelectric properties of Pnma SnSe are investigated via first-principles calculations.We uncover distinct energy isosurfaces topology transition of conduction band by applying pressure.The newly created conduction band valley caused by pressure has a distinct anisotropic shape compared to the old one.Inducing pressure can greatly enhance the anisotropy of electronic transport properties of the n-type Pnma SnSe.Furthermore,the lattice thermal conductivity also exhibits anisotropic behavior under pressure due to a special collaged phonon mode.The pressure-induced lattice thermal conductivity along the a-axis shows a slower growth trend than that along the b-axis and c-axis.The optimal ZT value of the n-type Pnma SnSe along the a-axis can reach 1.64 at room temperature.These results would be helpful for designing the Pnma SnSe-based materials for the potential thermoelectric and valleytronic applications.
基金the National Natural Science Foundation of China(No.22105058,52272163)Hebei(China)Natural Science Foundation(Grant No.B2021208014,B2021208073)+1 种基金Key R&D Program of Hebei(Grant No.20311501D,216Z1201G)Key Research and Development Program of Shaanxi Province(2021GY-217).
文摘Covalent organic framework(COF)film with highly exposed active sites is considered as the promising flexible selfsupported electrode for in-plane microsupercapacitor(MSC).Superlattice configuration assembled alternately by different nanofilms based on van der Waals force can integrate the advantages of each isolated layer to exhibit unexpected performances as MSC film electrodes,which may be a novel option to ensure energy output.Herein,a mesoporous free-standing A-COF nanofilm(pore size is 3.9 nm,averaged thickness is 4.1 nm)with imine bond linkage and a microporous B-COF nanofilm(pore size is 1.5 nm,averaged thickness is 9.3 nm)withβ-keto-enamine-linkages are prepared,and for the first time,we assembly the two lattice matching films into sandwich-type superlattices via layer-by-layer transfer,in which ABA–COF superlattice stacking into a“nano-hourglass”steric configuration that can accelerate the dynamic charge transportation/accumulation and promote the sufficient redox reactions to energy storage.The fabricated flexible MSC–ABA–COF exhibits the highest intrinsic CV of 927.9 F cm^(−3) at 10 mV s^(−1) than reported two-dimensional alloy,graphite-like carbon and undoped COF-based MSC devices so far,and shows a bending-resistant energy density of 63.2 mWh cm^(−3) even after high-angle and repeat arbitrary bending from 0 to 180°.This work provides a feasible way to meet the demand for future miniaturization and wearable electronics.
基金support by the National Key Researchand Development Program of China(2018YFBO104100).
文摘External short circuit(ESC)of lithium-ion batteries is one of the common and severe electrical failures in electric vehicles.In this study,a novel thermal modelis developed to capture the temperature behavior of batteries under ESC conditions.Experiments were systematically performed under different battery initial state of charge and ambient temperatures.Based on the experimental results,we employed an extreme learming machine(ELM)-based thermal(ELMT)model to depict battery temperature behavior under ESC,where a lumped-state thermal model was used to replace the activation function of conventional ELMs.To demonstrate the effectiveness of the proposed model,wecompared the ELMT model with a multi-lumped-state thermal(MLT)model parameterized by thegenetic algorithm using the experimental data from various sets of battery cells.It is shown that the ELMT model can achieve higher computa-tional efficiency than the MLT model and better fitting and prediction accuracy,where the average root mean squared error(RMSE)of the fitting is 0.65℃ for the ELMT model and 3.95℃ for the MLT model,and the RMES of the prediction under new data set is 3.97℃ for the ELMT model and 6.11℃ for the MLT model.
基金Supported by National Natural Science Foundation of China(Grant No.51507012)Beijing Municipal Natural Science Foundation of China(Grant No.3182035)
文摘The current research of state of charge(SoC) online estimation of lithium-ion battery(LiB) in electric vehicles(EVs)mainly focuses on adopting or improving of battery models and estimation filters. However, little attention has been paid to the accuracy of various open circuit voltage(OCV) models for correcting the SoC with aid of the ampere-hour counting method. This paper presents a comprehensive comparison study on eighteen OCV models which cover the majority of models used in literature. The low-current OCV tests are conducted on the typical commercial LiFePO/graphite(LFP) and LiNiMnCoO/graphite(NMC) cells to obtain the experimental OCV-SoC curves at different ambient temperature and aging stages. With selected OCV and SoC points from experimental OCV-SoC curves, the parameters of each OCV model are determined by curve fitting toolbox of MATLAB 2013. Then the fitting OCV-SoC curves based on diversified OCV models are also obtained. The indicator of root-mean-square error(RMSE) between the experimental data and fitted data is selected to evaluate the adaptabilities of these OCV models for their main features, advantages,and limitations. The sensitivities of OCV models to ambient temperatures, aging stages, numbers of data points,and SoC regions are studied for both NMC and LFP cells. Furthermore, the influences of these models on SoC estimation are discussed. Through a comprehensive comparison and analysis on OCV models, some recommendations in selecting OCV models for both NMC and LFP cells are given.
基金Supported by National Natural Science Foundation of China(Grant No.51922006).
文摘Lithium-ion batteries have always been a focus of research on new energy vehicles,however,their internal reactions are complex,and problems such as battery aging and safety have not been fully understood.In view of the research and preliminary application of the digital twin in complex systems such as aerospace,we will have the opportunity to use the digital twin to solve the bottleneck of current battery research.Firstly,this paper arranges the development history,basic concepts and key technologies of the digital twin,and summarizes current research methods and challenges in battery modeling,state estimation,remaining useful life prediction,battery safety and control.Furthermore,based on digital twin we describe the solutions for battery digital modeling,real-time state estimation,dynamic charging control,dynamic thermal management,and dynamic equalization control in the intelligent battery management system.We also give development opportunities for digital twin in the battery field.Finally we summarize the development trends and challenges of smart battery management.
基金Beijing Municipal Natural Science Foundation of China(Grant No.3182035)National Natural Science Foundation of China(Grant No.51877009).
文摘State of charge(SOC)estimation for lithium ion batteries plays a critical role in battery management systems for electric vehicles.Battery fractional order models(FOMs)which come from frequency-domain modelling have provided a distinct insight into SOC estimation.In this article,we compare five state-of-the-art FOMs in terms of SOC estimation.To this end,firstly,characterisation tests on lithium ion batteries are conducted,and the experimental results are used to identify FOM parameters.Parameter identification results show that increasing the complexity of FOMs cannot always improve accuracy.The model R(RQ)W shows superior identification accuracy than the other four FOMs.Secondly,the SOC estimation based on a fractional order unscented Kalman filter is conducted to compare model accuracy and computational burden under different profiles,memory lengths,ambient temperatures,cells and voltage/current drifts.The evaluation results reveal that the SOC estimation accuracy does not necessarily positively correlate to the complexity of FOMs.Although more complex models can have better robustness against temperature variation,R(RQ),the simplest FOM,can overall provide satisfactory accuracy.Validation results on different cells demonstrate the generalisation ability of FOMs,and R(RQ)outperforms other models.Moreover,R(RQ)shows better robustness against truncation error and can maintain high accuracy even under the occurrence of current or voltage sensor drift.
基金This work was supported by the National Key Research and Development Program of China(2017YFB0103802)the National Natural Science Foundation of China(51922006 and 51707011).
文摘Lithium-ion batteries(LIBs)have emerged as the preferred energy storage systems for various types of electric transports,including electric vehicles,electric boats,electric trains,and electric airplanes.The energy management of LIBs in electric transports for all-climate and long-life operation requires the accurate estimation of state of charge(SOC)and capacity in real-time.This study proposes a multistage model fusion algorithm to co-estimate SOC and capacity.Firstly,based on the assumption of a normal distribution,the mean and variance of the residual error from the model at different ageing levels are used to calculate the weight for the establishment of a fusion model with stable parameters.Secondly,a differential error gain with forward-looking ability is introduced into a proportional–integral observer(PIO)to accelerate convergence speed.Thirdly,a fusion algorithm is developed by combining a multistage model and proportional–integral–differential observer(PIDO)to co-estimate SOC and capacity under a complex application environment.Fourthly,the convergence and anti-noise performance of the fusion algorithm are discussed.Finally,the hardware-in-the-loop platform is set up to verify the performance of the fusion algorithm.The validation results of different aged LIBs over a wide range of temperature show that the presented fusion algorithm can realize a high-accuracy estimation of SOC and capacity with the relative errors within 2%and 3.3%,respectively.
基金Project supported by the National Key R&D Program of China(Grant No.2017YFB0404100)Science and Technology Planning Project of Guangdong Province,China(Grant No.2017B010112001)。
文摘The vertical GaN-on-GaN Schottky barrier diode with boron-implanted termination was fabricated and characterized.Compared with the Schottky barrier diode(SBD)without boron-implanted termination,this SBD effectively improved the breakdown voltage from 189 V to 585 V and significantly reduced the reverse leakage current by 10^5 times.In addition,a high Ion/Ioff ratio of ~10^8 was achieved by the boron-implanted technology.We used Technology Computer Aided Design(TCAD)to analyze reasons for the improved performance of the SBD with boron-implanted termination.The improved performance of diodes may be attributed to that B+could confine free carriers to suppress electron field crowding at the edge of the diode,which could improve the breakdown voltage and suppress the reverse leakage current.
基金the National Natural Science Foundation of China （No. 10474074） the StateKey Laboratory of Advanced Technology for Materials Synthesis and Processing （Wuhan University of Technology, WUT 2004 M03）.
文摘Nearly single-phase and polycrystalline charge-density-wave compound K0.3MoO3 have been prepared by using a simple method. In this work, K2CO3 and MoOs were used as starting materials and reacted by hot isostatic pressing （HIP） sintering. The product is nearly single phase K0.3MoO3 determined by X-ray powder diffraction （XRD） and energy dispersive spectroscopy （EDS）. Measurement of temperature dependence of resistivity reveals that the transport property of polycrystalline K0.3MoO3 obviously differs from that of single crystal due to the grain boundaries and the anisotropic structure in this kind of compound.
文摘The precipitation of TiN inclusion during solidification of different carbon content of 0.72%, 0.82% and 0.95% in tire cord steel is thermodynamically studied respectively. The results show that the carbon content has obvious effect on TiN inclusion precipitated in tire cord steel of different strength levels. With the carbon content of tire cord steel increasing, the temperature before solidifying reduced gradually and the required activity product of titanium and nitrogen for TiN inclusion precipitation also declined gradually. With the same condition of initial Ti and N content in liquid steel, the size of TiN inclusion precipitated in tire cord steel of higher carbon content is bigger than that of lower carbon content. In order to control the harmful effects on processability of TiN inclusion precipitated in hypereutectoid tire cord steel of the ultra high strength level, the measures of smelting process must be taken to further reduce the titanium and nitrogen content in liquid steel.
基金This work was supported by the National Natural Science Foundation of China under grant No.10474074the Hubei Natural Science Foundation under grant No.2001ABB060.
文摘TixAl1-xN films have been prepared by RF reactive magnetron sputtering. X-ray diffraction results showed that TixAl1-xN thin films in this study were hexagonal wurtzite structure with the Ti content up to 0.18. X-ray photoelectron spectrocopy studies provided that the Nls core-electron spectrum of TixAl1-xN thin film brodend with increasing Ti content, and the difference of the chemical shifts for Ti2p3/2 line between TiN and TixAl1-xN th77pj in film was 0.7 eV.
基金Chinese Academy of Medical Sciences,Peking Union Medical College Hospital,No.2018PT33001.
文摘BACKGROUND Sarcopenia is an age-related decline in skeletal muscle mass,which depends on an assessment of muscle strength and muscle mass.It has been reported that the prevalence of sarcopenia in non-hospitalized elderly people was 9.0%-18.5%in the lowland plains.However,epidemiological investigations of sarcopenia in plateau regions are limited.The city of Xining in Qinghai Province(altitude 2260 m)is the sole point of access to the Qinghai-Tibet plateau.We hypothesized that the diverse ethnicities or dietary habits of the people living in the plateau may influence the prevalence of sarcopenia.AIM To investigate the prevalence and risk factors of sarcopenia in geriatric patients from the Qinghai-Tibet plateau region.METHODS From October to December 2018,150 hospitalized geriatric patients(72.4±5.60 years)from Xining City(altitude 2260 m)in Qinghai Province were recruited.Collected data included demographics,history of fall,nutritional status,self-care ability,depression,handgrip,muscle mass,and 6-m gait speed.Sarcopenia was diagnosed based on the 2014 criteria of the Asian Working Group for Sarcopenia.RESULTS The overall rate of sarcopenia was 20%(8.7 and 11.3%in men and women,respectively).Binary logistic regression analysis indicated that widowhood and a history of falling were associated with sarcopenia,while higher body mass index and beef and mutton consumption were protective.CONCLUSION The prevalence of sarcopenia in hospitalized geriatric patients in the Qinghai-Tibet plateau region was higher than that in the plain region and in non-hospitalized geriatric people(reported elsewhere).Specific cultural features of the region,including ethnicity,brewed tea and ghee consumption,were not significantly associated with sarcopenia.Higher body mass index and consumption of beef and mutton were protective,while patients who were widowed or with a history of falling were at increased risk.
