As lithium-ion batteries become increasingly prevalent in electric scooters,vehicles,mobile devices,and energy storage systems,accurate estimation of remaining battery capacity is crucial for optimizing system perform...As lithium-ion batteries become increasingly prevalent in electric scooters,vehicles,mobile devices,and energy storage systems,accurate estimation of remaining battery capacity is crucial for optimizing system performance and reliability.Unlike traditional methods that rely on static alternating internal resistance(SAIR)measurements in an open-circuit state,this study presents a real-time state of charge(SOC)estimation method combining dynamic alternating internal resistance(DAIR)with artificial neural networks(ANN).The system simultaneously measures electrochemical impedance various frequencies,discharge C-rate,and battery surface temperature during the∣Z∣atdischarge process,using these parameters for ANN training.The ANN,leveraging its superior nonlinear system modeling capabilities,effectively captures the complex nonlinear relationships between AC impedance and SOC through iterative training.Compared to other machine learning approaches,the proposed ANN features a simpler architecture and lower computational overhead,making it more suitable for integration into battery management system(BMS)microcontrollers.In tests conducted with Samsung batteries using lithium cobalt oxide cathode material,the method achieved an overall average error of merely 0.42%in self-validation,with mean absolute errors(MAE)for individual SOCs not exceeding 1%.Secondary validation demonstrated an overall average error of 1.24%,with MAE for individual SOCs below 2.5%.This integrated DAIR-ANN approach not only provides enhanced estimation accuracy but also simplifies computational requirements,offering a more effective solution for battery management in practical applications.展开更多
Thermal runaway(TR)is a critical issue hindering the large-scale application of lithium-ion batteries(LIBs).Understanding the thermal safety behavior of LIBs at the cell and module level under different state of charg...Thermal runaway(TR)is a critical issue hindering the large-scale application of lithium-ion batteries(LIBs).Understanding the thermal safety behavior of LIBs at the cell and module level under different state of charges(SOCs)has significant implications for reinforcing the thermal safety design of the lithium-ion battery module.This study first investigates the thermal safety boundary(TSB)correspondence at the cells and modules level under the guidance of a newly proposed concept,safe electric quantity boundary(SEQB).A reasonable thermal runaway propagation(TRP)judgment indicator,peak heat transfer power(PHTP),is proposed to predict whether TRP occurs.Moreover,a validated 3D model is used to quantitatively clarify the TSB at different SOCs from the perspective of PHTP,TR trigger temperature,SOC,and the full cycle life.Besides,three different TRP transfer modes are discovered.The interconversion relationship of three different TRP modes is investigated from the perspective of PHTP.This paper explores the TSB of LIBs under different SOCs at both cell and module levels for the first time,which has great significance in guiding the thermal safety design of battery systems.展开更多
Recent experimental evidence of the charge-6e condensed phase in Kagome superconductors has generated significant interest.This study investigates the unconventional superconductivity in the Kagome superconductor CsV_...Recent experimental evidence of the charge-6e condensed phase in Kagome superconductors has generated significant interest.This study investigates the unconventional superconductivity in the Kagome superconductor CsV_(3)Sb_(5),focusing on the emergence of charge-6e superconductivity(SC)at temperatures higher than the conventional charge-2e SC state.By modeling the phase coherence of the SC order parameter using a frustrated antiferromagnetic XY model on an emergent Kagome lattice,the condensation of fractional vortices with 1/3 vorticity stabilizes the phase coherence in exp(i3θ),resulting in a charge-6e SC state.Using a tensor network approach tailored for frustrated spin systems,a Berezinskii-Kosterlitz-Thouless transition is identified at T_(c)/J≃0.075,where the unbinding of 1/3 fractional vortex-antivortex pairs transforms the system from the charge-6e SC phase to the normal phase.Below T_(c),the 1/3 fractional vortex correlations exhibit a power-law decay,whereas the integer vortex correlations decay exponentially,reflecting the dominance of charge-6e SC in the absence of charge-2e SC.The results provide a theoretical understanding of charge-6e SC in two-dimensional Kagome superconductors,emphasizing the interaction between fractional vortices,frustration,and topology in stabilizing the exotic SC phase.展开更多
ABSTRACT The accurate state-of-charge(SOC)estimation of sodium-ion batteries is the basis for their efficient application.In this paper,a new SOC estimation method suitable for sodium-ion batteries and their applicati...ABSTRACT The accurate state-of-charge(SOC)estimation of sodium-ion batteries is the basis for their efficient application.In this paper,a new SOC estimation method suitable for sodium-ion batteries and their application conditions is proposed,which considers the combination of open circuit voltage(OCV)and internal resistance correction.First,the optimal order of equivalent circuit model is analyzed and selected,and the monotonic and stable mapping relationships between OCV and SOC,as well as between ohmic internal resistance and SOC are determined.Then,a joint estimation algorithm for battery model parameters and SOC is estab-lished,and a joint SOC correction strategy based on OCV and ohmic internal resistance is established.The test results show that OCV correction is reliable when polarization is small,that the ohmic internal resistance correction is reliable when the current fluctuation is large,and that the maximum absolute error of SOC estimation of the proposed method is not more than 2.6%.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
在孤岛直流微电网系统中,线路阻抗不匹配会影响各线路的电流分配精度和荷电状态(state of charge,SOC)均衡效果。同时,由于采用下垂控制,虚拟阻抗的存在也会导致直流母线电压下降。针对以上问题,提出了一种基于自适应虚拟阻抗的SOC均衡...在孤岛直流微电网系统中,线路阻抗不匹配会影响各线路的电流分配精度和荷电状态(state of charge,SOC)均衡效果。同时,由于采用下垂控制,虚拟阻抗的存在也会导致直流母线电压下降。针对以上问题,提出了一种基于自适应虚拟阻抗的SOC均衡控制策略。该策略考虑了不同容量的分布式储能单元(distributed energy storage units,DESUs),并设计了交互DESUs邻居单元SOC均衡差异信息的收敛因子,以加快SOC均衡速度。利用结合多种系统状态信息的状态因子,通过单补偿环节即可实现输出电流的精准分配以及母线电压的恢复。使用改进后的动态平均一致性算法获取系统全局平均状态信息估计值。最后,在Matlab/Simulink仿真软件中搭建了4种工况模型,验证了所提控制策略的有效性和可靠性。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
文摘As lithium-ion batteries become increasingly prevalent in electric scooters,vehicles,mobile devices,and energy storage systems,accurate estimation of remaining battery capacity is crucial for optimizing system performance and reliability.Unlike traditional methods that rely on static alternating internal resistance(SAIR)measurements in an open-circuit state,this study presents a real-time state of charge(SOC)estimation method combining dynamic alternating internal resistance(DAIR)with artificial neural networks(ANN).The system simultaneously measures electrochemical impedance various frequencies,discharge C-rate,and battery surface temperature during the∣Z∣atdischarge process,using these parameters for ANN training.The ANN,leveraging its superior nonlinear system modeling capabilities,effectively captures the complex nonlinear relationships between AC impedance and SOC through iterative training.Compared to other machine learning approaches,the proposed ANN features a simpler architecture and lower computational overhead,making it more suitable for integration into battery management system(BMS)microcontrollers.In tests conducted with Samsung batteries using lithium cobalt oxide cathode material,the method achieved an overall average error of merely 0.42%in self-validation,with mean absolute errors(MAE)for individual SOCs not exceeding 1%.Secondary validation demonstrated an overall average error of 1.24%,with MAE for individual SOCs below 2.5%.This integrated DAIR-ANN approach not only provides enhanced estimation accuracy but also simplifies computational requirements,offering a more effective solution for battery management in practical applications.
基金supported by the National Natural Science Foundation of China(No.U20A20310 and No.52176199)sponsored by the Program of Shanghai Academic/Technology Research Leader(No.22XD1423800)。
文摘Thermal runaway(TR)is a critical issue hindering the large-scale application of lithium-ion batteries(LIBs).Understanding the thermal safety behavior of LIBs at the cell and module level under different state of charges(SOCs)has significant implications for reinforcing the thermal safety design of the lithium-ion battery module.This study first investigates the thermal safety boundary(TSB)correspondence at the cells and modules level under the guidance of a newly proposed concept,safe electric quantity boundary(SEQB).A reasonable thermal runaway propagation(TRP)judgment indicator,peak heat transfer power(PHTP),is proposed to predict whether TRP occurs.Moreover,a validated 3D model is used to quantitatively clarify the TSB at different SOCs from the perspective of PHTP,TR trigger temperature,SOC,and the full cycle life.Besides,three different TRP transfer modes are discovered.The interconversion relationship of three different TRP modes is investigated from the perspective of PHTP.This paper explores the TSB of LIBs under different SOCs at both cell and module levels for the first time,which has great significance in guiding the thermal safety design of battery systems.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFA1406400)。
文摘Recent experimental evidence of the charge-6e condensed phase in Kagome superconductors has generated significant interest.This study investigates the unconventional superconductivity in the Kagome superconductor CsV_(3)Sb_(5),focusing on the emergence of charge-6e superconductivity(SC)at temperatures higher than the conventional charge-2e SC state.By modeling the phase coherence of the SC order parameter using a frustrated antiferromagnetic XY model on an emergent Kagome lattice,the condensation of fractional vortices with 1/3 vorticity stabilizes the phase coherence in exp(i3θ),resulting in a charge-6e SC state.Using a tensor network approach tailored for frustrated spin systems,a Berezinskii-Kosterlitz-Thouless transition is identified at T_(c)/J≃0.075,where the unbinding of 1/3 fractional vortex-antivortex pairs transforms the system from the charge-6e SC phase to the normal phase.Below T_(c),the 1/3 fractional vortex correlations exhibit a power-law decay,whereas the integer vortex correlations decay exponentially,reflecting the dominance of charge-6e SC in the absence of charge-2e SC.The results provide a theoretical understanding of charge-6e SC in two-dimensional Kagome superconductors,emphasizing the interaction between fractional vortices,frustration,and topology in stabilizing the exotic SC phase.
基金supported by the Key Science and Technology Project of China Southern Power Grid Corporation:Sodium-ion Battery Energy Storage System Multi-Scenario Demonstration Application Project-Topic 2 Research on Safety Application Technology of Sodium-ion Battery Energy Storage(STKJXM 20210104)the National Natural Science Foundation of China under Grant 52307233.
文摘ABSTRACT The accurate state-of-charge(SOC)estimation of sodium-ion batteries is the basis for their efficient application.In this paper,a new SOC estimation method suitable for sodium-ion batteries and their application conditions is proposed,which considers the combination of open circuit voltage(OCV)and internal resistance correction.First,the optimal order of equivalent circuit model is analyzed and selected,and the monotonic and stable mapping relationships between OCV and SOC,as well as between ohmic internal resistance and SOC are determined.Then,a joint estimation algorithm for battery model parameters and SOC is estab-lished,and a joint SOC correction strategy based on OCV and ohmic internal resistance is established.The test results show that OCV correction is reliable when polarization is small,that the ohmic internal resistance correction is reliable when the current fluctuation is large,and that the maximum absolute error of SOC estimation of the proposed method is not more than 2.6%.
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.
文摘在孤岛直流微电网系统中,线路阻抗不匹配会影响各线路的电流分配精度和荷电状态(state of charge,SOC)均衡效果。同时,由于采用下垂控制,虚拟阻抗的存在也会导致直流母线电压下降。针对以上问题,提出了一种基于自适应虚拟阻抗的SOC均衡控制策略。该策略考虑了不同容量的分布式储能单元(distributed energy storage units,DESUs),并设计了交互DESUs邻居单元SOC均衡差异信息的收敛因子,以加快SOC均衡速度。利用结合多种系统状态信息的状态因子,通过单补偿环节即可实现输出电流的精准分配以及母线电压的恢复。使用改进后的动态平均一致性算法获取系统全局平均状态信息估计值。最后,在Matlab/Simulink仿真软件中搭建了4种工况模型,验证了所提控制策略的有效性和可靠性。
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.
