Thermal runaway(TR)is a critical issue hindering the large-scale application of lithium-ion batteries(LIBs).Understanding the thermal safety behavior of LIBs at the cell and module level under different state of charg...Thermal runaway(TR)is a critical issue hindering the large-scale application of lithium-ion batteries(LIBs).Understanding the thermal safety behavior of LIBs at the cell and module level under different state of charges(SOCs)has significant implications for reinforcing the thermal safety design of the lithium-ion battery module.This study first investigates the thermal safety boundary(TSB)correspondence at the cells and modules level under the guidance of a newly proposed concept,safe electric quantity boundary(SEQB).A reasonable thermal runaway propagation(TRP)judgment indicator,peak heat transfer power(PHTP),is proposed to predict whether TRP occurs.Moreover,a validated 3D model is used to quantitatively clarify the TSB at different SOCs from the perspective of PHTP,TR trigger temperature,SOC,and the full cycle life.Besides,three different TRP transfer modes are discovered.The interconversion relationship of three different TRP modes is investigated from the perspective of PHTP.This paper explores the TSB of LIBs under different SOCs at both cell and module levels for the first time,which has great significance in guiding the thermal safety design of battery systems.展开更多
Aim To research and develop a battery management system(BMS)with the state of charge(SOC)indicator for electric vehicles (EVs).Methods On the basis of analyzing the electro-chemical characteristics of lead-acid. batte...Aim To research and develop a battery management system(BMS)with the state of charge(SOC)indicator for electric vehicles (EVs).Methods On the basis of analyzing the electro-chemical characteristics of lead-acid. battery, the state of charge indicator for lead-acid battery was developed by means of an algorithm based on combination of ampere-hour, Peukert's equation and open-voltage method with the compensation of temperature,aging,self- discharging,etc..Results The BMS based on this method can attain an accurate surplus capa- city whose error is less than 5% in static experiments.It is proved by experiments that the BMS is reliable and can give the driver an accurate surplus capacity,precisely monitor the individual battery modules as the same time,even detect and warn the problems early,and so on. Conclusion A BMS can make the energy of the storage batteries used efficiently, develop the batteries cycle life,and increase the driving distance of EVs.展开更多
A fuzzy model was established to estimate the state of charge(SOC) of a lithium-ion battery for electric vehicles.The robust Gustafson-Kessel(GK) clustering algorithm based on clustering validity indices was appli...A fuzzy model was established to estimate the state of charge(SOC) of a lithium-ion battery for electric vehicles.The robust Gustafson-Kessel(GK) clustering algorithm based on clustering validity indices was applied to identify the structure and antecedent parameters of the model.The least squares algorithm was utilized to determine the consequent parameters.Validation results show that this model can provide accurate SOC estimation for the lithium-ion battery and satisfy the requirement for practical electric vehicle applications.展开更多
A combined algorithm for battery state of charge (SOC) estimation is proposed to solve the critical issue of hybrid electric vehicle (HEV). To obtain a more accurate SOC, both coulomb-accumulation and battery resi...A combined algorithm for battery state of charge (SOC) estimation is proposed to solve the critical issue of hybrid electric vehicle (HEV). To obtain a more accurate SOC, both coulomb-accumulation and battery resistance-capacitor (RC) model are weighted combined to compensate the deficiencies of individual methods. In order to solve the key issue of coulomb-accumulation, the battery thermal model is used. Based on the principle of energy conservation, the heat generated from battery charge and discharge process is converted into the equivalent electricity to calculate charge and discharge efficiency under variable current. The extended Kalman filter (EKF) as a closed loop algorithm is applied to estimate the parameters of resistance-capacitor model. The input variables do not increase much computing difficulty. The proposed combined algorithm is implemented by adjusting the weighting factor of coulomb- accumulation and resistance-capacitor model. In the end, four different methods including Ah-efficiency, Ah-Equip, RC-SOC and Combined-SOC are compared in federal testing procedure (FTP) drive cycle. The experiment results show that the proposed method has good robustness and high accuracy which is suitable for HEV application.展开更多
The excited state intramolecular proton transfer (ESIPT) coupled charge transfer of baicalein has been investigated using steady-state spectroscopic experiment and quantum chemistry calculations. The absence of the ...The excited state intramolecular proton transfer (ESIPT) coupled charge transfer of baicalein has been investigated using steady-state spectroscopic experiment and quantum chemistry calculations. The absence of the absorption peak from S1 excited state both in the experi-mental and calculated absorption spectra indicates that S1 is a dark state. The dark excited state S1 results in the very weak fluorescence of solid baicalein in the experiment. The fron- tier molecular orbital and the charge difference densities of baicalein show clearly that the S1 state is a charge-transfer state whereas the S2 state is a locally excited state. The only one stationary point on the potential energy profile of excited state suggests that the ESIPT reaction of baicalein is a barrierless process.展开更多
The on-line estimation of the state of charge (SOC) of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial...The on-line estimation of the state of charge (SOC) of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network (RBF NN) has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.展开更多
In the field of energy storage,it is very important to predict the state of charge and the state of health of lithium-ion batteries.In this paper,we review the current widely used equivalent circuit and electrochemica...In the field of energy storage,it is very important to predict the state of charge and the state of health of lithium-ion batteries.In this paper,we review the current widely used equivalent circuit and electrochemical models for battery state predictions.The review demonstrates that machine learning and deep learning approaches can be used to construct fast and accurate data-driven models for the prediction of battery performance.The details,advantages,and limitations of these approaches are presented,compared,and summarized.Finally,future key challenges and opportunities are discussed.展开更多
Lithium-ion batteries are widely used in electric vehicles and electronics, and their thermal safety receives widespread attention from consumers. In our study, thermal runaway testing was conducted on the thermal sta...Lithium-ion batteries are widely used in electric vehicles and electronics, and their thermal safety receives widespread attention from consumers. In our study, thermal runaway testing was conducted on the thermal stability of commercial lithium-ion batteries, and the internal structure of the battery was analyzed with an in-depth focus on the key factors of the thermal runaway. Through the study of the structure and thermal stability of the cathode, anode, and separator, the results showed that the phase transition reaction of the separator was the key factor affecting the thermal runaway of the battery for the condition of a low state of charge.展开更多
Since the main power source of hybrid electric vehicle(HEV) is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge(SOC) estimation has attracted great attention ...Since the main power source of hybrid electric vehicle(HEV) is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge(SOC) estimation has attracted great attention in the area of HEV.However,the value of SOC estimation could not be greatly precise so that the running performance of HEV is greatly affected.A variable structure extended kalman filter(VSEKF)-based estimation method,which could be used to analyze the SOC of lithium-ion battery in the fixed driving condition,is presented.First,the general lower-order battery equivalent circuit model(GLM),which includes column accumulation model,open circuit voltage model and the SOC output model,is established,and the off-line and online model parameters are calculated with hybrid pulse power characteristics(HPPC) test data.Next,a VSEKF estimation method of SOC,which integrates the ampere-hour(Ah) integration method and the extended Kalman filter(EKF) method,is executed with different adaptive weighting coefficients,which are determined according to the different values of open-circuit voltage obtained in the corresponding charging or discharging processes.According to the experimental analysis,the faster convergence speed and more accurate simulating results could be obtained using the VSEKF method in the running performance of HEV.The error rate of SOC estimation with the VSEKF method is focused in the range of 5% to 10% comparing with the range of 20% to 30% using the EKF method and the Ah integration method.In Summary,the accuracy of the SOC estimation in the lithium-ion battery cell and the pack of lithium-ion battery system,which is obtained utilizing the VSEKF method has been significantly improved comparing with the Ah integration method and the EKF method.The VSEKF method utilizing in the SOC estimation in the lithium-ion pack of HEV can be widely used in practical driving conditions.展开更多
The technology deployed for lithium-ion battery state of charge(SOC)estimation is an important part of the design of electric vehicle battery management systems.Accurate SOC estimation can forestall excessive charging...The technology deployed for lithium-ion battery state of charge(SOC)estimation is an important part of the design of electric vehicle battery management systems.Accurate SOC estimation can forestall excessive charging and discharging of lithium-ion batteries,thereby improving discharge efficiency and extending cycle life.In this study,the key lithium-ion battery SOC estimation technologies are summarized.First,the research status of lithium-ion battery modeling is introduced.Second,the main technologies and difficulties in model parameter identification for lithium-ion batteries are discussed.Third,the development status and advantages and disadvantages of SOC estimation methods are summarized.Finally,the current research problems and prospects for development trends are summarized.展开更多
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
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
We propose a simple scheme to generate x-type four-charge entangled states by using SQUID-based charge qubits capacitively coupled to a transmission line resonator (TLR). The coupling between the superconducting qub...We propose a simple scheme to generate x-type four-charge entangled states by using SQUID-based charge qubits capacitively coupled to a transmission line resonator (TLR). The coupling between the superconducting qubit and the TLR can be effectively controlled by properly adjusting the control parameters of the charge qubit. The experimental feasibility of our scheme is also shown.展开更多
State of Charge (SOC) determination is an increasingly important issue in battery technology. In addition to the immediate display of the remaining battery capacity to the user, precise knowledge of SOC exerts additio...State of Charge (SOC) determination is an increasingly important issue in battery technology. In addition to the immediate display of the remaining battery capacity to the user, precise knowledge of SOC exerts additional control over the charging/discharging process which in turn reduces the risk of over-voltage and gassing, which degrade the chemical composition of the electrolyte and plates. This paper describes a new approach to SOC determination for the lead-acid battery management system by combining Ah-balance with an EMF estimation algorithm, which predicts the battery’s EMF value while it is under load. The EMF estimation algorithm is based on an equivalent-circuit representation of the battery, with the parameters determined from a pulse test performed on the battery and a curve-fitting algorithm by means of least-square regression. The whole battery cycle is classified into seven states where the SOC is estimated with the Ah-balance method and the proposed EMF based algorithm. Laboratory tests and results are described in detail in the paper.展开更多
Using electric vehicles(EVs)for transportation is considered as a necessary component for managing sustainable development and environmental issues.The present concerns regarding the environment,such as rapid fossil f...Using electric vehicles(EVs)for transportation is considered as a necessary component for managing sustainable development and environmental issues.The present concerns regarding the environment,such as rapid fossil fuel depletion,increases in air pollution,accelerating energy demands,global warming,and climate change,have paved the way for the electrification of the transport sector.EVs can address all of the aforementioned issues.Portable power supplies have become the lifeline of the EV world,especially lithium-ion(Li-ion)batteries.Li-ion batteries have attracted considerable attention in the EV industry,owing to their high energy density,power density,lifespan,nominal voltage,and cost.One major issue with such batteries concerns providing a quick and accurate estimation of a battery’s state and health;therefore,accurate determinations of the battery’S performance and health,as well as an accurate prediction of its life,are necessary to ensure reliability and efficiency.This study conducts a review of the technological briefs of EVs and their types,as well as the corresponding battery characteristics.Various aspects of recent research and developments in Li-ion battery prognostics and health monitoring are summarized,along with the techniques,algorithms,and models used for current/voltage estimations,state-of-charge(SoC)estimations,capacity estimations,and remaining-useful-life predictions.展开更多
This paper presents a more accurate battery state of charge(SOC)and state of health(SOH)estimation method.A lithium battery is represented by a nonlinear two-order resistance-capacitance equivalent circuit model.The m...This paper presents a more accurate battery state of charge(SOC)and state of health(SOH)estimation method.A lithium battery is represented by a nonlinear two-order resistance-capacitance equivalent circuit model.The model parameters are estimated by searching least square error optimization algorithm.Precisely defined by this method,the model parameters allow to accurately determine the capacity of the battery,which in turn allows to specify the SOC prediction value used as a basis for the SOH value.Application of the extended Kalman filter(EKF)removes the need of prior known initial SOC,and applying the fuzzy logic helps to eliminate the measurement and process noise.Simulation results obtained during the urban dynamometer driving schedule(UDDS)test show that the maximum error in estimation of the battery SOC is 0.66%.Battery capacity is estimate by offline updated Kalman filter,and then SOH will be predicted.The maximum error in estimation of the battery capacity is 1.55%.展开更多
We perform density functional theory calculations to investigate the polaron pair (charge transfer state) photo-generation in donor-acceptor oligomer methyl-capped (4,7- benzo[2,1,3]thiadiazole-2,6-(4,4-bis(2-eth...We perform density functional theory calculations to investigate the polaron pair (charge transfer state) photo-generation in donor-acceptor oligomer methyl-capped (4,7- benzo[2,1,3]thiadiazole-2,6-(4,4-bis(2-ethylhexyl)-4H-cyclopent a[1,2-b;3,4-b']dithiophene-4, 7-benzo[2,1,3]thiadiazole) (CPDTBT). Results show that effective photo-generation of charge transfer state can happen in CPDTBT dimer when the group 4,7-benzo[2,1,3]thiadiazole (BT) in one monomer deviates against the conjugated plane (onset torsion angle is about 20°). The lower excitation energy (530 nm) can only generate the intramolecular excitonic state, while the higher excitation energy (370 nm) can generate the intermolecular charge transfer state, in good agreement with the experiment. Moreover, the mechanism of charge separation in CPDTBT oligorners is discussed.展开更多
The estimation of state of charge(SOC)using deep neural networks(DNN)generally requires a considerable number of labelled samples for training,which refer to the current and voltage pieces with knowing their correspon...The estimation of state of charge(SOC)using deep neural networks(DNN)generally requires a considerable number of labelled samples for training,which refer to the current and voltage pieces with knowing their corresponding SOCs.However,the collection of labelled samples is costly and time-consuming.In contrast,the unlabelled training samples,which consist of the current and voltage data with unknown SOCs,are easy to obtain.In view of this,this paper proposes an improved DNN for SOC estimation by effectively using both a pool of unlabelled samples and a limited number of labelled samples.Besides the traditional supervised network,the proposed method uses an input reconstruction network to reformulate the time dependency features of the voltage and current.In this way,the developed network can extract useful information from the unlabelled samples.The proposed method is validated under different drive cycles and temperature conditions.The results reveal that the SOC estimation accuracy of the DNN trained with both labelled and unlabelled samples outperforms that of only using a limited number of labelled samples.In addition,when the dataset with reduced number of labelled samples to some extent is used to test the developed network,it is found that the proposed method performs well and is robust in producing the model outputs with the required accuracy when the unlabelled samples are involved in the model training.Furthermore,the proposed method is evaluated with different recurrent neural networks(RNNs)applied to the input reconstruction module.The results indicate that the proposed method is feasible for various RNN algorithms,and it could be flexibly applied to other conditions as required.展开更多
The structural features and three-dimensional nature of the charge density wave (CDW) state of the layered chalcogenide 1T-TaSe2-xTex (0≤x≤2.0) are characterized by Cs-corrected transmission electron microscopy ...The structural features and three-dimensional nature of the charge density wave (CDW) state of the layered chalcogenide 1T-TaSe2-xTex (0≤x≤2.0) are characterized by Cs-corrected transmission electron microscopy measurements. Notable changes of both average structure and the CDW state arising from Te substitution for Se are clearly demonstrated in samples with x〉0.3. The commensurate CDW state characterized by the known star-of-David clustering in the 1T-TaSe2 crystal becomes visibly unstable with Te substitution and vanishes when x=0.3. The 1T-TaSe2-xTex (0.3≤x≤1.3) samples generally adopt a remarkable incommensurate CDW state with monoclinic distortion, which could be fundamentally in correlation with the strong qq-dependent electron-phonon coupling-induced period-lattice-distortion as identified in TaTe22. Systematic analysis demonstrates that the occurrence of superconductivity is related to the suppression of the commensurate CDW phase and the presence of discommensuration is an evident structural feature observed in the superconducting samples.展开更多
In order to improve the accuracy of the battery state of charge(SOC) estimation, in this paper we take a lithiumion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, ...In order to improve the accuracy of the battery state of charge(SOC) estimation, in this paper we take a lithiumion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate.Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded.展开更多
基金supported by the National Natural Science Foundation of China(No.U20A20310 and No.52176199)sponsored by the Program of Shanghai Academic/Technology Research Leader(No.22XD1423800)。
文摘Thermal runaway(TR)is a critical issue hindering the large-scale application of lithium-ion batteries(LIBs).Understanding the thermal safety behavior of LIBs at the cell and module level under different state of charges(SOCs)has significant implications for reinforcing the thermal safety design of the lithium-ion battery module.This study first investigates the thermal safety boundary(TSB)correspondence at the cells and modules level under the guidance of a newly proposed concept,safe electric quantity boundary(SEQB).A reasonable thermal runaway propagation(TRP)judgment indicator,peak heat transfer power(PHTP),is proposed to predict whether TRP occurs.Moreover,a validated 3D model is used to quantitatively clarify the TSB at different SOCs from the perspective of PHTP,TR trigger temperature,SOC,and the full cycle life.Besides,three different TRP transfer modes are discovered.The interconversion relationship of three different TRP modes is investigated from the perspective of PHTP.This paper explores the TSB of LIBs under different SOCs at both cell and module levels for the first time,which has great significance in guiding the thermal safety design of battery systems.
文摘Aim To research and develop a battery management system(BMS)with the state of charge(SOC)indicator for electric vehicles (EVs).Methods On the basis of analyzing the electro-chemical characteristics of lead-acid. battery, the state of charge indicator for lead-acid battery was developed by means of an algorithm based on combination of ampere-hour, Peukert's equation and open-voltage method with the compensation of temperature,aging,self- discharging,etc..Results The BMS based on this method can attain an accurate surplus capa- city whose error is less than 5% in static experiments.It is proved by experiments that the BMS is reliable and can give the driver an accurate surplus capacity,precisely monitor the individual battery modules as the same time,even detect and warn the problems early,and so on. Conclusion A BMS can make the energy of the storage batteries used efficiently, develop the batteries cycle life,and increase the driving distance of EVs.
基金Sponsored by the National High Technology Research and Development Program of China("863"Program)(2003AA501800)
文摘A fuzzy model was established to estimate the state of charge(SOC) of a lithium-ion battery for electric vehicles.The robust Gustafson-Kessel(GK) clustering algorithm based on clustering validity indices was applied to identify the structure and antecedent parameters of the model.The least squares algorithm was utilized to determine the consequent parameters.Validation results show that this model can provide accurate SOC estimation for the lithium-ion battery and satisfy the requirement for practical electric vehicle applications.
基金National Hi-tech Research Development Program of China(863 Program,No.2002AA501732)National Basic Research Program of China(973 Program,No.2007CB209707)
文摘A combined algorithm for battery state of charge (SOC) estimation is proposed to solve the critical issue of hybrid electric vehicle (HEV). To obtain a more accurate SOC, both coulomb-accumulation and battery resistance-capacitor (RC) model are weighted combined to compensate the deficiencies of individual methods. In order to solve the key issue of coulomb-accumulation, the battery thermal model is used. Based on the principle of energy conservation, the heat generated from battery charge and discharge process is converted into the equivalent electricity to calculate charge and discharge efficiency under variable current. The extended Kalman filter (EKF) as a closed loop algorithm is applied to estimate the parameters of resistance-capacitor model. The input variables do not increase much computing difficulty. The proposed combined algorithm is implemented by adjusting the weighting factor of coulomb- accumulation and resistance-capacitor model. In the end, four different methods including Ah-efficiency, Ah-Equip, RC-SOC and Combined-SOC are compared in federal testing procedure (FTP) drive cycle. The experiment results show that the proposed method has good robustness and high accuracy which is suitable for HEV application.
基金ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (No.61137005 and No.10974023), the Program for Liaoning Excellent Talents in University (No.LJQ2012002), and the Program for New Century Excellent Talents in University (No.NCET-12-0077).
文摘The excited state intramolecular proton transfer (ESIPT) coupled charge transfer of baicalein has been investigated using steady-state spectroscopic experiment and quantum chemistry calculations. The absence of the absorption peak from S1 excited state both in the experi-mental and calculated absorption spectra indicates that S1 is a dark state. The dark excited state S1 results in the very weak fluorescence of solid baicalein in the experiment. The fron- tier molecular orbital and the charge difference densities of baicalein show clearly that the S1 state is a charge-transfer state whereas the S2 state is a locally excited state. The only one stationary point on the potential energy profile of excited state suggests that the ESIPT reaction of baicalein is a barrierless process.
基金Project supported by the National High Technology Research and Development Program of China (Grant No. 2011AA110303)the Beijing Municipal Science & Technology Project,China (Grant No. Z111100064311001)
文摘The on-line estimation of the state of charge (SOC) of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network (RBF NN) has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.
基金funding support from the Department of Science and Technology of Guangdong Province(2019A050510043)the Department of Science and Technology of Zhuhai City(ZH22017001200059PWC)+1 种基金the National Natural Science Foundation of China(2210050123)the China Postdoctoral Science Foundation(2021TQ0161 and 2021M691709)。
文摘In the field of energy storage,it is very important to predict the state of charge and the state of health of lithium-ion batteries.In this paper,we review the current widely used equivalent circuit and electrochemical models for battery state predictions.The review demonstrates that machine learning and deep learning approaches can be used to construct fast and accurate data-driven models for the prediction of battery performance.The details,advantages,and limitations of these approaches are presented,compared,and summarized.Finally,future key challenges and opportunities are discussed.
基金financial supports from National Key R&D Program of China (2018YFC1902200)the key technologies R&D program of Tianjin (18YFZCGX00240)key R&D Program of China Automotive Technology and Research Center Co., Ltd. (18200116)。
文摘Lithium-ion batteries are widely used in electric vehicles and electronics, and their thermal safety receives widespread attention from consumers. In our study, thermal runaway testing was conducted on the thermal stability of commercial lithium-ion batteries, and the internal structure of the battery was analyzed with an in-depth focus on the key factors of the thermal runaway. Through the study of the structure and thermal stability of the cathode, anode, and separator, the results showed that the phase transition reaction of the separator was the key factor affecting the thermal runaway of the battery for the condition of a low state of charge.
基金Supported by National Key Technology R&D Program of Ministry of Science and Technology of China(Grant No.2013BAG14B01)
文摘Since the main power source of hybrid electric vehicle(HEV) is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge(SOC) estimation has attracted great attention in the area of HEV.However,the value of SOC estimation could not be greatly precise so that the running performance of HEV is greatly affected.A variable structure extended kalman filter(VSEKF)-based estimation method,which could be used to analyze the SOC of lithium-ion battery in the fixed driving condition,is presented.First,the general lower-order battery equivalent circuit model(GLM),which includes column accumulation model,open circuit voltage model and the SOC output model,is established,and the off-line and online model parameters are calculated with hybrid pulse power characteristics(HPPC) test data.Next,a VSEKF estimation method of SOC,which integrates the ampere-hour(Ah) integration method and the extended Kalman filter(EKF) method,is executed with different adaptive weighting coefficients,which are determined according to the different values of open-circuit voltage obtained in the corresponding charging or discharging processes.According to the experimental analysis,the faster convergence speed and more accurate simulating results could be obtained using the VSEKF method in the running performance of HEV.The error rate of SOC estimation with the VSEKF method is focused in the range of 5% to 10% comparing with the range of 20% to 30% using the EKF method and the Ah integration method.In Summary,the accuracy of the SOC estimation in the lithium-ion battery cell and the pack of lithium-ion battery system,which is obtained utilizing the VSEKF method has been significantly improved comparing with the Ah integration method and the EKF method.The VSEKF method utilizing in the SOC estimation in the lithium-ion pack of HEV can be widely used in practical driving conditions.
基金supported by research on value model and technology application of patent operation of science and technology project(52094020000U)National Natural Science Foundation of China(52177193).
文摘The technology deployed for lithium-ion battery state of charge(SOC)estimation is an important part of the design of electric vehicle battery management systems.Accurate SOC estimation can forestall excessive charging and discharging of lithium-ion batteries,thereby improving discharge efficiency and extending cycle life.In this study,the key lithium-ion battery SOC estimation technologies are summarized.First,the research status of lithium-ion battery modeling is introduced.Second,the main technologies and difficulties in model parameter identification for lithium-ion batteries are discussed.Third,the development status and advantages and disadvantages of SOC estimation methods are summarized.Finally,the current research problems and prospects for development trends are summarized.
基金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supported by the National Natural Science Foundations of China (Grant Nos. 10947017/A05 and 11074190)the Science Foundation of the Key Laboratory of Novel Thin Film Solar Cells, China (Grant No. KF200912)the Graduates' Innovative Scientific Research Project of Zhejiang Province, China (Grant No. 2011831)
文摘We propose a simple scheme to generate x-type four-charge entangled states by using SQUID-based charge qubits capacitively coupled to a transmission line resonator (TLR). The coupling between the superconducting qubit and the TLR can be effectively controlled by properly adjusting the control parameters of the charge qubit. The experimental feasibility of our scheme is also shown.
文摘State of Charge (SOC) determination is an increasingly important issue in battery technology. In addition to the immediate display of the remaining battery capacity to the user, precise knowledge of SOC exerts additional control over the charging/discharging process which in turn reduces the risk of over-voltage and gassing, which degrade the chemical composition of the electrolyte and plates. This paper describes a new approach to SOC determination for the lead-acid battery management system by combining Ah-balance with an EMF estimation algorithm, which predicts the battery’s EMF value while it is under load. The EMF estimation algorithm is based on an equivalent-circuit representation of the battery, with the parameters determined from a pulse test performed on the battery and a curve-fitting algorithm by means of least-square regression. The whole battery cycle is classified into seven states where the SOC is estimated with the Ah-balance method and the proposed EMF based algorithm. Laboratory tests and results are described in detail in the paper.
基金by Department of Science and Technology,New Delhi(Indo-Norway consortium)project entitled“Integrated Renewable Resources and Storage Operation and Management”program.
文摘Using electric vehicles(EVs)for transportation is considered as a necessary component for managing sustainable development and environmental issues.The present concerns regarding the environment,such as rapid fossil fuel depletion,increases in air pollution,accelerating energy demands,global warming,and climate change,have paved the way for the electrification of the transport sector.EVs can address all of the aforementioned issues.Portable power supplies have become the lifeline of the EV world,especially lithium-ion(Li-ion)batteries.Li-ion batteries have attracted considerable attention in the EV industry,owing to their high energy density,power density,lifespan,nominal voltage,and cost.One major issue with such batteries concerns providing a quick and accurate estimation of a battery’s state and health;therefore,accurate determinations of the battery’S performance and health,as well as an accurate prediction of its life,are necessary to ensure reliability and efficiency.This study conducts a review of the technological briefs of EVs and their types,as well as the corresponding battery characteristics.Various aspects of recent research and developments in Li-ion battery prognostics and health monitoring are summarized,along with the techniques,algorithms,and models used for current/voltage estimations,state-of-charge(SoC)estimations,capacity estimations,and remaining-useful-life predictions.
基金Open Fund Project of State Key Laboratory of Large Electric Transmission Systems and Equipment Technology(No.SKLLDJ042017005)。
文摘This paper presents a more accurate battery state of charge(SOC)and state of health(SOH)estimation method.A lithium battery is represented by a nonlinear two-order resistance-capacitance equivalent circuit model.The model parameters are estimated by searching least square error optimization algorithm.Precisely defined by this method,the model parameters allow to accurately determine the capacity of the battery,which in turn allows to specify the SOC prediction value used as a basis for the SOH value.Application of the extended Kalman filter(EKF)removes the need of prior known initial SOC,and applying the fuzzy logic helps to eliminate the measurement and process noise.Simulation results obtained during the urban dynamometer driving schedule(UDDS)test show that the maximum error in estimation of the battery SOC is 0.66%.Battery capacity is estimate by offline updated Kalman filter,and then SOH will be predicted.The maximum error in estimation of the battery capacity is 1.55%.
基金supported by the National Natural Science Foundation of China (No.21473092)the Jiangsu Province Production and Joint Innovation Fund-Prospective Joint Research Project (No. BY2014007-01)
文摘We perform density functional theory calculations to investigate the polaron pair (charge transfer state) photo-generation in donor-acceptor oligomer methyl-capped (4,7- benzo[2,1,3]thiadiazole-2,6-(4,4-bis(2-ethylhexyl)-4H-cyclopent a[1,2-b;3,4-b']dithiophene-4, 7-benzo[2,1,3]thiadiazole) (CPDTBT). Results show that effective photo-generation of charge transfer state can happen in CPDTBT dimer when the group 4,7-benzo[2,1,3]thiadiazole (BT) in one monomer deviates against the conjugated plane (onset torsion angle is about 20°). The lower excitation energy (530 nm) can only generate the intramolecular excitonic state, while the higher excitation energy (370 nm) can generate the intermolecular charge transfer state, in good agreement with the experiment. Moreover, the mechanism of charge separation in CPDTBT oligorners is discussed.
基金the financial support from the China Scholarship Council(CSC)(No.202207550010)。
文摘The estimation of state of charge(SOC)using deep neural networks(DNN)generally requires a considerable number of labelled samples for training,which refer to the current and voltage pieces with knowing their corresponding SOCs.However,the collection of labelled samples is costly and time-consuming.In contrast,the unlabelled training samples,which consist of the current and voltage data with unknown SOCs,are easy to obtain.In view of this,this paper proposes an improved DNN for SOC estimation by effectively using both a pool of unlabelled samples and a limited number of labelled samples.Besides the traditional supervised network,the proposed method uses an input reconstruction network to reformulate the time dependency features of the voltage and current.In this way,the developed network can extract useful information from the unlabelled samples.The proposed method is validated under different drive cycles and temperature conditions.The results reveal that the SOC estimation accuracy of the DNN trained with both labelled and unlabelled samples outperforms that of only using a limited number of labelled samples.In addition,when the dataset with reduced number of labelled samples to some extent is used to test the developed network,it is found that the proposed method performs well and is robust in producing the model outputs with the required accuracy when the unlabelled samples are involved in the model training.Furthermore,the proposed method is evaluated with different recurrent neural networks(RNNs)applied to the input reconstruction module.The results indicate that the proposed method is feasible for various RNN algorithms,and it could be flexibly applied to other conditions as required.
基金Supported by the National Basic Research Program of China under Grant Nos 2015CB921300 and 2012CB821404the National Key Research and Development Program of China under Grant Nos 2016YFA0300300 and 2016YFA0300404+1 种基金the National Natural Science Foundation of China under Grant Nos 11474323,11604372,11274368,91221102,11190022,11674326 and 91422303the Strategic Priority Research Program(B)of the Chinese Academy of Sciences under Grant No XDB07020000
文摘The structural features and three-dimensional nature of the charge density wave (CDW) state of the layered chalcogenide 1T-TaSe2-xTex (0≤x≤2.0) are characterized by Cs-corrected transmission electron microscopy measurements. Notable changes of both average structure and the CDW state arising from Te substitution for Se are clearly demonstrated in samples with x〉0.3. The commensurate CDW state characterized by the known star-of-David clustering in the 1T-TaSe2 crystal becomes visibly unstable with Te substitution and vanishes when x=0.3. The 1T-TaSe2-xTex (0.3≤x≤1.3) samples generally adopt a remarkable incommensurate CDW state with monoclinic distortion, which could be fundamentally in correlation with the strong qq-dependent electron-phonon coupling-induced period-lattice-distortion as identified in TaTe22. Systematic analysis demonstrates that the occurrence of superconductivity is related to the suppression of the commensurate CDW phase and the presence of discommensuration is an evident structural feature observed in the superconducting samples.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61004048 and 61201010)
文摘In order to improve the accuracy of the battery state of charge(SOC) estimation, in this paper we take a lithiumion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate.Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded.