For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models...For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models and physical models,each offering unique advantages but also facing limitations.Physics-informed neural networks(PINNs)provide a robust framework to integrate data-driven models with physical principles,ensuring consistency with underlying physics while enabling generalization across diverse operational conditions.This study introduces a PINN-based approach to reconstruct open circuit voltage(OCV)curves and estimate key ageing parameters at both the cell and electrode levels.These parameters include available capacity,electrode capacities,and lithium inventory capacity.The proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs)and is validated using a public dataset.The results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests,with errors in reconstructed OCV curves remaining within 15 mV.This demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level,advancing the potential for precise and efficient battery health management.展开更多
Background:Episodic memory loss is a prominent clinical manifestation of Alzheimer’s disease(AD),which is closely related to tau pathology and hippocampal impairment.Due to the heterogeneity of brain neurons,the spec...Background:Episodic memory loss is a prominent clinical manifestation of Alzheimer’s disease(AD),which is closely related to tau pathology and hippocampal impairment.Due to the heterogeneity of brain neurons,the specific roles of different brain neurons in terms of their sensitivity to tau accumulation and their contribution to AD-like social memory loss remain unclear.Therefore,further investigation is necessary.Methods:We investigated the effects of AD-like tau pathology by Tandem mass tag proteomic and phosphoproteomic analysis,social behavioural tests,hippocampal electrophysiology,immunofluorescence staining and in vivo optical fibre recording of GCaMP6f and iGABASnFR.Additionally,we utilized optogenetics and administered ursolic acid(UA)via oral gavage to examine the effects of these agents on social memory in mice.Results:The results of proteomic and phosphoproteomic analyses revealed the characteristics of ventral hippocampal CA1(vCA1)under both physiological conditions and AD-like tau pathology.As tau progressively accumulated,vCA1,especially its excitatory and parvalbumin(PV)neurons,were fully filled with mislocated and phosphorylated tau(p-Tau).This finding was not observed for dorsal hippocampal CA1(dCA1).The overexpression of human tau(hTau)in excitatory and PV neurons mimicked AD-like tau accumulation,significantly inhibited neuronal excitability and suppressed distinct discrimination-associated firings of these neurons within vCA1.Photoactivating excitatory and PV neurons in vCA1 at specific rhythms and time windows efficiently ameliorated tau-impaired social memory.Notably,1 month of UA administration efficiently decreased tau accumulation via autophagy in a transcription factor EB(TFEB)-dependent manner and restored the vCA1 microcircuit to ameliorate tau-impaired social memory.Conclusion:This study elucidated distinct protein and phosphoprotein networks between dCA1 and vCA1 and highlighted the susceptibility of the vCA1 microcircuit to AD-like tau accumulation.Notably,our novel findings regarding the efficacy of UA in reducing tau load and targeting the vCA1 microcircuit may provide a promising strategy for treating AD in the future.展开更多
Lithium plating is a detrimental phenomenon in lithium-ion cells that compromises both functionality and safety.This study investigates electro-chemo-mechanical behaviors of lithium plating in lithium iron phosphate p...Lithium plating is a detrimental phenomenon in lithium-ion cells that compromises both functionality and safety.This study investigates electro-chemo-mechanical behaviors of lithium plating in lithium iron phosphate pouch cells under different external pressures.Atomic force microscopy nanoindentation is performed on the graphite electrode to analyze the influence of external pressure on solid-electrolyte interphase(SEI),revealing that the mechanical strength of SEI,indicated by Young's modulus,increases with the presence of external pressure.Then,an improved phase field model for lithium plating is developed by incorporating electrochemical parameterization based on nonequilibrium thermodynamics.The results demonstrate that higher pressure promotes lateral lithium deposition,covering a larger area of SEI.Moreover,electrochemical impedance spectroscopy and thickness measurements of the pouch cells are conducted during overcharge,showing that external pressure suppresses gas generation and thus increases the proportion of lithium deposition among galvanostatic overcharge reactions.By integrating experimental results with numerical simulations,it is demonstrated that moderate pressure mitigates SEI damage during lithium plating,while both insufficient and excessive pressure may exacerbate it.This study offers new insights into optimizing the design and operation of lithium iron phosphate pouch cells under external pressures.展开更多
Electrochemical impedance spectroscopy(EIS)offers valuable insights into the dynamic behaviors of lithium-ion batteries,making it a powerful and non-invasive tool for evaluating battery health.However,EIS falls short ...Electrochemical impedance spectroscopy(EIS)offers valuable insights into the dynamic behaviors of lithium-ion batteries,making it a powerful and non-invasive tool for evaluating battery health.However,EIS falls short in quantitatively determining the degree of specific degradation modes,which are essential for improving battery lifespan.This study introduces a novel approach employing deep neural networks enhanced by an attention mechanism to identify the degree of degradation modes.The proposed method can automatically determine the most relevant frequency ranges for each degradation mode,which can link impedance characteristics to battery degradation.To overcome the limitation of scarce labeled experimental data,simulation results derived from mechanistic models are incorporated into the model.Validation results demonstrate that the proposed method could achieve root mean square errors below 3%for estimating loss of lithium inventory and loss of active material of the positive electrode,and below 4%for estimating loss of active material of the negative electrode while requiring only 25%of early-stage experimental degradation data.By integrating simulation results,the proposed method achieves a reduction in maximum estimation error ranging from 42.92%to 66.30%across different temperatures and operating conditions compared to the baseline model trained solely on experimental data.展开更多
Pyroelectric sensors based on pyroelectric effect have a wide range of applications.However,the use of lead-containing materials limits their development.In this paper,Na_(0.5)Bi_(0.5)TiO_(3)-Na_(0.5)Bi_(4.5)TiO_(15)-...Pyroelectric sensors based on pyroelectric effect have a wide range of applications.However,the use of lead-containing materials limits their development.In this paper,Na_(0.5)Bi_(0.5)TiO_(3)-Na_(0.5)Bi_(4.5)TiO_(15)-Mn lead-free pyroelectric ceramics are used as sensitive materials to prepare pyroelectric sensors.Na_(0.5)Bi_(0.5)TiO_(3)-Na_(0.5)Bi_(4.5)TiO_(15)-Mn ceramics can achieve 7.58×10^(-4)C·m^(-2)·K^(-1)high-roomtemperature pyroelectric coefficient and depolarization temperature of 151℃.Due to the low dielectric constant and loss caused by Mn doping,the high detection rate value of 24.382μPa^(-1/2)is obtained.The voltage response rate and specific detection rate of the sensor prepared on this basis can attain the JC-T 2397-2017(ε_(r)>200,tanδ<5%,T_(c)>200,p>3.50×10^(-4)C·m^(-2)·K^(-1))application standard of pyroelectric infrared detectors.Thermoelectric cooler is proposed to adjust the temperature of the sensor,and its voltage response to human radiation is measured.Harnessing the superior pyroelectric attributes of advanced materials and connectable devices,the nascentthermoelectric-pyroelectric detection method is poised to be a subject of intensive investigation and development.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
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
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
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
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
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
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
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
To obtain a high-performance heterogeneous photo-catalyst, herein, the hetero-structured Zn In_(2)S_(4)-Ni O@MOF(ZNM) nano-sheets are designed and prepared by partial pyrolysis of nickel-based MOFs(NiMOF) combined wit...To obtain a high-performance heterogeneous photo-catalyst, herein, the hetero-structured Zn In_(2)S_(4)-Ni O@MOF(ZNM) nano-sheets are designed and prepared by partial pyrolysis of nickel-based MOFs(NiMOF) combined with the low-temperature solvo-thermal method. The results indicate that the NiO nanoparticles, produced by partial pyrolysis of the Ni-MOF, have a high density of the surface active sites with limited aggregation, which act as a co-catalyst to capture photo-induced charge carriers. In addition, the morphology and structure of Ni-MOF nano-sheets were preserved in ZNM, which is beneficial to the reduction of the conduction barrier for the photo generated electron-hole pairs. With the synergetic advantages of co-catalyst and unique two-dimensional hetero-structure, ZNM nano-sheets exhibited significantly improved activity for photo-catalytic hydrogen production.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
基金supported by the Beijing Natural Science Foundation(Grant No.L223013)。
文摘For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models and physical models,each offering unique advantages but also facing limitations.Physics-informed neural networks(PINNs)provide a robust framework to integrate data-driven models with physical principles,ensuring consistency with underlying physics while enabling generalization across diverse operational conditions.This study introduces a PINN-based approach to reconstruct open circuit voltage(OCV)curves and estimate key ageing parameters at both the cell and electrode levels.These parameters include available capacity,electrode capacities,and lithium inventory capacity.The proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs)and is validated using a public dataset.The results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests,with errors in reconstructed OCV curves remaining within 15 mV.This demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level,advancing the potential for precise and efficient battery health management.
基金supported in part by the National Natural Science Foundation of China(91949205,82071219,82001134,31730035,81721005,and 82201584)the Hubei Provincial Key S&T Program(2018ACA142)the Guangdong Provincial Key S&T Program(2018B030336001).
文摘Background:Episodic memory loss is a prominent clinical manifestation of Alzheimer’s disease(AD),which is closely related to tau pathology and hippocampal impairment.Due to the heterogeneity of brain neurons,the specific roles of different brain neurons in terms of their sensitivity to tau accumulation and their contribution to AD-like social memory loss remain unclear.Therefore,further investigation is necessary.Methods:We investigated the effects of AD-like tau pathology by Tandem mass tag proteomic and phosphoproteomic analysis,social behavioural tests,hippocampal electrophysiology,immunofluorescence staining and in vivo optical fibre recording of GCaMP6f and iGABASnFR.Additionally,we utilized optogenetics and administered ursolic acid(UA)via oral gavage to examine the effects of these agents on social memory in mice.Results:The results of proteomic and phosphoproteomic analyses revealed the characteristics of ventral hippocampal CA1(vCA1)under both physiological conditions and AD-like tau pathology.As tau progressively accumulated,vCA1,especially its excitatory and parvalbumin(PV)neurons,were fully filled with mislocated and phosphorylated tau(p-Tau).This finding was not observed for dorsal hippocampal CA1(dCA1).The overexpression of human tau(hTau)in excitatory and PV neurons mimicked AD-like tau accumulation,significantly inhibited neuronal excitability and suppressed distinct discrimination-associated firings of these neurons within vCA1.Photoactivating excitatory and PV neurons in vCA1 at specific rhythms and time windows efficiently ameliorated tau-impaired social memory.Notably,1 month of UA administration efficiently decreased tau accumulation via autophagy in a transcription factor EB(TFEB)-dependent manner and restored the vCA1 microcircuit to ameliorate tau-impaired social memory.Conclusion:This study elucidated distinct protein and phosphoprotein networks between dCA1 and vCA1 and highlighted the susceptibility of the vCA1 microcircuit to AD-like tau accumulation.Notably,our novel findings regarding the efficacy of UA in reducing tau load and targeting the vCA1 microcircuit may provide a promising strategy for treating AD in the future.
基金supported by the National Key R&D Program of China(Grant No.2023YFB2503800)。
文摘Lithium plating is a detrimental phenomenon in lithium-ion cells that compromises both functionality and safety.This study investigates electro-chemo-mechanical behaviors of lithium plating in lithium iron phosphate pouch cells under different external pressures.Atomic force microscopy nanoindentation is performed on the graphite electrode to analyze the influence of external pressure on solid-electrolyte interphase(SEI),revealing that the mechanical strength of SEI,indicated by Young's modulus,increases with the presence of external pressure.Then,an improved phase field model for lithium plating is developed by incorporating electrochemical parameterization based on nonequilibrium thermodynamics.The results demonstrate that higher pressure promotes lateral lithium deposition,covering a larger area of SEI.Moreover,electrochemical impedance spectroscopy and thickness measurements of the pouch cells are conducted during overcharge,showing that external pressure suppresses gas generation and thus increases the proportion of lithium deposition among galvanostatic overcharge reactions.By integrating experimental results with numerical simulations,it is demonstrated that moderate pressure mitigates SEI damage during lithium plating,while both insufficient and excessive pressure may exacerbate it.This study offers new insights into optimizing the design and operation of lithium iron phosphate pouch cells under external pressures.
基金supported by the National Key R&D Program of China(2024YFB2505003).
文摘Electrochemical impedance spectroscopy(EIS)offers valuable insights into the dynamic behaviors of lithium-ion batteries,making it a powerful and non-invasive tool for evaluating battery health.However,EIS falls short in quantitatively determining the degree of specific degradation modes,which are essential for improving battery lifespan.This study introduces a novel approach employing deep neural networks enhanced by an attention mechanism to identify the degree of degradation modes.The proposed method can automatically determine the most relevant frequency ranges for each degradation mode,which can link impedance characteristics to battery degradation.To overcome the limitation of scarce labeled experimental data,simulation results derived from mechanistic models are incorporated into the model.Validation results demonstrate that the proposed method could achieve root mean square errors below 3%for estimating loss of lithium inventory and loss of active material of the positive electrode,and below 4%for estimating loss of active material of the negative electrode while requiring only 25%of early-stage experimental degradation data.By integrating simulation results,the proposed method achieves a reduction in maximum estimation error ranging from 42.92%to 66.30%across different temperatures and operating conditions compared to the baseline model trained solely on experimental data.
基金financially supported by the National Key Research and Development Program of China(No.2023YFB4603800)
文摘Pyroelectric sensors based on pyroelectric effect have a wide range of applications.However,the use of lead-containing materials limits their development.In this paper,Na_(0.5)Bi_(0.5)TiO_(3)-Na_(0.5)Bi_(4.5)TiO_(15)-Mn lead-free pyroelectric ceramics are used as sensitive materials to prepare pyroelectric sensors.Na_(0.5)Bi_(0.5)TiO_(3)-Na_(0.5)Bi_(4.5)TiO_(15)-Mn ceramics can achieve 7.58×10^(-4)C·m^(-2)·K^(-1)high-roomtemperature pyroelectric coefficient and depolarization temperature of 151℃.Due to the low dielectric constant and loss caused by Mn doping,the high detection rate value of 24.382μPa^(-1/2)is obtained.The voltage response rate and specific detection rate of the sensor prepared on this basis can attain the JC-T 2397-2017(ε_(r)>200,tanδ<5%,T_(c)>200,p>3.50×10^(-4)C·m^(-2)·K^(-1))application standard of pyroelectric infrared detectors.Thermoelectric cooler is proposed to adjust the temperature of the sensor,and its voltage response to human radiation is measured.Harnessing the superior pyroelectric attributes of advanced materials and connectable devices,the nascentthermoelectric-pyroelectric detection method is poised to be a subject of intensive investigation and development.
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.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 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support of National Science Foundation of China (Nos.91963207 and 12075174)。
文摘To obtain a high-performance heterogeneous photo-catalyst, herein, the hetero-structured Zn In_(2)S_(4)-Ni O@MOF(ZNM) nano-sheets are designed and prepared by partial pyrolysis of nickel-based MOFs(NiMOF) combined with the low-temperature solvo-thermal method. The results indicate that the NiO nanoparticles, produced by partial pyrolysis of the Ni-MOF, have a high density of the surface active sites with limited aggregation, which act as a co-catalyst to capture photo-induced charge carriers. In addition, the morphology and structure of Ni-MOF nano-sheets were preserved in ZNM, which is beneficial to the reduction of the conduction barrier for the photo generated electron-hole pairs. With the synergetic advantages of co-catalyst and unique two-dimensional hetero-structure, ZNM nano-sheets exhibited significantly improved activity for photo-catalytic hydrogen production.
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.
